Sample records for vibration fault diagnosis
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Distributed bearing fault diagnosis based on vibration analysis
NASA Astrophysics Data System (ADS)
Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani
2016-01-01
Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.
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Vibration signal models for fault diagnosis of planet bearings
NASA Astrophysics Data System (ADS)
Feng, Zhipeng; Ma, Haoqun; Zuo, Ming J.
2016-05-01
Rolling element bearings are key components of planetary gearboxes. Among them, the motion of planet bearings is very complex, encompassing spinning and revolution. Therefore, planet bearing vibrations are highly intricate and their fault characteristics are completely different from those of fixed-axis case, making planet bearing fault diagnosis a difficult topic. In order to address this issue, we derive the explicit equations for calculating the characteristic frequency of outer race, rolling element and inner race fault, considering the complex motion of planet bearings. We also develop the planet bearing vibration signal model for each fault case, considering the modulation effects of load zone passing, time-varying angle between the gear pair mesh and fault induced impact force, as well as the time-varying vibration transfer path. Based on the developed signal models, we derive the explicit equations of Fourier spectrum in each fault case, and summarize the vibration spectral characteristics respectively. The theoretical derivations are illustrated by numerical simulation, and further validated experimentally and all the three fault cases (i.e. outer race, rolling element and inner race localized fault) are diagnosed.
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Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego
2016-06-17
Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.
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Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning
Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego
2016-01-01
Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273
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NASA Technical Reports Server (NTRS)
Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.
1996-01-01
A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.
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Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis
He, Qingbo; Wang, Xiangxiang; Zhou, Qiang
2014-01-01
Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods. PMID:24379045
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Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing
NASA Astrophysics Data System (ADS)
Jiao, Jing; Yue, Jianhai; Pei, Di
2017-10-01
Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.
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NASA Astrophysics Data System (ADS)
Lai, Wenqing; Wang, Yuandong; Li, Wenpeng; Sun, Guang; Qu, Guomin; Cui, Shigang; Li, Mengke; Wang, Yongqiang
2017-10-01
Based on long term vibration monitoring of the No.2 oil-immersed fat wave reactor in the ±500kV converter station in East Mongolia, the vibration signals in normal state and in core loose fault state were saved. Through the time-frequency analysis of the signals, the vibration characteristics of the core loose fault were obtained, and a fault diagnosis method based on the dual tree complex wavelet (DT-CWT) and support vector machine (SVM) was proposed. The vibration signals were analyzed by DT-CWT, and the energy entropy of the vibration signals were taken as the feature vector; the support vector machine was used to train and test the feature vector, and the accurate identification of the core loose fault of the flat wave reactor was realized. Through the identification of many groups of normal and core loose fault state vibration signals, the diagnostic accuracy of the result reached 97.36%. The effectiveness and accuracy of the method in the fault diagnosis of the flat wave reactor core is verified.
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Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao
2014-01-01
Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately. PMID:24936949
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Research on vibration signal analysis and extraction method of gear local fault
NASA Astrophysics Data System (ADS)
Yang, X. F.; Wang, D.; Ma, J. F.; Shao, W.
2018-02-01
Gear is the main connection parts and power transmission parts in the mechanical equipment. If the fault occurs, it directly affects the running state of the whole machine and even endangers the personal safety. So it has important theoretical significance and practical value to study on the extraction of the gear fault signal and fault diagnosis of the gear. In this paper, the gear local fault as the research object, set up the vibration model of gear fault vibration mechanism, derive the vibration mechanism of the gear local fault and analyzes the similarities and differences of the vibration signal between the gear non fault and the gears local faults. In the MATLAB environment, the wavelet transform algorithm is used to denoise the fault signal. Hilbert transform is used to demodulate the fault vibration signal. The results show that the method can denoise the strong noise mechanical vibration signal and extract the local fault feature information from the fault vibration signal..
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Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis.
Saidi, Lotfi; Ali, Jaouher Ben; Fnaiech, Farhat
2014-09-01
Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
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Planetary Gearbox Fault Diagnosis Using a Single Piezoelectric Strain Sensor
2014-12-23
However, the fault detection of planetary gearbox is very complicate since the c omplex nature of dynamic rolling structure of p lanetary gearbox...vibration transfer paths due to the unique dynamic structure of rotating planet gears. Therefore, it is difficult to diagnose PGB faults via vibration...al. 2014). To overcome the above mentioned challenges in developing effective PGB fau lt diagnosis capability , a research investigation on
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NASA Astrophysics Data System (ADS)
Jing, Ya-Bing; Liu, Chang-Wen; Bi, Feng-Rong; Bi, Xiao-Yang; Wang, Xia; Shao, Kang
2017-07-01
Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying features. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastICA-SVM achieves higher classification accuracy and makes better generalization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastICA-SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines.
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NASA Astrophysics Data System (ADS)
Lu, Siliang; Zhou, Peng; Wang, Xiaoxian; Liu, Yongbin; Liu, Fang; Zhao, Jiwen
2018-02-01
Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used frequently in monitoring vital equipment. Benefiting from the development of data mining technologies, the massive data generated by sensors facilitate condition monitoring and fault diagnosis. However, too much data increase storage space, energy consumption, and computing resource, which can be considered fatal weaknesses for a WSN with limited resources. This study investigates a new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN. The proposed method, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique, can reduce the sampled data length while retaining the monitoring and diagnosis performance. A WSN prototype was designed, and simulations and experiments were conducted to evaluate the effectiveness and efficiency of the proposed method. Experimental results indicated that the sampled data length and transmission time of the proposed method result in a decrease of over 80% in comparison with that of the traditional method. Therefore, the proposed method indicates potential applications on condition monitoring and fault diagnosis of motor bearings installed in remote areas, such as wind farms and offshore platforms.
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A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network
Yang, Tao; Gao, Wei
2018-01-01
Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved. PMID:29734704
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A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network.
Guo, Sheng; Yang, Tao; Gao, Wei; Zhang, Chen
2018-05-04
Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved.
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Zhang, Wei; Peng, Gaoliang; Li, Chuanhao; Chen, Yuanhang; Zhang, Zhujun
2017-01-01
Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions. PMID:28241451
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Qu, Yongzhi; He, David; Yoon, Jae; Van Hecke, Brandon; Bechhoefer, Eric; Zhu, Junda
2014-01-01
In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance. PMID:24424467
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A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis
Sohaib, Muhammad; Kim, Cheol-Hong; Kim, Jong-Myon
2017-01-01
Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs). PMID:29232908
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NASA Astrophysics Data System (ADS)
Jiang, Guo-Qian; Xie, Ping; Wang, Xiao; Chen, Meng; He, Qun
2017-11-01
The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.
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Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform
Tang, Guiji; Tian, Tian; Zhou, Chong
2018-01-01
When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time–time (IHTT) transform, by combining a Hilbert time–time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures. PMID:29662013
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Huang, Wentao; Sun, Hongjian; Wang, Weijie
2017-06-03
Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.
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Huang, Wentao; Sun, Hongjian; Wang, Weijie
2017-01-01
Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD’s theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis. PMID:28587198
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Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding
Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping
2015-01-01
Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771
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NASA Technical Reports Server (NTRS)
Abbott, Kathy
1990-01-01
The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to
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A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders
NASA Astrophysics Data System (ADS)
Shao, Haidong; Jiang, Hongkai; Lin, Ying; Li, Xingqiu
2018-03-01
Automatic and accurate identification of rolling bearings fault categories, especially for the fault severities and fault orientations, is still a major challenge in rotating machinery fault diagnosis. In this paper, a novel method called ensemble deep auto-encoders (EDAEs) is proposed for intelligent fault diagnosis of rolling bearings. Firstly, different activation functions are employed as the hidden functions to design a series of auto-encoders (AEs) with different characteristics. Secondly, EDAEs are constructed with various auto-encoders for unsupervised feature learning from the measured vibration signals. Finally, a combination strategy is designed to ensure accurate and stable diagnosis results. The proposed method is applied to analyze the experimental bearing vibration signals. The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.
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Methodology for fault detection in induction motors via sound and vibration signals
NASA Astrophysics Data System (ADS)
Delgado-Arredondo, Paulo Antonio; Morinigo-Sotelo, Daniel; Osornio-Rios, Roque Alfredo; Avina-Cervantes, Juan Gabriel; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene de Jesus
2017-01-01
Nowadays, timely maintenance of electric motors is vital to keep up the complex processes of industrial production. There are currently a variety of methodologies for fault diagnosis. Usually, the diagnosis is performed by analyzing current signals at a steady-state motor operation or during a start-up transient. This method is known as motor current signature analysis, which identifies frequencies associated with faults in the frequency domain or by the time-frequency decomposition of the current signals. Fault identification may also be possible by analyzing acoustic sound and vibration signals, which is useful because sometimes this information is the only available. The contribution of this work is a methodology for detecting faults in induction motors in steady-state operation based on the analysis of acoustic sound and vibration signals. This proposed approach uses the Complete Ensemble Empirical Mode Decomposition for decomposing the signal into several intrinsic mode functions. Subsequently, the frequency marginal of the Gabor representation is calculated to obtain the spectral content of the IMF in the frequency domain. This proposal provides good fault detectability results compared to other published works in addition to the identification of more frequencies associated with the faults. The faults diagnosed in this work are two broken rotor bars, mechanical unbalance and bearing defects.
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NASA Astrophysics Data System (ADS)
Ai, Yan-Ting; Guan, Jiao-Yue; Fei, Cheng-Wei; Tian, Jing; Zhang, Feng-Ling
2017-05-01
To monitor rolling bearing operating status with casings in real time efficiently and accurately, a fusion method based on n-dimensional characteristic parameters distance (n-DCPD) was proposed for rolling bearing fault diagnosis with two types of signals including vibration signal and acoustic emission signals. The n-DCPD was investigated based on four information entropies (singular spectrum entropy in time domain, power spectrum entropy in frequency domain, wavelet space characteristic spectrum entropy and wavelet energy spectrum entropy in time-frequency domain) and the basic thought of fusion information entropy fault diagnosis method with n-DCPD was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner-ball faults, inner-outer faults and normal) are collected under different operation conditions with the emphasis on the rotation speed from 800 rpm to 2000 rpm. In the light of the proposed fusion information entropy method with n-DCPD, the diagnosis of rolling bearing faults was completed. The fault diagnosis results show that the fusion entropy method holds high precision in the recognition of rolling bearing faults. The efforts of this study provide a novel and useful methodology for the fault diagnosis of an aeroengine rolling bearing.
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Applications of Fault Detection in Vibrating Structures
NASA Technical Reports Server (NTRS)
Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.
2012-01-01
Structural fault detection and identification remains an area of active research. Solutions to fault detection and identification may be based on subtle changes in the time series history of vibration signals originating from various sensor locations throughout the structure. The purpose of this paper is to document the application of vibration based fault detection methods applied to several structures. Overall, this paper demonstrates the utility of vibration based methods for fault detection in a controlled laboratory setting and limitations of applying the same methods to a similar structure during flight on an experimental subscale aircraft.
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Layered clustering multi-fault diagnosis for hydraulic piston pump
NASA Astrophysics Data System (ADS)
Du, Jun; Wang, Shaoping; Zhang, Haiyan
2013-04-01
Efficient diagnosis is very important for improving reliability and performance of aircraft hydraulic piston pump, and it is one of the key technologies in prognostic and health management system. In practice, due to harsh working environment and heavy working loads, multiple faults of an aircraft hydraulic pump may occur simultaneously after long time operations. However, most existing diagnosis methods can only distinguish pump faults that occur individually. Therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic pump. In this paper, a new method based on the layered clustering algorithm is proposed to diagnose multiple faults of an aircraft hydraulic pump that occur simultaneously. The intensive failure mechanism analyses of the five main types of faults are carried out, and based on these analyses the optimal combination and layout of diagnostic sensors is attained. The three layered diagnosis reasoning engine is designed according to the faults' risk priority number and the characteristics of different fault feature extraction methods. The most serious failures are first distinguished with the individual signal processing. To the desultory faults, i.e., swash plate eccentricity and incremental clearance increases between piston and slipper, the clustering diagnosis algorithm based on the statistical average relative power difference (ARPD) is proposed. By effectively enhancing the fault features of these two faults, the ARPDs calculated from vibration signals are employed to complete the hypothesis testing. The ARPDs of the different faults follow different probability distributions. Compared with the classical fast Fourier transform-based spectrum diagnosis method, the experimental results demonstrate that the proposed algorithm can diagnose the multiple faults, which occur synchronously, with higher precision and reliability.
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An improved wrapper-based feature selection method for machinery fault diagnosis
2017-01-01
A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks. PMID:29261689
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Gear fault diagnosis based on the structured sparsity time-frequency analysis
NASA Astrophysics Data System (ADS)
Sun, Ruobin; Yang, Zhibo; Chen, Xuefeng; Tian, Shaohua; Xie, Yong
2018-03-01
Over the last decade, sparse representation has become a powerful paradigm in mechanical fault diagnosis due to its excellent capability and the high flexibility for complex signal description. The structured sparsity time-frequency analysis (SSTFA) is a novel signal processing method, which utilizes mixed-norm priors on time-frequency coefficients to obtain a fine match for the structure of signals. In order to extract the transient feature from gear vibration signals, a gear fault diagnosis method based on SSTFA is proposed in this work. The steady modulation components and impulsive components of the defective gear vibration signals can be extracted simultaneously by choosing different time-frequency neighborhood and generalized thresholding operators. Besides, the time-frequency distribution with high resolution is obtained by piling different components in the same diagram. The diagnostic conclusion can be made according to the envelope spectrum of the impulsive components or by the periodicity of impulses. The effectiveness of the method is verified by numerical simulations, and the vibration signals registered from a gearbox fault simulator and a wind turbine. To validate the efficiency of the presented methodology, comparisons are made among some state-of-the-art vibration separation methods and the traditional time-frequency analysis methods. The comparisons show that the proposed method possesses advantages in separating feature signals under strong noise and accounting for the inner time-frequency structure of the gear vibration signals.
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New procedure for gear fault detection and diagnosis using instantaneous angular speed
NASA Astrophysics Data System (ADS)
Li, Bing; Zhang, Xining; Wu, Jili
2017-02-01
Besides the extreme complexity of gear dynamics, the fault diagnosis results in terms of vibration signal are sometimes easily misled and even distorted by the interference of transmission channel or other components like bearings, bars. Recently, the research field of Instantaneous Angular Speed (IAS) has attracted significant attentions due to its own advantages over conventional vibration analysis. On the basis of IAS signal's advantages, this paper presents a new feature extraction method by combining the Empirical Mode Decomposition (EMD) and Autocorrelation Local Cepstrum (ALC) for fault diagnosis of sophisticated multistage gearbox. Firstly, as a pre-processing step, signal reconstruction is employed to address the oversampled issue caused by the high resolution of the angular sensor and the test speed. Then the adaptive EMD is used to acquire a number of Intrinsic Mode Functions (IMFs). Nevertheless, not all the IMFs are needed for the further analysis since different IMFs have different sensitivities to fault. Hence, the cosine similarity metric is introduced to select the most sensitive IMF. Even though, the sensitive IMF is still insufficient for the gear fault diagnosis due to the weakness of the fault component related to the gear fault. Therefore, as the final step, ALC is used for the purpose of signal de-noising and feature extraction. The effectiveness and robustness of the new approach has been validated experimentally on the basis of two gear test rigs with gears under different working conditions. Diagnosis results show that the new approach is capable of effectively handling the gear fault diagnosis i.e., the highlighted quefrency and its rahmonics corresponding to the rotary period and its multiple are displayed clearly in the cepstrum record of the proposed method.
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Scattering transform and LSPTSVM based fault diagnosis of rotating machinery
NASA Astrophysics Data System (ADS)
Ma, Shangjun; Cheng, Bo; Shang, Zhaowei; Liu, Geng
2018-05-01
This paper proposes an algorithm for fault diagnosis of rotating machinery to overcome the shortcomings of classical techniques which are noise sensitive in feature extraction and time consuming for training. Based on the scattering transform and the least squares recursive projection twin support vector machine (LSPTSVM), the method has the advantages of high efficiency and insensitivity for noise signal. Using the energy of the scattering coefficients in each sub-band, the features of the vibration signals are obtained. Then, an LSPTSVM classifier is used for fault diagnosis. The new method is compared with other common methods including the proximal support vector machine, the standard support vector machine and multi-scale theory by using fault data for two systems, a motor bearing and a gear box. The results show that the new method proposed in this study is more effective for fault diagnosis of rotating machinery.
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Fault diagnosis for diesel valve trains based on time frequency images
NASA Astrophysics Data System (ADS)
Wang, Chengdong; Zhang, Youyun; Zhong, Zhenyuan
2008-11-01
In this paper, the Wigner-Ville distributions (WVD) of vibration acceleration signals which were acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images; and the probabilistic neural networks (PNN) were directly used to classify the time-frequency images after the images were normalized. By this way, the fault diagnosis of valve train was transferred to the classification of time-frequency images. As there is no need to extract further fault features (such as eigenvalues or symptom parameters) from time-frequency distributions before classification, the fault diagnosis process is highly simplified. The experimental results show that the faults of diesel valve trains can be classified accurately by the proposed methods.
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Huang, Nantian; Chen, Huaijin; Cai, Guowei; Fang, Lihua; Wang, Yuqiang
2016-11-10
Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD) and multi-layer classifier (MLC) is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs). The IMF matrix is divided into submatrices to compute the local singular values (LSV). The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs) and a support vector machine (SVM) is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF₆ HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever) are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods.
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Huang, Nantian; Chen, Huaijin; Cai, Guowei; Fang, Lihua; Wang, Yuqiang
2016-01-01
Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD) and multi-layer classifier (MLC) is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs). The IMF matrix is divided into submatrices to compute the local singular values (LSV). The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs) and a support vector machine (SVM) is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF6 HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever) are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods. PMID:27834902
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A method of real-time fault diagnosis for power transformers based on vibration analysis
NASA Astrophysics Data System (ADS)
Hong, Kaixing; Huang, Hai; Zhou, Jianping; Shen, Yimin; Li, Yujie
2015-11-01
In this paper, a novel probability-based classification model is proposed for real-time fault detection of power transformers. First, the transformer vibration principle is introduced, and two effective feature extraction techniques are presented. Next, the details of the classification model based on support vector machine (SVM) are shown. The model also includes a binary decision tree (BDT) which divides transformers into different classes according to health state. The trained model produces posterior probabilities of membership to each predefined class for a tested vibration sample. During the experiments, the vibrations of transformers under different conditions are acquired, and the corresponding feature vectors are used to train the SVM classifiers. The effectiveness of this model is illustrated experimentally on typical in-service transformers. The consistency between the results of the proposed model and the actual condition of the test transformers indicates that the model can be used as a reliable method for transformer fault detection.
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NASA Astrophysics Data System (ADS)
Avendaño-Valencia, Luis David; Fassois, Spilios D.
2017-07-01
The study focuses on vibration response based health monitoring for an operating wind turbine, which features time-dependent dynamics under environmental and operational uncertainty. A Gaussian Mixture Model Random Coefficient (GMM-RC) model based Structural Health Monitoring framework postulated in a companion paper is adopted and assessed. The assessment is based on vibration response signals obtained from a simulated offshore 5 MW wind turbine. The non-stationarity in the vibration signals originates from the continually evolving, due to blade rotation, inertial properties, as well as the wind characteristics, while uncertainty is introduced by random variations of the wind speed within the range of 10-20 m/s. Monte Carlo simulations are performed using six distinct structural states, including the healthy state and five types of damage/fault in the tower, the blades, and the transmission, with each one of them characterized by four distinct levels. Random vibration response modeling and damage diagnosis are illustrated, along with pertinent comparisons with state-of-the-art diagnosis methods. The results demonstrate consistently good performance of the GMM-RC model based framework, offering significant performance improvements over state-of-the-art methods. Most damage types and levels are shown to be properly diagnosed using a single vibration sensor.
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NASA Astrophysics Data System (ADS)
Zheng, Jinde; Pan, Haiyang; Yang, Shubao; Cheng, Junsheng
2018-01-01
Multiscale permutation entropy (MPE) is a recently proposed nonlinear dynamic method for measuring the randomness and detecting the nonlinear dynamic change of time series and can be used effectively to extract the nonlinear dynamic fault feature from vibration signals of rolling bearing. To solve the drawback of coarse graining process in MPE, an improved MPE method called generalized composite multiscale permutation entropy (GCMPE) was proposed in this paper. Also the influence of parameters on GCMPE and its comparison with the MPE are studied by analyzing simulation data. GCMPE was applied to the fault feature extraction from vibration signal of rolling bearing and then based on the GCMPE, Laplacian score for feature selection and the Particle swarm optimization based support vector machine, a new fault diagnosis method for rolling bearing was put forward in this paper. Finally, the proposed method was applied to analyze the experimental data of rolling bearing. The analysis results show that the proposed method can effectively realize the fault diagnosis of rolling bearing and has a higher fault recognition rate than the existing methods.
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The application of S-transformation and M-2DPCA in I.C. Engine fault diagnosis
NASA Astrophysics Data System (ADS)
Zhang, Shixiong; Cai, Yanping; Mu, Weijie
2017-04-01
According to the problem of parameter selection and feature extraction for vibration diagnosis of traditional internal combustion engine is discussed. The method based on S-transformation and Module Two Dimensional Principal Components Analysis (M-2DPCA) is proposed to carry out fault diagnosis of I.C. Engine valve mechanism. First of all, the method transfers cylinder surface vibration signals of I.C. into images through S-transform. The second, extracting the optimized projection vectors from the general distribution matrix G which is obtained by all sample sub-images, so that vibration spectrum images can be modularized using M-2DPCA. The last, these features matrix obtained from images project will served as the enters of nearest neighbor classifier, it is used to achieve fault types' division. The method is applied to the diagnosis example of the vibration signal of the valve mechanism eight operating modes, recognition rate up to 94.17 percent; the effectiveness of the proposed method is proved.
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NASA Astrophysics Data System (ADS)
Mishra, C.; Samantaray, A. K.; Chakraborty, G.
2016-09-01
Vibration analysis for diagnosis of faults in rolling element bearings is complicated when the rotor speed is variable or slow. In the former case, the time interval between the fault-induced impact responses in the vibration signal are non-uniform and the signal strength is variable. In the latter case, the fault-induced impact response strength is weak and generally gets buried in the noise, i.e. noise dominates the signal. This article proposes a diagnosis scheme based on a combination of a few signal processing techniques. The proposed scheme initially represents the vibration signal in terms of uniformly resampled angular position of the rotor shaft by using the interpolated instantaneous angular position measurements. Thereafter, intrinsic mode functions (IMFs) are generated through empirical mode decomposition (EMD) of resampled vibration signal which is followed by thresholding of IMFs and signal reconstruction to de-noise the signal and envelope order tracking to diagnose the faults. Data for validating the proposed diagnosis scheme are initially generated from a multi-body simulation model of rolling element bearing which is developed using bond graph approach. This bond graph model includes the ball and cage dynamics, localized fault geometry, contact mechanics, rotor unbalance, and friction and slip effects. The diagnosis scheme is finally validated with experiments performed with the help of a machine fault simulator (MFS) system. Some fault scenarios which could not be experimentally recreated are then generated through simulations and analyzed through the developed diagnosis scheme.
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NASA Astrophysics Data System (ADS)
Guo, Jun; Lu, Siliang; Zhai, Chao; He, Qingbo
2018-02-01
An automatic bearing fault diagnosis method is proposed for permanent magnet synchronous generators (PMSGs), which are widely installed in wind turbines subjected to low rotating speeds, speed fluctuations, and electrical device noise interferences. The mechanical rotating angle curve is first extracted from the phase current of a PMSG by sequentially applying a series of algorithms. The synchronous sampled vibration signal of the fault bearing is then resampled in the angular domain according to the obtained rotating phase information. Considering that the resampled vibration signal is still overwhelmed by heavy background noise, an adaptive stochastic resonance filter is applied to the resampled signal to enhance the fault indicator and facilitate bearing fault identification. Two types of fault bearings with different fault sizes in a PMSG test rig are subjected to experiments to test the effectiveness of the proposed method. The proposed method is fully automated and thus shows potential for convenient, highly efficient and in situ bearing fault diagnosis for wind turbines subjected to harsh environments.
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Multi-Scale Stochastic Resonance Spectrogram for fault diagnosis of rolling element bearings
NASA Astrophysics Data System (ADS)
He, Qingbo; Wu, Enhao; Pan, Yuanyuan
2018-04-01
It is not easy to identify incipient defect of a rolling element bearing by analyzing the vibration data because of the disturbance of background noise. The weak and unrecognizable transient fault signal of a mechanical system can be enhanced by the stochastic resonance (SR) technique that utilizes the noise in the system. However, it is challenging for the SR technique to identify sensitive fault information in non-stationary signals. This paper proposes a new method called multi-scale SR spectrogram (MSSRS) for bearing defect diagnosis. The new method considers the non-stationary property of the defective bearing vibration signals, and treats every scale of the time-frequency distribution (TFD) as a modulation system. Then the SR technique is utilized on each modulation system according to each frequencies in the TFD. The SR results are sensitive to the defect information because the energy of transient vibration is distributed in a limited frequency band in the TFD. Collecting the spectra of the SR outputs at all frequency scales then generates the MSSRS. The proposed MSSRS is able to well deal with the non-stationary transient signal, and can highlight the defect-induced frequency component corresponding to the impulse information. Experimental results with practical defective bearing vibration data have shown that the proposed method outperforms the former SR methods and exhibits a good application prospect in rolling element bearing fault diagnosis.
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NASA Astrophysics Data System (ADS)
Chen, Junxun; Cheng, Longsheng; Yu, Hui; Hu, Shaolin
2018-01-01
ABSTRACTSFor the timely identification of the potential <span class="hlt">faults</span> of a rolling bearing and to observe its health condition intuitively and accurately, a novel <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and health assessment model for a rolling bearing based on the ensemble empirical mode decomposition (EEMD) method and the adjustment Mahalanobis-Taguchi system (AMTS) method is proposed. The specific steps are as follows: First, the <span class="hlt">vibration</span> signal of a rolling bearing is decomposed by EEMD, and the extracted features are used as the input vectors of AMTS. Then, the AMTS method, which is designed to overcome the shortcomings of the traditional Mahalanobis-Taguchi system and to extract the key features, is proposed for <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Finally, a type of HI concept is proposed according to the results of the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> to accomplish the health assessment of a bearing in its life cycle. To validate the superiority of the developed method proposed approach, it is compared with other recent method and proposed methodology is successfully validated on a <span class="hlt">vibration</span> data-set acquired from seeded defects and from an accelerated life test. The results show that this method represents the actual situation well and is able to accurately and effectively identify the <span class="hlt">fault</span> type.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AIPC.1839b0113L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AIPC.1839b0113L"><span>Research on the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of bearing based on wavelet and demodulation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Jiapeng; Yuan, Yu</p> <p>2017-05-01</p> <p>As a most commonly-used machine part, antifriction bearing is extensively used in mechanical equipment. <span class="hlt">Vibration</span> signal analysis is one of the methods to monitor and diagnose the running status of antifriction bearings. Therefore, using wavelet analysis for demising is of great importance in the engineering practice. This paper firstly presented the basic theory of wavelet analysis to study the transformation, decomposition and reconstruction of wavelet. In addition, edition software LabVIEW was adopted to conduct wavelet and demodulation upon the <span class="hlt">vibration</span> signal of antifriction bearing collected. With the combination of Hilbert envelop demodulation analysis, the <span class="hlt">fault</span> character frequencies of the demised signal were extracted to conduct <span class="hlt">fault</span> <span class="hlt">diagnosis</span> analysis, which serves as a reference for the wavelet and demodulation of the <span class="hlt">vibration</span> signal in engineering practice.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110023759','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110023759"><span>Planetary Gearbox <span class="hlt">Fault</span> Detection Using <span class="hlt">Vibration</span> Separation Techniques</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lewicki, David G.; LaBerge, Kelsen E.; Ehinger, Ryan T.; Fetty, Jason</p> <p>2011-01-01</p> <p>Studies were performed to demonstrate the capability to detect planetary gear and bearing <span class="hlt">faults</span> in helicopter main-rotor transmissions. The work supported the Operations Support and Sustainment (OSST) program with the U.S. Army Aviation Applied Technology Directorate (AATD) and Bell Helicopter Textron. <span class="hlt">Vibration</span> data from the OH-58C planetary system were collected on a healthy transmission as well as with various seeded-<span class="hlt">fault</span> components. Planetary <span class="hlt">fault</span> detection algorithms were used with the collected data to evaluate <span class="hlt">fault</span> detection effectiveness. Planet gear tooth cracks and spalls were detectable using the <span class="hlt">vibration</span> separation techniques. Sun gear tooth cracks were not discernibly detectable from the <span class="hlt">vibration</span> separation process. Sun gear tooth spall defects were detectable. Ring gear tooth cracks were only clearly detectable by accelerometers located near the crack location or directly across from the crack. Enveloping provided an effective method for planet bearing inner- and outer-race spalling <span class="hlt">fault</span> detection.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5552175','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5552175"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> for Rolling Bearings under Variable Conditions Based on Visual Cognition</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Cheng, Yujie; Zhou, Bo; Lu, Chen; Yang, Chao</p> <p>2017-01-01</p> <p><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for rolling bearings under variable conditions. This paper introduces a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the <span class="hlt">vibration</span> signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract <span class="hlt">fault</span> features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field. PMID:28772943</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JSV...399..308W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JSV...399..308W"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of sensor networked structures with multiple <span class="hlt">faults</span> using a virtual beam based approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, H.; Jing, X. J.</p> <p>2017-07-01</p> <p>This paper presents a virtual beam based approach suitable for conducting <span class="hlt">diagnosis</span> of multiple <span class="hlt">faults</span> in complex structures with limited prior knowledge of the <span class="hlt">faults</span> involved. The "virtual beam", a recently-proposed concept for <span class="hlt">fault</span> detection in complex structures, is applied, which consists of a chain of sensors representing a <span class="hlt">vibration</span> energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for <span class="hlt">fault</span> detection due to limited prior knowledge of normal operational conditions and <span class="hlt">fault</span> conditions. To isolate the multiple <span class="hlt">faults</span> within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple <span class="hlt">fault</span> localization with limited prior knowledge of normal conditions and <span class="hlt">faults</span>. With extensive experimental results, it is validated that the proposed method can localize both single <span class="hlt">fault</span> and multiple <span class="hlt">faults</span> more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990064445&hterms=articles+learning&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Darticles%2Blearning','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990064445&hterms=articles+learning&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Darticles%2Blearning"><span>Experimental Evaluation of a Structure-Based Connectionist Network for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Helicopter Gearboxes</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jammu, V. B.; Danai, K.; Lewicki, D. G.</p> <p>1998-01-01</p> <p>This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) <span class="hlt">fault</span> diagnostic system introduced in the preceding article. For this <span class="hlt">vibration</span> data from two different helicopter gearboxes: OH-58A and S-61, are used. A salient feature of SBCN is its reliance on the knowledge of the gearbox structure and the type of features obtained from processed <span class="hlt">vibration</span> signals as a substitute to training. To formulate this knowledge, approximate <span class="hlt">vibration</span> transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component <span class="hlt">faults</span> on <span class="hlt">vibration</span> features. The validity of the structural influences is evaluated by comparing them with those obtained from experimental RMS values. These influences are also evaluated ba comparing them with the weights of a connectionist network trained though supervised learning. The results indicate general agreement between the modeled and experimentally obtained influences. The <span class="hlt">vibration</span> data from the two gearboxes are also used to evaluate the performance of SBCN in <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The diagnostic results indicate that the SBCN is effective in directing the presence of <span class="hlt">faults</span> and isolating them within gearbox subsystems based on structural influences, but its performance is not as good in isolating faulty components, mainly due to lack of appropriate <span class="hlt">vibration</span> features.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...80..429C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...80..429C"><span>Iterative generalized time-frequency reassignment for planetary gearbox <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under nonstationary conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Xiaowang; Feng, Zhipeng</p> <p>2016-12-01</p> <p>Planetary gearboxes are widely used in many sorts of machinery, for its large transmission ratio and high load bearing capacity in a compact structure. Their <span class="hlt">fault</span> <span class="hlt">diagnosis</span> relies on effective identification of <span class="hlt">fault</span> characteristic frequencies. However, in addition to the <span class="hlt">vibration</span> complexity caused by intricate mechanical kinematics, volatile external conditions result in time-varying running speed and/or load, and therefore nonstationary <span class="hlt">vibration</span> signals. This usually leads to time-varying complex <span class="hlt">fault</span> characteristics, and adds difficulty to planetary gearbox <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Time-frequency analysis is an effective approach to extracting the frequency components and their time variation of nonstationary signals. Nevertheless, the commonly used time-frequency analysis methods suffer from poor time-frequency resolution as well as outer and inner interferences, which hinder accurate identification of time-varying <span class="hlt">fault</span> characteristic frequencies. Although time-frequency reassignment improves the time-frequency readability, it is essentially subject to the constraints of mono-component and symmetric time-frequency distribution about true instantaneous frequency. Hence, it is still susceptible to erroneous energy reallocation or even generates pseudo interferences, particularly for multi-component signals of highly nonlinear instantaneous frequency. In this paper, to overcome the limitations of time-frequency reassignment, we propose an improvement with fine time-frequency resolution and free from interferences for highly nonstationary multi-component signals, by exploiting the merits of iterative generalized demodulation. The signal is firstly decomposed into mono-components of constant frequency by iterative generalized demodulation. Time-frequency reassignment is then applied to each generalized demodulated mono-component, obtaining a fine time-frequency distribution. Finally, the time-frequency distribution of each signal component is restored and superposed to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018SPIE10710E..2AC','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018SPIE10710E..2AC"><span>Application of lifting wavelet and random forest in compound <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of gearbox</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Tang; Cui, Yulian; Feng, Fuzhou; Wu, Chunzhi</p> <p>2018-03-01</p> <p>Aiming at the weakness of compound <span class="hlt">fault</span> characteristic signals of a gearbox of an armored vehicle and difficult to identify <span class="hlt">fault</span> types, a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on lifting wavelet and random forest is proposed. First of all, this method uses the lifting wavelet transform to decompose the original <span class="hlt">vibration</span> signal in multi-layers, reconstructs the multi-layer low-frequency and high-frequency components obtained by the decomposition to get multiple component signals. Then the time-domain feature parameters are obtained for each component signal to form multiple feature vectors, which is input into the random forest pattern recognition classifier to determine the compound <span class="hlt">fault</span> type. Finally, a variety of compound <span class="hlt">fault</span> data of the gearbox <span class="hlt">fault</span> analog test platform are verified, the results show that the recognition accuracy of the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method combined with the lifting wavelet and the random forest is up to 99.99%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5053415','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5053415"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> for Rotating Machinery: A Method based on Image Processing</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie</p> <p>2016-01-01</p> <p>Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods based on multi-disciplines are becoming the focus in the field of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery. This paper presents a multi-discipline method based on image-processing for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in a two-dimensional space. The proposed method mainly includes the following steps. First, the <span class="hlt">vibration</span> signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact <span class="hlt">fault</span> features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main <span class="hlt">fault</span> features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for <span class="hlt">fault</span> identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for rotating machinery. PMID</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27711246','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27711246"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> for Rotating Machinery: A Method based on Image Processing.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie</p> <p>2016-01-01</p> <p>Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods based on multi-disciplines are becoming the focus in the field of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery. This paper presents a multi-discipline method based on image-processing for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in a two-dimensional space. The proposed method mainly includes the following steps. First, the <span class="hlt">vibration</span> signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact <span class="hlt">fault</span> features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main <span class="hlt">fault</span> features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for <span class="hlt">fault</span> identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for rotating machinery.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ChJME..30.1347S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ChJME..30.1347S"><span>A Deep Learning Approach for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Induction Motors in Manufacturing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shao, Si-Yu; Sun, Wen-Jun; Yan, Ru-Qiang; Wang, Peng; Gao, Robert X.</p> <p>2017-11-01</p> <p>Extracting features from original signals is a key procedure for traditional <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of induction motors, as it directly influences the performance of <span class="hlt">fault</span> recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of <span class="hlt">vibration</span> signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for <span class="hlt">fault</span> classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine <span class="hlt">fault</span> simulator verifies the effectiveness of the deep learning approach for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of induction motors. This research proposes an intelligent <span class="hlt">diagnosis</span> method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013MSSP...41..113J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013MSSP...41..113J"><span>Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiang, Li; Xuan, Jianping; Shi, Tielin</p> <p>2013-12-01</p> <p>Generally, the <span class="hlt">vibration</span> signals of faulty machinery are non-stationary and nonlinear under complicated operating conditions. Therefore, it is a big challenge for machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span> to extract optimal features for improving classification accuracy. This paper proposes semi-supervised kernel Marginal Fisher analysis (SSKMFA) for feature extraction, which can discover the intrinsic manifold structure of dataset, and simultaneously consider the intra-class compactness and the inter-class separability. Based on SSKMFA, a novel approach to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is put forward and applied to <span class="hlt">fault</span> recognition of rolling bearings. SSKMFA directly extracts the low-dimensional characteristics from the raw high-dimensional <span class="hlt">vibration</span> signals, by exploiting the inherent manifold structure of both labeled and unlabeled samples. Subsequently, the optimal low-dimensional features are fed into the simplest K-nearest neighbor (KNN) classifier to recognize different <span class="hlt">fault</span> categories and severities of bearings. The experimental results demonstrate that the proposed approach improves the <span class="hlt">fault</span> recognition performance and outperforms the other four feature extraction methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MS%26E..241a2035P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MS%26E..241a2035P"><span>Rolling bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on information fusion using Dempster-Shafer evidence theory</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pei, Di; Yue, Jianhai; Jiao, Jing</p> <p>2017-10-01</p> <p>This paper presents a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for rolling bearing based on information fusion. Acceleration sensors are arranged at different position to get bearing <span class="hlt">vibration</span> data as diagnostic evidence. The Dempster-Shafer (D-S) evidence theory is used to fuse multi-sensor data to improve diagnostic accuracy. The efficiency of the proposed method is demonstrated by the high speed train transmission test bench. The results of experiment show that the proposed method in this paper improves the rolling bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> accuracy compared with traditional signal analysis methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5038797','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5038797"><span>A Sparsity-Promoted Decomposition for Compressed <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Roller Bearings</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Wang, Huaqing; Ke, Yanliang; Song, Liuyang; Tang, Gang; Chen, Peng</p> <p>2016-01-01</p> <p>The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the <span class="hlt">vibration</span> signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which impedes the application of CS theory. Therefore, it is of great significance to promote the sparsity when applying the CS theory to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of roller bearings. To increase the sparsity of <span class="hlt">vibration</span> signals, a sparsity-promoted method called the tunable Q-factor wavelet transform based on decomposing the analyzed signals into transient impact components and high oscillation components is utilized in this work. The former become sparser than the raw signals with noise eliminated, whereas the latter include noise. Thus, the decomposed transient impact components replace the original signals for analysis. The CS theory is applied to extract the <span class="hlt">fault</span> features without complete reconstruction, which means that the reconstruction can be completed when the components with interested frequencies are detected and the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> can be achieved during the reconstruction procedure. The application cases prove that the CS theory assisted by the tunable Q-factor wavelet transform can successfully extract the <span class="hlt">fault</span> features from the compressed samples. PMID:27657063</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23352553','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23352553"><span>Application of the Teager-Kaiser energy operator in bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Henríquez Rodríguez, Patricia; Alonso, Jesús B; Ferrer, Miguel A; Travieso, Carlos M</p> <p>2013-03-01</p> <p>Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing <span class="hlt">diagnosis</span> techniques have been developed. Some of them feature the bearing <span class="hlt">vibration</span> signal with statistical measures and others extract the bearing <span class="hlt">fault</span> characteristic frequency from the AM component of the <span class="hlt">vibration</span> signal. In this paper, we propose to transform the <span class="hlt">vibration</span> signal to the Teager-Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The <span class="hlt">diagnosis</span> is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JPhCS.842a2057W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPhCS.842a2057W"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> for Centre Wear <span class="hlt">Fault</span> of Roll Grinder Based on a Resonance Demodulation Scheme</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Liming; Shao, Yimin; Yin, Lei; Yuan, Yilin; Liu, Jing</p> <p>2017-05-01</p> <p>Roll grinder is one of the important parts in the rolling machinery, and the grinding precision of roll surface has direct influence on the surface quality of steel strip. However, during the grinding process, the centre bears the gravity of the roll and alternating stress. Therefore, wear or spalling <span class="hlt">faults</span> are easily observed on the centre, which will lead to an anomalous <span class="hlt">vibration</span> of the roll grinder. In this study, a resonance demodulation scheme is proposed to detect the centre wear <span class="hlt">fault</span> of roll grinder. Firstly, fast kurtogram method is employed to help select the sub-band filter parameters for optimal resonance demodulation. Further, the envelope spectrum are derived based on the filtered signal. Finally, two health indicators are designed to conduct the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for centre wear <span class="hlt">fault</span>. The proposed scheme is assessed by analysing experimental data from a roll grinder of twenty-high rolling mill. The results show that the proposed scheme can effectively detect the centre wear <span class="hlt">fault</span> of the roll grinder.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...84..731Q','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...84..731Q"><span>An adaptive unsaturated bistable stochastic resonance method and its application in mechanical <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Qiao, Zijian; Lei, Yaguo; Lin, Jing; Jia, Feng</p> <p>2017-02-01</p> <p>In mechanical <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, most traditional methods for signal processing attempt to suppress or cancel noise imbedded in <span class="hlt">vibration</span> signals for extracting weak <span class="hlt">fault</span> characteristics, whereas stochastic resonance (SR), as a potential tool for signal processing, is able to utilize the noise to enhance <span class="hlt">fault</span> characteristics. The classical bistable SR (CBSR), as one of the most widely used SR methods, however, has the disadvantage of inherent output saturation. The output saturation not only reduces the output signal-to-noise ratio (SNR) but also limits the enhancement capability for <span class="hlt">fault</span> characteristics. To overcome this shortcoming, a novel method is proposed to extract the <span class="hlt">fault</span> characteristics, where a piecewise bistable potential model is established. Simulated signals are used to illustrate the effectiveness of the proposed method, and the results show that the method is able to extract weak <span class="hlt">fault</span> characteristics and has good enhancement performance and anti-noise capability. Finally, the method is applied to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of bearings and planetary gearboxes, respectively. The <span class="hlt">diagnosis</span> results demonstrate that the proposed method can obtain larger output SNR, higher spectrum peaks at <span class="hlt">fault</span> characteristic frequencies and therefore larger recognizable degree than the CBSR method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4418566','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4418566"><span>A <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Methodology for Gear Pump Based on EEMD and Bayesian Network</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing</p> <p>2015-01-01</p> <p>This paper proposes a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional <span class="hlt">fault</span> <span class="hlt">diagnosis</span> with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a <span class="hlt">fault</span> layer, a <span class="hlt">fault</span> feature layer and a multi-source information layer. <span class="hlt">Vibration</span> signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as <span class="hlt">fault</span> features. These features are added into the <span class="hlt">fault</span> feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating <span class="hlt">faults</span> and <span class="hlt">fault</span> symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. It is effective and efficient in diagnosing <span class="hlt">faults</span> based on uncertain, incomplete information. PMID:25938760</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25938760','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25938760"><span>A <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Methodology for Gear Pump Based on EEMD and Bayesian Network.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing</p> <p>2015-01-01</p> <p>This paper proposes a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional <span class="hlt">fault</span> <span class="hlt">diagnosis</span> with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a <span class="hlt">fault</span> layer, a <span class="hlt">fault</span> feature layer and a multi-source information layer. <span class="hlt">Vibration</span> signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as <span class="hlt">fault</span> features. These features are added into the <span class="hlt">fault</span> feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating <span class="hlt">faults</span> and <span class="hlt">fault</span> symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. It is effective and efficient in diagnosing <span class="hlt">faults</span> based on uncertain, incomplete information.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29899216','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29899216"><span>A Novel Bearing Multi-<span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhou, Shenghan; Qian, Silin; Chang, Wenbing; Xiao, Yiyong; Cheng, Yang</p> <p>2018-06-14</p> <p>Timely and accurate state detection and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing <span class="hlt">vibration</span> signal was calculated to detect the <span class="hlt">fault</span>. Secondly, if a bearing <span class="hlt">fault</span> occurred, the <span class="hlt">vibration</span> signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the <span class="hlt">fault</span> feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-<span class="hlt">fault</span> types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing <span class="hlt">faults</span> and maintain a high accuracy rate of <span class="hlt">fault</span> recognition when a small number of training samples are available.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..100..706X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..100..706X"><span>Torsional <span class="hlt">vibration</span> signal analysis as a diagnostic tool for planetary gear <span class="hlt">fault</span> detection</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xue, Song; Howard, Ian</p> <p>2018-02-01</p> <p>This paper aims to investigate the effectiveness of using the torsional <span class="hlt">vibration</span> signal as a diagnostic tool for planetary gearbox <span class="hlt">faults</span> detection. The traditional approach for condition monitoring of the planetary gear uses a stationary transducer mounted on the ring gear casing to measure all the <span class="hlt">vibration</span> data when the planet gears pass by with the rotation of the carrier arm. However, the time variant <span class="hlt">vibration</span> transfer paths between the stationary transducer and the rotating planet gear modulate the resultant <span class="hlt">vibration</span> spectra and make it complex. Torsional <span class="hlt">vibration</span> signals are theoretically free from this modulation effect and therefore, it is expected to be much easier and more effective to diagnose planetary gear <span class="hlt">faults</span> using the <span class="hlt">fault</span> diagnostic information extracted from the torsional <span class="hlt">vibration</span>. In this paper, a 20 degree of freedom planetary gear lumped-parameter model was developed to obtain the gear dynamic response. In the model, the gear mesh stiffness variations are the main internal <span class="hlt">vibration</span> generation mechanism and the finite element models were developed for calculation of the sun-planet and ring-planet gear mesh stiffnesses. Gear <span class="hlt">faults</span> on different components were created in the finite element models to calculate the resultant gear mesh stiffnesses, which were incorporated into the planetary gear model later on to obtain the <span class="hlt">faulted</span> <span class="hlt">vibration</span> signal. Some advanced signal processing techniques were utilized to analyses the <span class="hlt">fault</span> diagnostic results from the torsional <span class="hlt">vibration</span>. It was found that the planetary gear torsional <span class="hlt">vibration</span> not only successfully detected the gear <span class="hlt">fault</span>, but also had the potential to indicate the location of the gear <span class="hlt">fault</span>. As a result, the planetary gear torsional <span class="hlt">vibration</span> can be considered an effective alternative approach for planetary gear condition monitoring.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JSV...321.1171C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JSV...321.1171C"><span>Detecting the crankshaft torsional <span class="hlt">vibration</span> of diesel engines for combustion related <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Charles, P.; Sinha, Jyoti K.; Gu, F.; Lidstone, L.; Ball, A. D.</p> <p>2009-04-01</p> <p>Early <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> for medium-speed diesel engines is important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion related <span class="hlt">fault</span> detection capability of crankshaft torsional <span class="hlt">vibration</span>. The encoder signal, often used for shaft speed measurement, has been used to construct the instantaneous angular speed (IAS) waveform, which actually represents the signature of the torsional <span class="hlt">vibration</span>. Earlier studies have shown that the IAS signal and its fast Fourier transform (FFT) analysis are effective for monitoring engines with less than eight cylinders. The applicability to medium-speed engines, however, is strongly contested due to the high number of cylinders and large moment of inertia. Therefore the effectiveness of the FFT-based approach has further been enhanced by improving the signal processing to determine the IAS signal and subsequently tested on a 16-cylinder engine. In addition, a novel method of presentation, based on the polar coordinate system of the IAS signal, has also been introduced; to improve the discrimination features of the <span class="hlt">faults</span> compared to the FFT-based approach of the IAS signal. The paper discusses two typical experimental studies on 16- and 20-cylinder engines, with and without <span class="hlt">faults</span>, and the <span class="hlt">diagnosis</span> results by the proposed polar presentation method. The results were also compared with the earlier FFT-based method of the IAS signal.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..105..319L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..105..319L"><span>A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Yongbo; Li, Guoyan; Yang, Yuantao; Liang, Xihui; Xu, Minqiang</p> <p>2018-05-01</p> <p>The <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the <span class="hlt">fault</span>-unrelated components and enhance the <span class="hlt">fault</span> characteristics. Second, MHPE is utilized to extract the <span class="hlt">fault</span> features from the denoised <span class="hlt">vibration</span> signals. Third, Laplacian score (LS) approach is employed to refine the <span class="hlt">fault</span> features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the <span class="hlt">fault</span> pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different <span class="hlt">fault</span> categories of planetary gearboxes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...85..746Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...85..746Z"><span>Rolling bearing <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> based on composite multiscale fuzzy entropy and ensemble support vector machines</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zheng, Jinde; Pan, Haiyang; Cheng, Junsheng</p> <p>2017-02-01</p> <p>To timely detect the incipient failure of rolling bearing and find out the accurate <span class="hlt">fault</span> location, a novel rolling bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is proposed based on the composite multiscale fuzzy entropy (CMFE) and ensemble support vector machines (ESVMs). Fuzzy entropy (FuzzyEn), as an improvement of sample entropy (SampEn), is a new nonlinear method for measuring the complexity of time series. Since FuzzyEn (or SampEn) in single scale can not reflect the complexity effectively, multiscale fuzzy entropy (MFE) is developed by defining the FuzzyEns of coarse-grained time series, which represents the system dynamics in different scales. However, the MFE values will be affected by the data length, especially when the data are not long enough. By combining information of multiple coarse-grained time series in the same scale, the CMFE algorithm is proposed in this paper to enhance MFE, as well as FuzzyEn. Compared with MFE, with the increasing of scale factor, CMFE obtains much more stable and consistent values for a short-term time series. In this paper CMFE is employed to measure the complexity of <span class="hlt">vibration</span> signals of rolling bearings and is applied to extract the nonlinear features hidden in the <span class="hlt">vibration</span> signals. Also the physically meanings of CMFE being suitable for rolling bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are explored. Based on these, to fulfill an automatic <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, the ensemble SVMs based multi-classifier is constructed for the intelligent classification of <span class="hlt">fault</span> features. Finally, the proposed <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method of rolling bearing is applied to experimental data analysis and the results indicate that the proposed method could effectively distinguish different <span class="hlt">fault</span> categories and severities of rolling bearings.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MS%26E..197a2079P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MS%26E..197a2079P"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of helical gearbox using acoustic signal and wavelets</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pranesh, SK; Abraham, Siju; Sugumaran, V.; Amarnath, M.</p> <p>2017-05-01</p> <p>The efficient transmission of power in machines is needed and gears are an appropriate choice. <span class="hlt">Faults</span> in gears result in loss of energy and money. The monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are done by analysis of the acoustic and <span class="hlt">vibrational</span> signals which are generally considered to be unwanted by products. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the sound signals produced by the gearbox. Artificial <span class="hlt">faults</span> were created and subsequently signals were captured by a microphone. An extensive study using different wavelet transformations for feature extraction from the acoustic signals was done, followed by waveletselection and feature selection using J48 decision tree and feature classification was performed using K star algorithm. Classification accuracy of 100% was obtained in the study</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5201295','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5201295"><span>A Rolling Element Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Approach Based on Multifractal Theory and Gray Relation Theory</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Li, Jingchao; Cao, Yunpeng; Ying, Yulong; Li, Shuying</p> <p>2016-01-01</p> <p>Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing <span class="hlt">vibration</span> signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of <span class="hlt">fault</span> features from the bearing <span class="hlt">vibration</span> signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing <span class="hlt">fault</span> pattern recognition. The experimental results show that the proposed method can identify various bearing <span class="hlt">fault</span> types as well as severities effectively and accurately. PMID:28036329</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28036329','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28036329"><span>A Rolling Element Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Approach Based on Multifractal Theory and Gray Relation Theory.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Jingchao; Cao, Yunpeng; Ying, Yulong; Li, Shuying</p> <p>2016-01-01</p> <p>Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing <span class="hlt">vibration</span> signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of <span class="hlt">fault</span> features from the bearing <span class="hlt">vibration</span> signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing <span class="hlt">fault</span> pattern recognition. The experimental results show that the proposed method can identify various bearing <span class="hlt">fault</span> types as well as severities effectively and accurately.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28788099','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28788099"><span>An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang; Hu, Jianjun</p> <p>2017-07-28</p> <p>Intelligent machine health monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are becoming increasingly important for modern manufacturing industries. Current <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the <span class="hlt">vibration</span> signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better <span class="hlt">fault</span> <span class="hlt">diagnosis</span> performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5579931','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5579931"><span>An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang</p> <p>2017-01-01</p> <p>Intelligent machine health monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are becoming increasingly important for modern manufacturing industries. Current <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the <span class="hlt">vibration</span> signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better <span class="hlt">fault</span> <span class="hlt">diagnosis</span> performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions. PMID:28788099</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23938005','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23938005"><span>Comparative investigation of <span class="hlt">diagnosis</span> media for induction machine mechanical unbalance <span class="hlt">fault</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Salah, Mohamed; Bacha, Khmais; Chaari, Abdelkader</p> <p>2013-11-01</p> <p>For an induction machine, we suggest a theoretical development of the mechanical unbalance effect on the analytical expressions of radial <span class="hlt">vibration</span> and stator current. Related spectra are described and characteristic defect frequencies are determined. Moreover, the stray flux expressions are developed for both axial and radial sensor coil positions and a substitute <span class="hlt">diagnosis</span> technique is proposed. In addition, the load torque effect on the detection efficiency of these <span class="hlt">diagnosis</span> media is discussed and a comparative investigation is performed. The decisive factor of comparison is the <span class="hlt">fault</span> sensitivity. Experimental results show that spectral analysis of the axial stray flux can be an alternative solution to cover effectiveness limitation of the traditional stator current technique and to substitute the classical <span class="hlt">vibration</span> practice. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26512668','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26512668"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Demountable Disk-Drum Aero-Engine Rotor Using Customized Multiwavelet Method.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chen, Jinglong; Wang, Yu; He, Zhengjia; Wang, Xiaodong</p> <p>2015-10-23</p> <p>The demountable disk-drum aero-engine rotor is an important piece of equipment that greatly impacts the safe operation of aircraft. However, assembly looseness or crack <span class="hlt">fault</span> has led to several unscheduled breakdowns and serious accidents. Thus, condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> technique are required for identifying abnormal conditions. Customized ensemble multiwavelet method for aero-engine rotor condition identification, using measured <span class="hlt">vibration</span> data, is developed in this paper. First, customized multiwavelet basis function with strong adaptivity is constructed via symmetric multiwavelet lifting scheme. Then <span class="hlt">vibration</span> signal is processed by customized ensemble multiwavelet transform. Next, normalized information entropy of multiwavelet decomposition coefficients is computed to directly reflect and evaluate the condition. The proposed approach is first applied to <span class="hlt">fault</span> detection of an experimental aero-engine rotor. Finally, the proposed approach is used in an engineering application, where it successfully identified the crack <span class="hlt">fault</span> of a demountable disk-drum aero-engine rotor. The results show that the proposed method possesses excellent performance in <span class="hlt">fault</span> detection of aero-engine rotor. Moreover, the robustness of the multiwavelet method against noise is also tested and verified by simulation and field experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4634489','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4634489"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Demountable Disk-Drum Aero-Engine Rotor Using Customized Multiwavelet Method</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Chen, Jinglong; Wang, Yu; He, Zhengjia; Wang, Xiaodong</p> <p>2015-01-01</p> <p>The demountable disk-drum aero-engine rotor is an important piece of equipment that greatly impacts the safe operation of aircraft. However, assembly looseness or crack <span class="hlt">fault</span> has led to several unscheduled breakdowns and serious accidents. Thus, condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> technique are required for identifying abnormal conditions. Customized ensemble multiwavelet method for aero-engine rotor condition identification, using measured <span class="hlt">vibration</span> data, is developed in this paper. First, customized multiwavelet basis function with strong adaptivity is constructed via symmetric multiwavelet lifting scheme. Then <span class="hlt">vibration</span> signal is processed by customized ensemble multiwavelet transform. Next, normalized information entropy of multiwavelet decomposition coefficients is computed to directly reflect and evaluate the condition. The proposed approach is first applied to <span class="hlt">fault</span> detection of an experimental aero-engine rotor. Finally, the proposed approach is used in an engineering application, where it successfully identified the crack <span class="hlt">fault</span> of a demountable disk-drum aero-engine rotor. The results show that the proposed method possesses excellent performance in <span class="hlt">fault</span> detection of aero-engine rotor. Moreover, the robustness of the multiwavelet method against noise is also tested and verified by simulation and field experiments. PMID:26512668</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JSV...410..124F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JSV...410..124F"><span>Spectral negentropy based sidebands and demodulation analysis for planet bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Feng, Zhipeng; Ma, Haoqun; Zuo, Ming J.</p> <p>2017-12-01</p> <p>Planet bearing <span class="hlt">vibration</span> signals are highly complex due to intricate kinematics (involving both revolution and spinning) and strong multiple modulations (including not only the <span class="hlt">fault</span> induced amplitude modulation and frequency modulation, but also additional amplitude modulations due to load zone passing, time-varying <span class="hlt">vibration</span> transfer path, and time-varying angle between the gear pair mesh lines of action and <span class="hlt">fault</span> impact force vector), leading to difficulty in <span class="hlt">fault</span> feature extraction. Rolling element bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> essentially relies on detection of <span class="hlt">fault</span> induced repetitive impulses carried by resonance <span class="hlt">vibration</span>, but they are usually contaminated by noise and therefor are hard to be detected. This further adds complexity to planet bearing diagnostics. Spectral negentropy is able to reveal the frequency distribution of repetitive transients, thus providing an approach to identify the optimal frequency band of a filter for separating repetitive impulses. In this paper, we find the informative frequency band (including the center frequency and bandwidth) of bearing <span class="hlt">fault</span> induced repetitive impulses using the spectral negentropy based infogram. In Fourier spectrum, we identify planet bearing <span class="hlt">faults</span> according to sideband characteristics around the center frequency. For demodulation analysis, we filter out the sensitive component based on the informative frequency band revealed by the infogram. In amplitude demodulated spectrum (squared envelope spectrum) of the sensitive component, we diagnose planet bearing <span class="hlt">faults</span> by matching the present peaks with the theoretical <span class="hlt">fault</span> characteristic frequencies. We further decompose the sensitive component into mono-component intrinsic mode functions (IMFs) to estimate their instantaneous frequencies, and select a sensitive IMF with an instantaneous frequency fluctuating around the center frequency for frequency demodulation analysis. In the frequency demodulated spectrum (Fourier spectrum of instantaneous frequency) of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013MSSP...38..515L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013MSSP...38..515L"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, Jinshan; Chen, Qian</p> <p>2013-07-01</p> <p><span class="hlt">Vibration</span> data of faulty rolling bearings are usually nonstationary and nonlinear, and contain fairly weak <span class="hlt">fault</span> features. As a result, feature extraction of rolling bearing <span class="hlt">fault</span> data is always an intractable problem and has attracted considerable attention for a long time. This paper introduces multifractal detrended fluctuation analysis (MF-DFA) to analyze bearing <span class="hlt">vibration</span> data and proposes a novel method for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling bearings based on MF-DFA and Mahalanobis distance criterion (MDC). MF-DFA, an extension of monofractal DFA, is a powerful tool for uncovering the nonlinear dynamical characteristics buried in nonstationary time series and can capture minor changes of complex system conditions. To begin with, by MF-DFA, multifractality of bearing <span class="hlt">fault</span> data was quantified with the generalized Hurst exponent, the scaling exponent and the multifractal spectrum. Consequently, controlled by essentially different dynamical mechanisms, the multifractality of four heterogeneous bearing <span class="hlt">fault</span> data is significantly different; by contrast, controlled by slightly different dynamical mechanisms, the multifractality of homogeneous bearing <span class="hlt">fault</span> data with different <span class="hlt">fault</span> diameters is significantly or slightly different depending on different types of bearing <span class="hlt">faults</span>. Therefore, the multifractal spectrum, as a set of parameters describing multifractality of time series, can be employed to characterize different types and severity of bearing <span class="hlt">faults</span>. Subsequently, five characteristic parameters sensitive to changes of bearing <span class="hlt">fault</span> conditions were extracted from the multifractal spectrum and utilized to construct <span class="hlt">fault</span> features of bearing <span class="hlt">fault</span> data. Moreover, Hilbert transform based envelope analysis, empirical mode decomposition (EMD) and wavelet transform (WT) were utilized to study the same bearing <span class="hlt">fault</span> data. Also, the kurtosis and the peak levels of the EMD or the WT component corresponding to the bearing tones in the frequency domain were carefully checked</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JPhCS.364a2042J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JPhCS.364a2042J"><span>An intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method of rolling bearings based on regularized kernel Marginal Fisher analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiang, Li; Shi, Tielin; Xuan, Jianping</p> <p>2012-05-01</p> <p>Generally, the <span class="hlt">vibration</span> signals of <span class="hlt">fault</span> bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on RKMFA is put forward and applied to <span class="hlt">fault</span> recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional <span class="hlt">vibration</span> signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different <span class="hlt">fault</span> categories of bearings. The experimental results demonstrate that the proposed approach improves the <span class="hlt">fault</span> classification performance and outperforms the other conventional approaches.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28773148','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28773148"><span>An Intelligent Gear <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sun, Weifang; Yao, Bin; Zeng, Nianyin; Chen, Binqiang; He, Yuchao; Cao, Xincheng; He, Wangpeng</p> <p>2017-07-12</p> <p>As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical <span class="hlt">faults</span>. Among these <span class="hlt">faults</span>, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via <span class="hlt">vibration</span> signals, the <span class="hlt">fault</span> signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical <span class="hlt">fault</span>'s characteristic signal is far from an easy task. In order to improve the recognition accuracy of a <span class="hlt">fault</span>'s characteristic signal, a novel intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal's features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a <span class="hlt">fault</span> feature from the multiscale signal features. The experiment results of the recognition for gear <span class="hlt">faults</span> show the feasibility and effectiveness of the proposed method, especially in the gear's weak <span class="hlt">fault</span> features.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ChJME..26..813S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ChJME..26..813S"><span>Rule-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of hall sensors and <span class="hlt">fault</span>-tolerant control of PMSM</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Song, Ziyou; Li, Jianqiu; Ouyang, Minggao; Gu, Jing; Feng, Xuning; Lu, Dongbin</p> <p>2013-07-01</p> <p>Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor <span class="hlt">faults</span> occur. But there is scarcely any research focusing on <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and <span class="hlt">fault</span>-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor <span class="hlt">faults</span> which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm of Hall sensor, which is based on three rules, is proposed to classify the <span class="hlt">fault</span> phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the <span class="hlt">fault</span>-tolerant control algorithm. The <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm can detect 60 Hall <span class="hlt">fault</span> phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The <span class="hlt">fault</span>-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and <span class="hlt">fault</span>-tolerant control strategies. The <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm can detect all Hall sensor <span class="hlt">faults</span> promptly and <span class="hlt">fault</span>-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and <span class="hlt">fault</span>-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor <span class="hlt">faults</span> of PMSM in real applications, and can be provided to realize the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and <span class="hlt">fault</span>-tolerant control of PMSM.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1002404','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1002404"><span>A <span class="hlt">Vibration</span>-Based Approach for Stator Winding <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Induction Motors: Application of Envelope Analysis</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-10-02</p> <p>takes it either as auxiliary to magnetic flux, or is not able to detect the winding <span class="hlt">faults</span> unless severity is already quite significant. This paper...different loads, speeds and severity levels. The experimental results show that the proposed method was able to detect inter-turn <span class="hlt">faults</span> in the...maintenance strategy requires the technologies of: (a) on- line condition monitoring, (b) <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span>, and (c) prognostics. Figure 1</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27865432','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27865432"><span>A hybrid <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approach based on mixed-domain state features for rotating machinery.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Xue, Xiaoming; Zhou, Jianzhong</p> <p>2017-01-01</p> <p>To make further improvement in the <span class="hlt">diagnosis</span> accuracy and efficiency, a mixed-domain state features data based hybrid <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> problems, the execution of the proposed method is divided into three steps, i.e., <span class="hlt">fault</span> preliminary detection, <span class="hlt">fault</span> type recognition and <span class="hlt">fault</span> degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If <span class="hlt">fault</span> exists, the following two processes based on the artificial intelligence approach are performed to further recognize the <span class="hlt">fault</span> type and then identify the <span class="hlt">fault</span> degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the <span class="hlt">fault</span> peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where <span class="hlt">vibration</span> signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the <span class="hlt">diagnosis</span> thought is more suitable for practical application. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29751671','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29751671"><span>Planetary Gears Feature Extraction and <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Method Based on VMD and CNN.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong</p> <p>2018-05-11</p> <p>Given local weak feature information, a novel feature extraction and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original <span class="hlt">vibration</span> signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current <span class="hlt">fault</span> state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear <span class="hlt">fault</span> state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different <span class="hlt">faults</span>. The singular value vector matrices of different <span class="hlt">fault</span> states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> technique for planetary gears.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948592','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948592"><span>Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> by a Robust Higher-Order Super-Twisting Sliding Mode Observer</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kim, Jong-Myon</p> <p>2018-01-01</p> <p>An effective bearing <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing’s <span class="hlt">vibration</span> data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a <span class="hlt">vibration</span> dataset provided by Case Western Reserve University, which consists of <span class="hlt">vibration</span> acceleration signals recorded for REBs with inner, outer, ball, and no <span class="hlt">faults</span>, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively. PMID:29642459</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29642459','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29642459"><span>Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> by a Robust Higher-Order Super-Twisting Sliding Mode Observer.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Piltan, Farzin; Kim, Jong-Myon</p> <p>2018-04-07</p> <p>An effective bearing <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing's <span class="hlt">vibration</span> data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a <span class="hlt">vibration</span> dataset provided by Case Western Reserve University, which consists of <span class="hlt">vibration</span> acceleration signals recorded for REBs with inner, outer, ball, and no <span class="hlt">faults</span>, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040085710','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040085710"><span>Composite Bending Box Section Modal <span class="hlt">Vibration</span> <span class="hlt">Fault</span> Detection</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Werlink, Rudy</p> <p>2002-01-01</p> <p>One of the primary concerns with Composite construction in critical structures such as wings and stabilizers is that hidden <span class="hlt">faults</span> and cracks can develop operationally. In the real world, catastrophic sudden failure can result from these undetected <span class="hlt">faults</span> in composite structures. <span class="hlt">Vibration</span> data incorporating a broad frequency modal approach, could detect significant changes prior to failure. The purpose of this report is to investigate the usefulness of frequency mode testing before and after bending and torsion loading on a composite bending Box Test section. This test article is representative of construction techniques being developed for the recent NASA Blended Wing Body Low Speed Vehicle Project. The Box section represents the construction technique on the proposed blended wing aircraft. Modal testing using an impact hammer provides an frequency fingerprint before and after bending and torsional loading. If a significant structural discontinuity develops, the <span class="hlt">vibration</span> response is expected to change. The limitations of the data will be evaluated for future use as a non-destructive in-situ method of assessing hidden damage in similarly constructed composite wing assemblies. Modal <span class="hlt">vibration</span> <span class="hlt">fault</span> detection sensitivity to band-width, location and axis will be investigated. Do the sensor accelerometers need to be near the <span class="hlt">fault</span> and or in the same axis? The response data used in this report was recorded at 17 locations using tri-axial accelerometers. The modal tests were conducted following 5 independent loading conditions before load to failure and 2 following load to failure over a period of 6 weeks. Redundant data was used to minimize effects from uncontrolled variables which could lead to incorrect interpretations. It will be shown that <span class="hlt">vibrational</span> modes detected failure at many locations when skin de-bonding failures occurred near the center section. Important considerations are the axis selected and frequency range.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...84...15W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...84...15W"><span>A sensor network based virtual beam-like structure method for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and monitoring of complex structures with Improved Bacterial Optimization</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, H.; Jing, X. J.</p> <p>2017-02-01</p> <p>This paper proposes a novel method for the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of complex structures based on an optimized virtual beam-like structure approach. A complex structure can be regarded as a combination of numerous virtual beam-like structures considering the <span class="hlt">vibration</span> transmission path from <span class="hlt">vibration</span> sources to each sensor. The structural 'virtual beam' consists of a sensor chain automatically obtained by an Improved Bacterial Optimization Algorithm (IBOA). The biologically inspired optimization method (i.e. IBOA) is proposed for solving the discrete optimization problem associated with the selection of the optimal virtual beam for <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. This novel virtual beam-like-structure approach needs less or little prior knowledge. Neither does it require stationary response data, nor is it confined to a specific structure design. It is easy to implement within a sensor network attached to the monitored structure. The proposed <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method has been tested on the detection of loosening screws located at varying positions in a real satellite-like model. Compared with empirical methods, the proposed virtual beam-like structure method has proved to be very effective and more reliable for <span class="hlt">fault</span> localization.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29495646','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29495646"><span>Ontology-Based Method for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Loaders.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Xu, Feixiang; Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei</p> <p>2018-02-28</p> <p>This paper proposes an ontology-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method which overcomes the difficulty of understanding complex <span class="hlt">fault</span> <span class="hlt">diagnosis</span> knowledge of loaders and offers a universal approach for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of all loaders. This method contains the following components: (1) An ontology-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> model is proposed to achieve the integrating, sharing and reusing of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate <span class="hlt">fault</span> diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the <span class="hlt">fault</span> causes, <span class="hlt">fault</span> locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5876616','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5876616"><span>Ontology-Based Method for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Loaders</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei</p> <p>2018-01-01</p> <p>This paper proposes an ontology-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method which overcomes the difficulty of understanding complex <span class="hlt">fault</span> <span class="hlt">diagnosis</span> knowledge of loaders and offers a universal approach for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of all loaders. This method contains the following components: (1) An ontology-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> model is proposed to achieve the integrating, sharing and reusing of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate <span class="hlt">fault</span> diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the <span class="hlt">fault</span> causes, <span class="hlt">fault</span> locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study. PMID:29495646</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5335956','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5335956"><span>Optimal Resonant Band Demodulation Based on an Improved Correlated Kurtosis and Its Application in Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Chen, Xianglong; Zhang, Bingzhi; Feng, Fuzhou; Jiang, Pengcheng</p> <p>2017-01-01</p> <p>The kurtosis-based indexes are usually used to identify the optimal resonant frequency band. However, kurtosis can only describe the strength of transient impulses, which cannot differentiate impulse noises and repetitive transient impulses cyclically generated in bearing <span class="hlt">vibration</span> signals. As a result, it may lead to inaccurate results in identifying resonant frequency bands, in demodulating <span class="hlt">fault</span> features and hence in <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. In view of those drawbacks, this manuscript redefines the correlated kurtosis based on kurtosis and auto-correlative function, puts forward an improved correlated kurtosis based on squared envelope spectrum of bearing <span class="hlt">vibration</span> signals. Meanwhile, this manuscript proposes an optimal resonant band demodulation method, which can adaptively determine the optimal resonant frequency band and accurately demodulate transient <span class="hlt">fault</span> features of rolling bearings, by combining the complex Morlet wavelet filter and the Particle Swarm Optimization algorithm. Analysis of both simulation data and experimental data reveal that the improved correlated kurtosis can effectively remedy the drawbacks of kurtosis-based indexes and the proposed optimal resonant band demodulation is more accurate in identifying the optimal central frequencies and bandwidth of resonant bands. Improved <span class="hlt">fault</span> <span class="hlt">diagnosis</span> results in experiment verified the validity and advantage of the proposed method over the traditional kurtosis-based indexes. PMID:28208820</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JSV...414...43H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JSV...414...43H"><span>Bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under unknown time-varying rotational speed conditions via multiple time-frequency curve extraction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huang, Huan; Baddour, Natalie; Liang, Ming</p> <p>2018-02-01</p> <p>Under normal operating conditions, bearings often run under time-varying rotational speed conditions. Under such circumstances, the bearing <span class="hlt">vibrational</span> signal is non-stationary, which renders ineffective the techniques used for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under constant running conditions. One of the conventional methods of bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under time-varying speed conditions is resampling the non-stationary signal to a stationary signal via order tracking with the measured variable speed. With the resampled signal, the methods available for constant condition cases are thus applicable. However, the accuracy of the order tracking is often inadequate and the time-varying speed is sometimes not measurable. Thus, resampling-free methods are of interest for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under time-varying rotational speed for use without tachometers. With the development of time-frequency analysis, the time-varying <span class="hlt">fault</span> character manifests as curves in the time-frequency domain. By extracting the Instantaneous <span class="hlt">Fault</span> Characteristic Frequency (IFCF) from the Time-Frequency Representation (TFR) and converting the IFCF, its harmonics, and the Instantaneous Shaft Rotational Frequency (ISRF) into straight lines, the bearing <span class="hlt">fault</span> can be detected and diagnosed without resampling. However, so far, the extraction of the IFCF for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is mostly based on the assumption that at each moment the IFCF has the highest amplitude in the TFR, which is not always true. Hence, a more reliable T-F curve extraction approach should be investigated. Moreover, if the T-F curves including the IFCF, its harmonic, and the ISRF can be all extracted from the TFR directly, no extra processing is needed for <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Therefore, this paper proposes an algorithm for multiple T-F curve extraction from the TFR based on a fast path optimization which is more reliable for T-F curve extraction. Then, a new procedure for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under unknown time-varying speed</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25685841','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25685841"><span>A dynamic integrated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for power transformers.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gao, Wensheng; Bai, Cuifen; Liu, Tong</p> <p>2015-01-01</p> <p>In order to diagnose transformer <span class="hlt">fault</span> efficiently and accurately, a dynamic integrated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on Bayesian network is proposed in this paper. First, an integrated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the <span class="hlt">diagnosis</span> model is gradually acquired and the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> process in reality is multistep, a dynamic <span class="hlt">fault</span> <span class="hlt">diagnosis</span> mechanism is proposed based on the integrated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> model. Different from the existing one-step <span class="hlt">diagnosis</span> mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of <span class="hlt">diagnosis</span>. Finally, the dynamic integrated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is applied to actual cases, and the validity of this method is verified.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4320846','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4320846"><span>A Dynamic Integrated <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Method for Power Transformers</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Gao, Wensheng; Liu, Tong</p> <p>2015-01-01</p> <p>In order to diagnose transformer <span class="hlt">fault</span> efficiently and accurately, a dynamic integrated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on Bayesian network is proposed in this paper. First, an integrated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the <span class="hlt">diagnosis</span> model is gradually acquired and the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> process in reality is multistep, a dynamic <span class="hlt">fault</span> <span class="hlt">diagnosis</span> mechanism is proposed based on the integrated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> model. Different from the existing one-step <span class="hlt">diagnosis</span> mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of <span class="hlt">diagnosis</span>. Finally, the dynamic integrated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is applied to actual cases, and the validity of this method is verified. PMID:25685841</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5551833','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5551833"><span>An Intelligent Gear <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Sun, Weifang; Yao, Bin; Zeng, Nianyin; He, Yuchao; Cao, Xincheng; He, Wangpeng</p> <p>2017-01-01</p> <p>As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical <span class="hlt">faults</span>. Among these <span class="hlt">faults</span>, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via <span class="hlt">vibration</span> signals, the <span class="hlt">fault</span> signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault’s characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault’s characteristic signal, a novel intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal’s features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a <span class="hlt">fault</span> feature from the multiscale signal features. The experiment results of the recognition for gear <span class="hlt">faults</span> show the feasibility and effectiveness of the proposed method, especially in the gear’s weak <span class="hlt">fault</span> features. PMID:28773148</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AIPC.1831b0053W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AIPC.1831b0053W"><span><span class="hlt">Fault</span> detection of gearbox using time-frequency method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Widodo, A.; Satrijo, Dj.; Prahasto, T.; Haryanto, I.</p> <p>2017-04-01</p> <p>This research deals with <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> of gearbox by using <span class="hlt">vibration</span> signature. In this work, <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> are approached by employing time-frequency method, and then the results are compared with cepstrum analysis. Experimental work has been conducted for data acquisition of <span class="hlt">vibration</span> signal thru self-designed gearbox test rig. This test-rig is able to demonstrate normal and faulty gearbox i.e., wears and tooth breakage. Three accelerometers were used for <span class="hlt">vibration</span> signal acquisition from gearbox, and optical tachometer was used for shaft rotation speed measurement. The results show that frequency domain analysis using fast-fourier transform was less sensitive to wears and tooth breakage condition. However, the method of short-time fourier transform was able to monitor the <span class="hlt">faults</span> in gearbox. Wavelet Transform (WT) method also showed good performance in gearbox <span class="hlt">fault</span> detection using <span class="hlt">vibration</span> signal after employing time synchronous averaging (TSA).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..101..292W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..101..292W"><span>Sparsity guided empirical wavelet transform for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Dong; Zhao, Yang; Yi, Cai; Tsui, Kwok-Leung; Lin, Jianhui</p> <p>2018-02-01</p> <p>Rolling element bearings are widely used in various industrial machines, such as electric motors, generators, pumps, gearboxes, railway axles, turbines, and helicopter transmissions. <span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of rolling element bearings is beneficial to preventing any unexpected accident and reducing economic loss. In the past years, many bearing <span class="hlt">fault</span> detection methods have been developed. Recently, a new adaptive signal processing method called empirical wavelet transform attracts much attention from readers and engineers and its applications to bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> have been reported. The main problem of empirical wavelet transform is that Fourier segments required in empirical wavelet transform are strongly dependent on the local maxima of the amplitudes of the Fourier spectrum of a signal, which connotes that Fourier segments are not always reliable and effective if the Fourier spectrum of the signal is complicated and overwhelmed by heavy noises and other strong <span class="hlt">vibration</span> components. In this paper, sparsity guided empirical wavelet transform is proposed to automatically establish Fourier segments required in empirical wavelet transform for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings. Industrial bearing <span class="hlt">fault</span> signals caused by single and multiple railway axle bearing defects are used to verify the effectiveness of the proposed sparsity guided empirical wavelet transform. Results show that the proposed method can automatically discover Fourier segments required in empirical wavelet transform and reveal single and multiple railway axle bearing defects. Besides, some comparisons with three popular signal processing methods including ensemble empirical mode decomposition, the fast kurtogram and the fast spectral correlation are conducted to highlight the superiority of the proposed method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...97...33C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...97...33C"><span><span class="hlt">Fault</span> detection in rotating machines with beamforming: Spatial visualization of <span class="hlt">diagnosis</span> features</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cardenas Cabada, E.; Leclere, Q.; Antoni, J.; Hamzaoui, N.</p> <p>2017-12-01</p> <p>Rotating machines <span class="hlt">diagnosis</span> is conventionally related to <span class="hlt">vibration</span> analysis. Sensors are usually placed on the machine to gather information about its components. The recorded signals are then processed through a <span class="hlt">fault</span> detection algorithm allowing the identification of the failing part. This paper proposes an acoustic-based <span class="hlt">diagnosis</span> method. A microphone array is used to record the acoustic field radiated by the machine. The main advantage over <span class="hlt">vibration</span>-based <span class="hlt">diagnosis</span> is that the contact between the sensors and the machine is no longer required. Moreover, the application of acoustic imaging makes possible the identification of the sources of acoustic radiation on the machine surface. The display of information is then spatially continuous while the accelerometers only give it discrete. Beamforming provides the time-varying signals radiated by the machine as a function of space. Any <span class="hlt">fault</span> detection tool can be applied to the beamforming output. Spectral kurtosis, which highlights the impulsiveness of a signal as function of frequency, is used in this study. The combination of spectral kurtosis with acoustic imaging makes possible the mapping of the impulsiveness as a function of space and frequency. The efficiency of this approach lays on the source separation in the spatial and frequency domains. These mappings make possible the localization of such impulsive sources. The faulty components of the machine have an impulsive behavior and thus will be highlighted on the mappings. The study presents experimental validations of the method on rotating machines.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://eric.ed.gov/?q=knitting&pg=3&id=EJ097494','ERIC'); return false;" href="https://eric.ed.gov/?q=knitting&pg=3&id=EJ097494"><span>Training for Skill in <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Turner, J. D.</p> <p>1974-01-01</p> <p>The Knitting, Lace and Net Industry Training Board has developed a training innovation called <span class="hlt">fault</span> <span class="hlt">diagnosis</span> training. The entire training process concentrates on teaching based on the experiences of troubleshooters or any other employees whose main tasks involve <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and rectification. (Author/DS)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3943295','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3943295"><span>Experimental Investigation for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Li, Pengfei; Jiang, Yongying; Xiang, Jiawei</p> <p>2014-01-01</p> <p>To deal with the difficulty to obtain a large number of <span class="hlt">fault</span> samples under the practical condition for mechanical <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the <span class="hlt">vibration</span> signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear <span class="hlt">fault</span> diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical <span class="hlt">faults</span> under the condition of small <span class="hlt">fault</span> samples. PMID:24688361</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5982505','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5982505"><span>Planetary Gears Feature Extraction and <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Method Based on VMD and CNN</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Cheng, Gang; Chen, Xihui</p> <p>2018-01-01</p> <p>Given local weak feature information, a novel feature extraction and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original <span class="hlt">vibration</span> signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current <span class="hlt">fault</span> state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear <span class="hlt">fault</span> state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different <span class="hlt">faults</span>. The singular value vector matrices of different <span class="hlt">fault</span> states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> technique for planetary gears. PMID:29751671</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19930022657','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19930022657"><span>Efficient <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of helicopter gearboxes</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chin, H.; Danai, K.; Lewicki, D. G.</p> <p>1993-01-01</p> <p>Application of a diagnostic system to a helicopter gearbox is presented. The diagnostic system is a nonparametric pattern classifier that uses a multi-valued influence matrix (MVIM) as its diagnostic model and benefits from a fast learning algorithm that enables it to estimate its diagnostic model from a small number of measurement-<span class="hlt">fault</span> data. To test this diagnostic system, <span class="hlt">vibration</span> measurements were collected from a helicopter gearbox test stand during accelerated fatigue tests and at various <span class="hlt">fault</span> instances. The diagnostic results indicate that the MVIM system can accurately detect and diagnose various gearbox <span class="hlt">faults</span> so long as they are included in training.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..310a2076R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..310a2076R"><span>Machinery Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Using Variational Mode Decomposition and Support Vector Machine as a Classifier</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rama Krishna, K.; Ramachandran, K. I.</p> <p>2018-02-01</p> <p>Crack propagation is a major cause of failure in rotating machines. It adversely affects the productivity, safety, and the machining quality. Hence, detecting the crack’s severity accurately is imperative for the predictive maintenance of such machines. <span class="hlt">Fault</span> <span class="hlt">diagnosis</span> is an established concept in identifying the <span class="hlt">faults</span>, for observing the non-linear behaviour of the <span class="hlt">vibration</span> signals at various operating conditions. In this work, we find the classification efficiencies for both original and the reconstructed <span class="hlt">vibrational</span> signals. The reconstructed signals are obtained using Variational Mode Decomposition (VMD), by splitting the original signal into three intrinsic mode functional components and framing them accordingly. Feature extraction, feature selection and feature classification are the three phases in obtaining the classification efficiencies. All the statistical features from the original signals and reconstructed signals are found out in feature extraction process individually. A few statistical parameters are selected in feature selection process and are classified using the SVM classifier. The obtained results show the best parameters and appropriate kernel in SVM classifier for detecting the <span class="hlt">faults</span> in bearings. Hence, we conclude that better results were obtained by VMD and SVM process over normal process using SVM. This is owing to denoising and filtering the raw <span class="hlt">vibrational</span> signals.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29331434','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29331434"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of rolling element bearing using a new optimal scale morphology analysis method.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yan, Xiaoan; Jia, Minping; Zhang, Wan; Zhu, Lin</p> <p>2018-02-01</p> <p>Periodic transient impulses are key indicators of rolling element bearing defects. Efficient acquisition of impact impulses concerned with the defects is of much concern to the precise detection of bearing defects. However, transient features of rolling element bearing are generally immersed in stochastic noise and harmonic interference. Therefore, in this paper, a new optimal scale morphology analysis method, named adaptive multiscale combination morphological filter-hat transform (AMCMFH), is proposed for rolling element bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, which can both reduce stochastic noise and reserve signal details. In this method, firstly, an adaptive selection strategy based on the feature energy factor (FEF) is introduced to determine the optimal structuring element (SE) scale of multiscale combination morphological filter-hat transform (MCMFH). Subsequently, MCMFH containing the optimal SE scale is applied to obtain the impulse components from the bearing <span class="hlt">vibration</span> signal. Finally, <span class="hlt">fault</span> types of bearing are confirmed by extracting the defective frequency from envelope spectrum of the impulse components. The validity of the proposed method is verified through the simulated analysis and bearing <span class="hlt">vibration</span> data derived from the laboratory bench. Results indicate that the proposed method has a good capability to recognize localized <span class="hlt">faults</span> appeared on rolling element bearing from <span class="hlt">vibration</span> signal. The study supplies a novel technique for the detection of faulty bearing. Copyright © 2018. Published by Elsevier Ltd.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MeScT..28l5104W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MeScT..28l5104W"><span>A hybrid approach to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of roller bearings under variable speed conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Yanxue; Yang, Lin; Xiang, Jiawei; Yang, Jianwei; He, Shuilong</p> <p>2017-12-01</p> <p>Rolling element bearings are one of the main elements in rotating machines, whose failure may lead to a fatal breakdown and significant economic losses. Conventional <span class="hlt">vibration</span>-based diagnostic methods are based on the stationary assumption, thus they are not applicable to the <span class="hlt">diagnosis</span> of bearings working under varying speeds. This constraint limits the bearing <span class="hlt">diagnosis</span> to the industrial application significantly. A hybrid approach to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of roller bearings under variable speed conditions is proposed in this work, based on computed order tracking (COT) and variational mode decomposition (VMD)-based time frequency representation (VTFR). COT is utilized to resample the non-stationary <span class="hlt">vibration</span> signal in the angular domain, while VMD is used to decompose the resampled signal into a number of band-limited intrinsic mode functions (BLIMFs). A VTFR is then constructed based on the estimated instantaneous frequency and instantaneous amplitude of each BLIMF. Moreover, the Gini index and time-frequency kurtosis are both proposed to quantitatively measure the sparsity and concentration measurement of time-frequency representation, respectively. The effectiveness of the VTFR for extracting nonlinear components has been verified by a bat signal. Results of this numerical simulation also show the sparsity and concentration of the VTFR are better than those of short-time Fourier transform, continuous wavelet transform, Hilbert-Huang transform and Wigner-Ville distribution techniques. Several experimental results have further demonstrated that the proposed method can well detect bearing <span class="hlt">faults</span> under variable speed conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940032446','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940032446"><span><span class="hlt">Vibration</span> Signature Analysis of a <span class="hlt">Faulted</span> Gear Transmission System</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Choy, F. K.; Huang, S.; Zakrajsek, J. J.; Handschuh, R. F.; Townsend, D. P.</p> <p>1994-01-01</p> <p>A comprehensive procedure in predicting <span class="hlt">faults</span> in gear transmission systems under normal operating conditions is presented. Experimental data was obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. Time synchronous averaged <span class="hlt">vibration</span> data was recorded throughout the test as the <span class="hlt">fault</span> progressed from a small single pit to severe pitting over several teeth, and finally tooth fracture. A numerical procedure based on the Winger-Ville distribution was used to examine the time averaged <span class="hlt">vibration</span> data. Results from the Wigner-Ville procedure are compared to results from a variety of signal analysis techniques which include time domain analysis methods and frequency analysis methods. Using photographs of the gear tooth at various stages of damage, the limitations and accuracy of the various techniques are compared and discussed. Conclusions are drawn from the comparison of the different approaches as well as the applicability of the Wigner-Ville method in predicting gear <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JSV...360..277L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JSV...360..277L"><span>A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Yongbo; Xu, Minqiang; Wang, Rixin; Huang, Wenhu</p> <p>2016-01-01</p> <p>This paper presents a new rolling bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on local mean decomposition (LMD), improved multiscale fuzzy entropy (IMFE), Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT). When the <span class="hlt">fault</span> occurs in rolling bearings, the measured <span class="hlt">vibration</span> signal is a multi-component amplitude-modulated and frequency-modulated (AM-FM) signal. LMD, a new self-adaptive time-frequency analysis method can decompose any complicated signal into a series of product functions (PFs), each of which is exactly a mono-component AM-FM signal. Hence, LMD is introduced to preprocess the <span class="hlt">vibration</span> signal. Furthermore, IMFE that is designed to avoid the inaccurate estimation of fuzzy entropy can be utilized to quantify the complexity and self-similarity of time series for a range of scales based on fuzzy entropy. Besides, the LS approach is introduced to refine the <span class="hlt">fault</span> features by sorting the scale factors. Subsequently, the obtained features are fed into the multi-<span class="hlt">fault</span> classifier ISVM-BT to automatically fulfill the <span class="hlt">fault</span> pattern identifications. The experimental results validate the effectiveness of the methodology and demonstrate that proposed algorithm can be applied to recognize the different categories and severities of rolling bearings.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5876880','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5876880"><span>EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Railway Axle Bearings</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Fan, Wei; Tsui, Kwok-Leung; Lin, Jianhui</p> <p>2018-01-01</p> <p>Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing <span class="hlt">vibration</span> signals. Firstly, <span class="hlt">vibration</span> data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed <span class="hlt">vibration</span> data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> scheme is effective in identifying different bearing health conditions. PMID:29495446</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4634422','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4634422"><span>Compressive Sensing of Roller Bearing <span class="hlt">Faults</span> via Harmonic Detection from Under-Sampled <span class="hlt">Vibration</span> Signals</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei</p> <p>2015-01-01</p> <p>The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing <span class="hlt">fault</span> signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed <span class="hlt">vibration</span> signals for detecting roller bearing <span class="hlt">faults</span> is developed in this study. Considering that harmonics often represent the <span class="hlt">fault</span> characteristic frequencies in <span class="hlt">vibration</span> signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing <span class="hlt">faults</span>. A compressed <span class="hlt">vibration</span> signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled <span class="hlt">vibration</span> signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing <span class="hlt">fault</span> features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments. PMID:26473858</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900051006&hterms=artificial+intelligence+diagnosis&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dartificial%2Bintelligence%2Bdiagnosis','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900051006&hterms=artificial+intelligence+diagnosis&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dartificial%2Bintelligence%2Bdiagnosis"><span>A survey of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> technology</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Riedesel, Joel</p> <p>1989-01-01</p> <p>Existing techniques and methodologies for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are surveyed. The techniques run the gamut from theoretical artificial intelligence work to conventional software engineering applications. They are shown to define a spectrum of implementation alternatives where tradeoffs determine their position on the spectrum. Various tradeoffs include execution time limitations and memory requirements of the algorithms as well as their effectiveness in addressing the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> problem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948935','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948935"><span>Intelligent <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of HVCB with Feature Space Optimization-Based Random Forest</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Ma, Suliang; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan</p> <p>2018-01-01</p> <p>Mechanical <span class="hlt">faults</span> of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the <span class="hlt">fault</span> features and identifying the <span class="hlt">fault</span> type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB <span class="hlt">fault</span>, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB <span class="hlt">vibration</span> signals by considering six typical <span class="hlt">fault</span> classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed <span class="hlt">diagnosis</span> algorithm has higher efficiency and robustness than traditional methods. PMID:29659548</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..103...76B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..103...76B"><span>Amplitude-cyclic frequency decomposition of <span class="hlt">vibration</span> signals for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on phase editing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barbini, L.; Eltabach, M.; Hillis, A. J.; du Bois, J. L.</p> <p>2018-03-01</p> <p>In rotating machine <span class="hlt">diagnosis</span> different spectral tools are used to analyse <span class="hlt">vibration</span> signals. Despite the good diagnostic performance such tools are usually refined, computationally complex to implement and require oversight of an expert user. This paper introduces an intuitive and easy to implement method for <span class="hlt">vibration</span> analysis: amplitude cyclic frequency decomposition. This method firstly separates <span class="hlt">vibration</span> signals accordingly to their spectral amplitudes and secondly uses the squared envelope spectrum to reveal the presence of cyclostationarity in each amplitude level. The intuitive idea is that in a rotating machine different components contribute <span class="hlt">vibrations</span> at different amplitudes, for instance defective bearings contribute a very weak signal in contrast to gears. This paper also introduces a new quantity, the decomposition squared envelope spectrum, which enables separation between the components of a rotating machine. The amplitude cyclic frequency decomposition and the decomposition squared envelope spectrum are tested on real word signals, both at stationary and varying speeds, using data from a wind turbine gearbox and an aircraft engine. In addition a benchmark comparison to the spectral correlation method is presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5751710','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5751710"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jiang, Zhinong; Wang, Zijia; Zhang, Jinjie</p> <p>2017-01-01</p> <p>Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance <span class="hlt">fault</span> based on the <span class="hlt">vibration</span> signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from <span class="hlt">vibration</span> signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance <span class="hlt">fault</span> becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable. PMID:29244722</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29244722','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29244722"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jiang, Zhinong; Mao, Zhiwei; Wang, Zijia; Zhang, Jinjie</p> <p>2017-12-15</p> <p>Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance <span class="hlt">fault</span> based on the <span class="hlt">vibration</span> signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from <span class="hlt">vibration</span> signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance <span class="hlt">fault</span> becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SPIE10605E..20W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SPIE10605E..20W"><span>Research of test <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for micro-satellite PSS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Haichao; Wang, Jinqi; Yang, Zhi; Yan, Meizhi</p> <p>2017-11-01</p> <p>Along with the increase in the number of micro-satellite and the shortening of the product's lifecycle, negative effects of satellite ground test failure become more and more serious. Real-time and efficient <span class="hlt">fault</span> <span class="hlt">diagnosis</span> becomes more and more necessary. PSS plays an important role in the satellite ground test's safety and reliability as one of the most important subsystems that guarantees the safety of micro-satellite energy. Take test <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method of micro-satellite PSS as research object. On the basis of system features of PSS and classic <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods, propose a kind of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on the layered and loose coupling way. This article can provide certain reference for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods research of other subsystems of micro-satellite.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MeScT..29e5012L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MeScT..29e5012L"><span>Extraction of repetitive transients with frequency domain multipoint kurtosis for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liao, Yuhe; Sun, Peng; Wang, Baoxiang; Qu, Lei</p> <p>2018-05-01</p> <p>The appearance of repetitive transients in a <span class="hlt">vibration</span> signal is one typical feature of faulty rolling element bearings. However, accurate extraction of these <span class="hlt">fault</span>-related characteristic components has always been a challenging task, especially when there is interference from large amplitude impulsive noises. A frequency domain multipoint kurtosis (FDMK)-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is proposed in this paper. The multipoint kurtosis is redefined in the frequency domain and the computational accuracy is improved. An envelope autocorrelation function is also presented to estimate the <span class="hlt">fault</span> characteristic frequency, which is used to set the frequency hunting zone of the FDMK. Then, the FDMK, instead of kurtosis, is utilized to generate a fast kurtogram and only the optimal band with maximum FDMK value is selected for envelope analysis. Negative interference from both large amplitude impulsive noise and shaft rotational speed related harmonic components are therefore greatly reduced. The analysis results of simulation and experimental data verify the capability and feasibility of this FDMK-based method</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23793021','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23793021"><span>Sequential fuzzy <span class="hlt">diagnosis</span> method for motor roller bearing in variable operating conditions based on <span class="hlt">vibration</span> analysis.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Ke; Ping, Xueliang; Wang, Huaqing; Chen, Peng; Cao, Yi</p> <p>2013-06-21</p> <p>A novel intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary <span class="hlt">vibration</span> signal measured for condition <span class="hlt">diagnosis</span>. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the <span class="hlt">vibration</span> signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition <span class="hlt">diagnosis</span> are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition <span class="hlt">diagnosis</span>. Finally, a fuzzy <span class="hlt">diagnosis</span> method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3715255','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3715255"><span>Sequential Fuzzy <span class="hlt">Diagnosis</span> Method for Motor Roller Bearing in Variable Operating Conditions Based on <span class="hlt">Vibration</span> Analysis</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Li, Ke; Ping, Xueliang; Wang, Huaqing; Chen, Peng; Cao, Yi</p> <p>2013-01-01</p> <p>A novel intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary <span class="hlt">vibration</span> signal measured for condition <span class="hlt">diagnosis</span>. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the <span class="hlt">vibration</span> signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition <span class="hlt">diagnosis</span> are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition <span class="hlt">diagnosis</span>. Finally, a fuzzy <span class="hlt">diagnosis</span> method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well. PMID:23793021</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MeScT..27b5001Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MeScT..27b5001Z"><span>An adaptive demodulation approach for bearing <span class="hlt">fault</span> detection based on adaptive wavelet filtering and spectral subtraction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang</p> <p>2016-02-01</p> <p><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of rolling element bearings is important for improving mechanical system reliability and performance. <span class="hlt">Vibration</span> signals contain a wealth of complex information useful for state monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. However, any <span class="hlt">fault</span>-related impulses in the original signal are often severely tainted by various noises and the interfering <span class="hlt">vibrations</span> caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing <span class="hlt">faults</span> by identifying bearing <span class="hlt">fault</span> characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing <span class="hlt">fault</span>. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in bearings. First, to eliminate the frequency associated with interfering <span class="hlt">vibrations</span>, the <span class="hlt">vibration</span> signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and <span class="hlt">vibration</span> data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the <span class="hlt">fault</span>-related impulses</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20542268','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20542268"><span>Multiple sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for dynamic processes.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Cheng-Chih; Jeng, Jyh-Cheng</p> <p>2010-10-01</p> <p>Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor <span class="hlt">faults</span> for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor <span class="hlt">fault</span> matrix (BSFM), consisting of the normalized basic <span class="hlt">fault</span> vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. This study also proposes a novel monitoring index and derives corresponding sensor <span class="hlt">fault</span> detectability. The study also utilizes that vector to isolate and identify multiple sensor <span class="hlt">faults</span>, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006MSSP...20..939Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006MSSP...20..939Y"><span>Support vector machines-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for turbo-pump rotor</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yuan, Sheng-Fa; Chu, Fu-Lei</p> <p>2006-05-01</p> <p>Most artificial intelligence methods used in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are based on empirical risk minimisation principle and have poor generalisation when <span class="hlt">fault</span> samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when <span class="hlt">fault</span> samples are few. <span class="hlt">Fault</span> <span class="hlt">diagnosis</span> based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> problems are multi-class cases, a new multi-class classification of SVM named 'one to others' algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by <span class="hlt">fault</span> priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for turbo pump rotor.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22035775','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22035775"><span>A novel KFCM based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for unknown <span class="hlt">faults</span> in satellite reaction wheels.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hu, Di; Sarosh, Ali; Dong, Yun-Feng</p> <p>2012-03-01</p> <p>Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct <span class="hlt">diagnosis</span> of their <span class="hlt">faults</span> is quintessential for efficient operation of these spacecraft. The known <span class="hlt">faults</span> in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown <span class="hlt">fault</span> occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct <span class="hlt">diagnosis</span>. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of both known and unknown <span class="hlt">faults</span> as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known <span class="hlt">faults</span>, in this way discrete number of <span class="hlt">fault</span> classes are determined at the start. Similarity parameters are derived and determined for each of the <span class="hlt">fault</span> data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the 'known-<span class="hlt">fault</span>' classes while the low similarity points are labeled as 'unknown-<span class="hlt">faults</span>'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical <span class="hlt">fault</span> data (as in reaction wheels) and diagnose the <span class="hlt">faults</span> to an accuracy of more than 91%. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..327b2067K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..327b2067K"><span>Product quality management based on CNC machine <span class="hlt">fault</span> prognostics and <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kozlov, A. M.; Al-jonid, Kh M.; Kozlov, A. A.; Antar, Sh D.</p> <p>2018-03-01</p> <p>This paper presents a new <span class="hlt">fault</span> classification model and an integrated approach to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> which involves the combination of ideas of Neuro-fuzzy Networks (NF), Dynamic Bayesian Networks (DBN) and Particle Filtering (PF) algorithm on a single platform. In the new model, <span class="hlt">faults</span> are categorized in two aspects, namely first and second degree <span class="hlt">faults</span>. First degree <span class="hlt">faults</span> are instantaneous in nature, and second degree <span class="hlt">faults</span> are evolutional and appear as a developing phenomenon which starts from the initial stage, goes through the development stage and finally ends at the mature stage. These categories of <span class="hlt">faults</span> have a lifetime which is inversely proportional to a machine tool's life according to the modified version of Taylor’s equation. For <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, this framework consists of two phases: the first one is focusing on <span class="hlt">fault</span> prognosis, which is done online, and the second one is concerned with <span class="hlt">fault</span> <span class="hlt">diagnosis</span> which depends on both off-line and on-line modules. In the first phase, a neuro-fuzzy predictor is used to take a decision on whether to embark Conditional Based Maintenance (CBM) or <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on the severity of a <span class="hlt">fault</span>. The second phase only comes into action when an evolving <span class="hlt">fault</span> goes beyond a critical threshold limit called a CBM limit for a command to be issued for <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. During this phase, DBN and PF techniques are used as an intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system to determine the severity, time and location of the <span class="hlt">fault</span>. The feasibility of this approach was tested in a simulation environment using the CNC machine as a case study and the results were studied and analyzed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19930002213','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19930002213"><span>Implementation of a model based <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> for actuation <span class="hlt">faults</span> of the Space Shuttle main engine</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.</p> <p>1992-01-01</p> <p>In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> system for actuation <span class="hlt">faults</span> of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> scheme for the real time actuation <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The <span class="hlt">diagnosis</span> system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007MSSP...21.2560W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007MSSP...21.2560W"><span>Support vector machine in machine condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Widodo, Achmad; Yang, Bo-Suk</p> <p>2007-08-01</p> <p>Recently, the issue of machine condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and <span class="hlt">diagnosis</span>. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and <span class="hlt">diagnosis</span>. Until 2006, the use of SVM in machine condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using SVM will be future works.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890013838','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890013838"><span>Expert systems for real-time monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Edwards, S. J.; Caglayan, A. K.</p> <p>1989-01-01</p> <p>Methods for building real-time onboard expert systems were investigated, and the use of expert systems technology was demonstrated in improving the performance of current real-time onboard monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> applications. The potential applications of the proposed research include an expert system environment allowing the integration of expert systems into conventional time-critical application solutions, a grammar for describing the discrete event behavior of monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> systems, and their applications to new real-time hardware <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and monitoring systems for aircraft.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...92..173M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...92..173M"><span>Application of an improved maximum correlated kurtosis deconvolution method for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miao, Yonghao; Zhao, Ming; Lin, Jing; Lei, Yaguo</p> <p>2017-08-01</p> <p>The extraction of periodic impulses, which are the important indicators of rolling bearing <span class="hlt">faults</span>, from <span class="hlt">vibration</span> signals is considerably significance for <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Maximum correlated kurtosis deconvolution (MCKD) developed from minimum entropy deconvolution (MED) has been proven as an efficient tool for enhancing the periodic impulses in the <span class="hlt">diagnosis</span> of rolling element bearings and gearboxes. However, challenges still exist when MCKD is applied to the bearings operating under harsh working conditions. The difficulties mainly come from the rigorous requires for the multi-input parameters and the complicated resampling process. To overcome these limitations, an improved MCKD (IMCKD) is presented in this paper. The new method estimates the iterative period by calculating the autocorrelation of the envelope signal rather than relies on the provided prior period. Moreover, the iterative period will gradually approach to the true <span class="hlt">fault</span> period through updating the iterative period after every iterative step. Since IMCKD is unaffected by the impulse signals with the high kurtosis value, the new method selects the maximum kurtosis filtered signal as the final choice from all candidates in the assigned iterative counts. Compared with MCKD, IMCKD has three advantages. First, without considering prior period and the choice of the order of shift, IMCKD is more efficient and has higher robustness. Second, the resampling process is not necessary for IMCKD, which is greatly convenient for the subsequent frequency spectrum analysis and envelope spectrum analysis without resetting the sampling rate. Third, IMCKD has a significant performance advantage in diagnosing the bearing compound-<span class="hlt">fault</span> which expands the application range. Finally, the effectiveness and superiority of IMCKD are validated by a number of simulated bearing <span class="hlt">fault</span> signals and applying to compound <span class="hlt">faults</span> and single <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of a locomotive bearing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..107...29Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..107...29Z"><span>Bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time-frequency atom dictionary</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Xin; Liu, Zhiwen; Miao, Qiang; Wang, Lei</p> <p>2018-07-01</p> <p>Condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings are significant to guarantee the reliability and functionality of a mechanical system, production efficiency, and plant safety. However, this is almost invariably a formidable challenge because the <span class="hlt">fault</span> features are often buried by strong background noises and other unstable interference components. To satisfactorily extract the bearing <span class="hlt">fault</span> features, a whale optimization algorithm (WOA)-optimized orthogonal matching pursuit (OMP) with a combined time-frequency atom dictionary is proposed in this paper. Firstly, a combined time-frequency atom dictionary whose atom is a combination of Fourier dictionary atom and impact time-frequency dictionary atom is designed according to the properties of bearing <span class="hlt">fault</span> <span class="hlt">vibration</span> signal. Furthermore, to improve the efficiency and accuracy of signal sparse representation, the WOA is introduced into the OMP algorithm to optimize the atom parameters for best approximating the original signal with the dictionary atoms. The proposed method is validated through analyzing the bearing <span class="hlt">fault</span> simulation signal and the real <span class="hlt">vibration</span> signals collected from an experimental bearing and a wheelset bearing of high-speed trains. The comparisons with the respect to the state of the art in the field are illustrated in detail, which highlight the advantages of the proposed method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017E%26ES...64a2099W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017E%26ES...64a2099W"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of power transformer based on <span class="hlt">fault</span>-tree analysis (FTA)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Yongliang; Li, Xiaoqiang; Ma, Jianwei; Li, SuoYu</p> <p>2017-05-01</p> <p>Power transformers is an important equipment in power plants and substations, power distribution transmission link is made an important hub of power systems. Its performance directly affects the quality and health of the power system reliability and stability. This paper summarizes the five parts according to the <span class="hlt">fault</span> type power transformers, then from the time dimension divided into three stages of power transformer <span class="hlt">fault</span>, use DGA routine analysis and infrared diagnostics criterion set power transformer running state, finally, according to the needs of power transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, by the general to the section by stepwise refinement of dendritic tree constructed power transformer <span class="hlt">fault</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4701258','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4701258"><span>A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Hilbert Huang Transform</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li</p> <p>2015-01-01</p> <p>Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their <span class="hlt">fault</span> <span class="hlt">diagnosis</span> has attracted considerable research attention. Established <span class="hlt">fault</span> feature extraction methods focus on statistical characteristics of the <span class="hlt">vibration</span> signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of <span class="hlt">vibration</span> signals by Hilbert–Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the <span class="hlt">fault</span> classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, <span class="hlt">fault</span> classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs <span class="hlt">fault</span> classification accuracy</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26540059','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26540059"><span>A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Hilbert Huang Transform.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li</p> <p>2015-11-03</p> <p>Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their <span class="hlt">fault</span> <span class="hlt">diagnosis</span> has attracted considerable research attention. Established <span class="hlt">fault</span> feature extraction methods focus on statistical characteristics of the <span class="hlt">vibration</span> signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of <span class="hlt">vibration</span> signals by Hilbert-Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the <span class="hlt">fault</span> classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, <span class="hlt">fault</span> classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500-800 and a m range of 50-300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs <span class="hlt">fault</span> classification accuracy and a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...92..213P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...92..213P"><span>Two methods for modeling <span class="hlt">vibrations</span> of planetary gearboxes including <span class="hlt">faults</span>: Comparison and validation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Parra, J.; Vicuña, Cristián Molina</p> <p>2017-08-01</p> <p>Planetary gearboxes are important components of many industrial applications. <span class="hlt">Vibration</span> analysis can increase their lifetime and prevent expensive repair and safety concerns. However, an effective analysis is only possible if the <span class="hlt">vibration</span> features of planetary gearboxes are properly understood. In this paper, models are used to study the frequency content of planetary gearbox <span class="hlt">vibrations</span> under non-<span class="hlt">fault</span> and different <span class="hlt">fault</span> conditions. Two different models are considered: phenomenological model, which is an analytical-mathematical formulation based on observation, and lumped-parameter model, which is based on the solution of the equations of motion of the system. Results of both models are not directly comparable, because the phenomenological model provides the <span class="hlt">vibration</span> on a fixed radial direction, such as the measurements of the <span class="hlt">vibration</span> sensor mounted on the outer part of the ring gear. On the other hand, the lumped-parameter model provides the <span class="hlt">vibrations</span> on the basis of a rotating reference frame fixed to the carrier. To overcome this situation, a function to decompose the lumped-parameter model solutions to a fixed reference frame is presented. Finally, comparisons of results from both model perspectives and experimental measurements are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19850006200','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19850006200"><span>The role of knowledge structures in <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Smith, P. J.; Giffin, W. C.; Rockwell, T. H.; Thomas, M. E.</p> <p>1984-01-01</p> <p>The use of human memory and knowledge structures to direct <span class="hlt">fault</span> <span class="hlt">diagnosis</span> performance was investigated. The performances of 20 pilots with instrument flight ratings were studied in a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> task. The pilots were read a scenario which described flight conditions under which the symptoms which are indicative of a problem were detected. They were asked to think out loud as they requested and interpreted various pieces of information to diagnose the cause of the problem. Only 11 of the 20 pilots successfully diagnosed the problem. Pilot performance on this <span class="hlt">fault</span> <span class="hlt">diagnosis</span> task was modeled in the use of domain specific knowledge organized in a frame system. Eighteen frames, with a common structure, were necessary to account for the data from all twenty subjects.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19920001799','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19920001799"><span>Real-time <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for propulsion systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Merrill, Walter C.; Guo, Ten-Huei; Delaat, John C.; Duyar, Ahmet</p> <p>1991-01-01</p> <p>Current research toward real time <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for propulsion systems at NASA-Lewis is described. The research is being applied to both air breathing and rocket propulsion systems. Topics include <span class="hlt">fault</span> detection methods including neural networks, system modeling, and real time implementations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...80..349Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...80..349Z"><span>Kurtosis based weighted sparse model with convex optimization technique for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yan, Ruqiang</p> <p>2016-12-01</p> <p>The bearing failure, generating harmful <span class="hlt">vibrations</span>, is one of the most frequent reasons for machine breakdowns. Thus, performing bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is an essential procedure to improve the reliability of the mechanical system and reduce its operating expenses. Most of the previous studies focused on rolling bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> could be categorized into two main families, kurtosis-based filter method and wavelet-based shrinkage method. Although tremendous progresses have been made, their effectiveness suffers from three potential drawbacks: firstly, <span class="hlt">fault</span> information is often decomposed into proximal frequency bands and results in impulsive feature frequency band splitting (IFFBS) phenomenon, which significantly degrades the performance of capturing the optimal information band; secondly, noise energy spreads throughout all frequency bins and contaminates <span class="hlt">fault</span> information in the information band, especially under the heavy noisy circumstance; thirdly, wavelet coefficients are shrunk equally to satisfy the sparsity constraints and most of the feature information energy are thus eliminated unreasonably. Therefore, exploiting two pieces of prior information (i.e., one is that the coefficient sequences of <span class="hlt">fault</span> information in the wavelet basis is sparse, and the other is that the kurtosis of the envelope spectrum could evaluate accurately the information capacity of rolling bearing <span class="hlt">faults</span>), a novel weighted sparse model and its corresponding framework for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is proposed in this paper, coined KurWSD. KurWSD formulates the prior information into weighted sparse regularization terms and then obtains a nonsmooth convex optimization problem. The alternating direction method of multipliers (ADMM) is sequentially employed to solve this problem and the <span class="hlt">fault</span> information is extracted through the estimated wavelet coefficients. Compared with state-of-the-art methods, KurWSD overcomes the three drawbacks and utilizes the advantages of both family</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JSV...408..190F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JSV...408..190F"><span>A phase angle based diagnostic scheme to planetary gear <span class="hlt">faults</span> diagnostics under non-stationary operational conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Feng, Ke; Wang, Kesheng; Ni, Qing; Zuo, Ming J.; Wei, Dongdong</p> <p>2017-11-01</p> <p>Planetary gearbox is a critical component for rotating machinery. It is widely used in wind turbines, aerospace and transmission systems in heavy industry. Thus, it is important to monitor planetary gearboxes, especially for <span class="hlt">fault</span> diagnostics, during its operational conditions. However, in practice, operational conditions of planetary gearbox are often characterized by variations of rotational speeds and loads, which may bring difficulties for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> through the measured <span class="hlt">vibrations</span>. In this paper, phase angle data extracted from measured planetary gearbox <span class="hlt">vibrations</span> is used for <span class="hlt">fault</span> detection under non-stationary operational conditions. Together with sample entropy, <span class="hlt">fault</span> <span class="hlt">diagnosis</span> on planetary gearbox is implemented. The proposed scheme is explained and demonstrated in both simulation and experimental studies. The scheme proves to be effective and features advantages on <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of planetary gearboxes under non-stationary operational conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22346592','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22346592"><span>Study and application of acoustic emission testing in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of low-speed heavy-duty gears.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gao, Lixin; Zai, Fenlou; Su, Shanbin; Wang, Huaqing; Chen, Peng; Liu, Limei</p> <p>2011-01-01</p> <p>Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the <span class="hlt">faults</span> of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of on-spot low-speed heavy-duty gears and could be a significant supplement to <span class="hlt">vibration</span> testing <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3274124','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3274124"><span>Study and Application of Acoustic Emission Testing in <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Low-Speed Heavy-Duty Gears</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Gao, Lixin; Zai, Fenlou; Su, Shanbin; Wang, Huaqing; Chen, Peng; Liu, Limei</p> <p>2011-01-01</p> <p>Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the <span class="hlt">faults</span> of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of on-spot low-speed heavy-duty gears and could be a significant supplement to <span class="hlt">vibration</span> testing <span class="hlt">diagnosis</span>. PMID:22346592</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19910066604&hterms=tree+identification&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dtree%2Bidentification','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19910066604&hterms=tree+identification&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dtree%2Bidentification"><span>Object-oriented <span class="hlt">fault</span> tree models applied to system <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Iverson, David L.; Patterson-Hine, F. A.</p> <p>1990-01-01</p> <p>When a <span class="hlt">diagnosis</span> system is used in a dynamic environment, such as the distributed computer system planned for use on Space Station Freedom, it must execute quickly and its knowledge base must be easily updated. Representing system knowledge as object-oriented augmented <span class="hlt">fault</span> trees provides both features. The <span class="hlt">diagnosis</span> system described here is based on the failure cause identification process of the diagnostic system described by Narayanan and Viswanadham. Their system has been enhanced in this implementation by replacing the knowledge base of if-then rules with an object-oriented <span class="hlt">fault</span> tree representation. This allows the system to perform its task much faster and facilitates dynamic updating of the knowledge base in a changing <span class="hlt">diagnosis</span> environment. Accessing the information contained in the objects is more efficient than performing a lookup operation on an indexed rule base. Additionally, the object-oriented <span class="hlt">fault</span> trees can be easily updated to represent current system status. This paper describes the <span class="hlt">fault</span> tree representation, the <span class="hlt">diagnosis</span> algorithm extensions, and an example application of this system. Comparisons are made between the object-oriented <span class="hlt">fault</span> tree knowledge structure solution and one implementation of a rule-based solution. Plans for future work on this system are also discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013RScI...84b5107S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013RScI...84b5107S"><span>Multi-<span class="hlt">fault</span> clustering and <span class="hlt">diagnosis</span> of gear system mined by spectrum entropy clustering based on higher order cumulants</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei</p> <p>2013-02-01</p> <p>Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and <span class="hlt">diagnosis</span> method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and <span class="hlt">diagnosis</span> of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-<span class="hlt">fault</span> is extracted. Adopting a data-mining method of SEC conducts an analysis and <span class="hlt">diagnosis</span> at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-<span class="hlt">fault</span>, short crack-<span class="hlt">fault</span> in tooth root, long crack-<span class="hlt">fault</span> in tooth root, short crack-<span class="hlt">fault</span> in pitch circle, long crack-<span class="hlt">fault</span> in pitch circle, and wear-<span class="hlt">fault</span> on tooth. Research shows that this combined method of detection and <span class="hlt">diagnosis</span> can also identify the degree of damage of some <span class="hlt">faults</span>. On this basis, the virtual instrument of the gear system which detects damage and diagnoses <span class="hlt">faults</span> is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing <span class="hlt">vibration</span> signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23464251','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23464251"><span>Multi-<span class="hlt">fault</span> clustering and <span class="hlt">diagnosis</span> of gear system mined by spectrum entropy clustering based on higher order cumulants.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei</p> <p>2013-02-01</p> <p>Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and <span class="hlt">diagnosis</span> method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and <span class="hlt">diagnosis</span> of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-<span class="hlt">fault</span> is extracted. Adopting a data-mining method of SEC conducts an analysis and <span class="hlt">diagnosis</span> at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-<span class="hlt">fault</span>, short crack-<span class="hlt">fault</span> in tooth root, long crack-<span class="hlt">fault</span> in tooth root, short crack-<span class="hlt">fault</span> in pitch circle, long crack-<span class="hlt">fault</span> in pitch circle, and wear-<span class="hlt">fault</span> on tooth. Research shows that this combined method of detection and <span class="hlt">diagnosis</span> can also identify the degree of damage of some <span class="hlt">faults</span>. On this basis, the virtual instrument of the gear system which detects damage and diagnoses <span class="hlt">faults</span> is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing <span class="hlt">vibration</span> signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950020952','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950020952"><span><span class="hlt">Diagnosis</span> of helicopter gearboxes using structure-based networks</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.</p> <p>1995-01-01</p> <p>A connectionist network is introduced for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of helicopter gearboxes that incorporates knowledge of the gearbox structure and characteristics of the <span class="hlt">vibration</span> features as its fuzzy weights. <span class="hlt">Diagnosis</span> is performed by propagating the abnormal features of <span class="hlt">vibration</span> measurements through this Structure-Based Connectionist Network (SBCN), the outputs of which represent the <span class="hlt">fault</span> possibility values for individual components of the gearbox. The performance of this network is evaluated by applying it to experimental <span class="hlt">vibration</span> data from an OH-58A helicopter gearbox. The diagnostic results indicate that the network performance is comparable to those obtained from supervised pattern classification.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JSV...401..139L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JSV...401..139L"><span>Rolling bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Jimeng; Li, Ming; Zhang, Jinfeng</p> <p>2017-08-01</p> <p>Rolling bearings are the key components in the modern machinery, and tough operation environments often make them prone to failure. However, due to the influence of the transmission path and background noise, the useful feature information relevant to the bearing <span class="hlt">fault</span> contained in the <span class="hlt">vibration</span> signals is weak, which makes it difficult to identify the <span class="hlt">fault</span> symptom of rolling bearings in time. Therefore, the paper proposes a novel weak signal detection method based on time-delayed feedback monostable stochastic resonance (TFMSR) system and adaptive minimum entropy deconvolution (MED) to realize the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling bearings. The MED method is employed to preprocess the <span class="hlt">vibration</span> signals, which can deconvolve the effect of transmission path and clarify the defect-induced impulses. And a modified power spectrum kurtosis (MPSK) index is constructed to realize the adaptive selection of filter length in the MED algorithm. By introducing the time-delayed feedback item in to an over-damped monostable system, the TFMSR method can effectively utilize the historical information of input signal to enhance the periodicity of SR output, which is beneficial to the detection of periodic signal. Furthermore, the influence of time delay and feedback intensity on the SR phenomenon is analyzed, and by selecting appropriate time delay, feedback intensity and re-scaling ratio with genetic algorithm, the SR can be produced to realize the resonance detection of weak signal. The combination of the adaptive MED (AMED) method and TFMSR method is conducive to extracting the feature information from strong background noise and realizing the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling bearings. Finally, some experiments and engineering application are performed to evaluate the effectiveness of the proposed AMED-TFMSR method in comparison with a traditional bistable SR method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MeScT..28d5401G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MeScT..28d5401G"><span>Application of optimized multiscale mathematical morphology for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gong, Tingkai; Yuan, Yanbin; Yuan, Xiaohui; Wu, Xiaotao</p> <p>2017-04-01</p> <p>In order to suppress noise effectively and extract the impulsive features in the <span class="hlt">vibration</span> signals of faulty rolling element bearings, an optimized multiscale morphology (OMM) based on conventional multiscale morphology (CMM) and iterative morphology (IM) is presented in this paper. Firstly, the operator used in the IM method must be non-idempotent; therefore, an optimized difference (ODIF) operator has been designed. Furthermore, in the iterative process the current operation is performed on the basis of the previous one. This means that if a larger scale is employed, more <span class="hlt">fault</span> features are inhibited. Thereby, a unit scale is proposed as the structuring element (SE) scale in IM. According to the above definitions, the IM method is implemented on the results over different scales obtained by CMM. The validity of the proposed method is first evaluated by a simulated signal. Subsequently, aimed at an outer race <span class="hlt">fault</span> two <span class="hlt">vibration</span> signals sampled by different accelerometers are analyzed by OMM and CMM, respectively. The same is done for an inner race <span class="hlt">fault</span>. The results show that the optimized method is effective in diagnosing the two bearing <span class="hlt">faults</span>. Compared with the CMM method, the OMM method can extract much more <span class="hlt">fault</span> features under strong noise background.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3231250','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3231250"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> for Micro-Gas Turbine Engine Sensors via Wavelet Entropy</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yu, Bing; Liu, Dongdong; Zhang, Tianhong</p> <p>2011-01-01</p> <p>Sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can’t be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> can be performed by extracting features of the measured signals. This paper proposes a novel <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of <span class="hlt">faults</span> with different magnitudes are presented. The experimental results show that the proposed method for sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is efficient. PMID:22163734</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22163734','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22163734"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> for micro-gas turbine engine sensors via wavelet entropy.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yu, Bing; Liu, Dongdong; Zhang, Tianhong</p> <p>2011-01-01</p> <p>Sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can't be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> can be performed by extracting features of the measured signals. This paper proposes a novel <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of <span class="hlt">faults</span> with different magnitudes are presented. The experimental results show that the proposed method for sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is efficient.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015MSSP...62...30W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015MSSP...62...30W"><span>Bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under unknown variable speed via gear noise cancellation and rotational order sideband identification</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Tianyang; Liang, Ming; Li, Jianyong; Cheng, Weidong; Li, Chuan</p> <p>2015-10-01</p> <p>The interfering <span class="hlt">vibration</span> signals of a gearbox often represent a challenging issue in rolling bearing <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span>, particularly under unknown variable rotational speed conditions. Though some methods have been proposed to remove the gearbox interfering signals based on their discrete frequency nature, such methods may not work well under unknown variable speed conditions. As such, we propose a new approach to address this issue. The new approach consists of three main steps: (a) adaptive gear interference removal, (b) <span class="hlt">fault</span> characteristic order (FCO) based <span class="hlt">fault</span> detection, and (c) rotational-order-sideband (ROS) based <span class="hlt">fault</span> type identification. For gear interference removal, an enhanced adaptive noise cancellation (ANC) algorithm has been developed in this study. The new ANC algorithm does not require an additional accelerometer to provide reference input. Instead, the reference signal is adaptively constructed from signal maxima and instantaneous dominant meshing multiple (IDMM) trend. Key ANC parameters such as filter length and step size have also been tailored to suit the variable speed conditions, The main advantage of using ROS for <span class="hlt">fault</span> type <span class="hlt">diagnosis</span> is that it is insusceptible to confusion caused by the co-existence of bearing and gear rotational frequency peaks in the identification of the bearing <span class="hlt">fault</span> characteristic frequency in the FCO sub-order region. The effectiveness of the proposed method has been demonstrated using both simulation and experimental data. Our experimental study also indicates that the proposed method is applicable regardless whether the bearing and gear rotational speeds are proportional to each other or not.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948558','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948558"><span>Data Decomposition Techniques with Multi-Scale Permutation Entropy Calculations for Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yasir, Muhammad Naveed; Koh, Bong-Hwan</p> <p>2018-01-01</p> <p>This paper presents the local mean decomposition (LMD) integrated with multi-scale permutation entropy (MPE), also known as LMD-MPE, to investigate the rolling element bearing (REB) <span class="hlt">fault</span> <span class="hlt">diagnosis</span> from measured <span class="hlt">vibration</span> signals. First, the LMD decomposed the <span class="hlt">vibration</span> data or acceleration measurement into separate product functions that are composed of both amplitude and frequency modulation. MPE then calculated the statistical permutation entropy from the product functions to extract the nonlinear features to assess and classify the condition of the healthy and damaged REB system. The comparative experimental results of the conventional LMD-based multi-scale entropy and MPE were presented to verify the authenticity of the proposed technique. The study found that LMD-MPE’s integrated approach provides reliable, damage-sensitive features when analyzing the bearing condition. The results of REB experimental datasets show that the proposed approach yields more vigorous outcomes than existing methods. PMID:29690526</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29690526','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29690526"><span>Data Decomposition Techniques with Multi-Scale Permutation Entropy Calculations for Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yasir, Muhammad Naveed; Koh, Bong-Hwan</p> <p>2018-04-21</p> <p>This paper presents the local mean decomposition (LMD) integrated with multi-scale permutation entropy (MPE), also known as LMD-MPE, to investigate the rolling element bearing (REB) <span class="hlt">fault</span> <span class="hlt">diagnosis</span> from measured <span class="hlt">vibration</span> signals. First, the LMD decomposed the <span class="hlt">vibration</span> data or acceleration measurement into separate product functions that are composed of both amplitude and frequency modulation. MPE then calculated the statistical permutation entropy from the product functions to extract the nonlinear features to assess and classify the condition of the healthy and damaged REB system. The comparative experimental results of the conventional LMD-based multi-scale entropy and MPE were presented to verify the authenticity of the proposed technique. The study found that LMD-MPE’s integrated approach provides reliable, damage-sensitive features when analyzing the bearing condition. The results of REB experimental datasets show that the proposed approach yields more vigorous outcomes than existing methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4732146','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4732146"><span>Modeling Sensor Reliability in <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Evidence Theory</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong</p> <p>2016-01-01</p> <p>Sensor data fusion plays an important role in <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Dempster–Shafer (D-R) evidence theory is widely used in <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> due to the fact that the information volume of each sensor report is taken into consideration. An application in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> from 81.19% to 89.48% compared to the existing methods. PMID:26797611</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016SMaS...25j5019D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016SMaS...25j5019D"><span>Distributed adaptive <span class="hlt">diagnosis</span> of sensor <span class="hlt">faults</span> using structural response data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dragos, Kosmas; Smarsly, Kay</p> <p>2016-10-01</p> <p>The reliability and consistency of wireless structural health monitoring (SHM) systems can be compromised by sensor <span class="hlt">faults</span>, leading to miscalibrations, corrupted data, or even data loss. Several research approaches towards <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, referred to as ‘analytical redundancy’, have been proposed that analyze the correlations between different sensor outputs. In wireless SHM, most analytical redundancy approaches require centralized data storage on a server for data analysis, while other approaches exploit the on-board computing capabilities of wireless sensor nodes, analyzing the raw sensor data directly on board. However, using raw sensor data poses an operational constraint due to the limited power resources of wireless sensor nodes. In this paper, a new distributed autonomous approach towards sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on processed structural response data is presented. The inherent correlations among Fourier amplitudes of acceleration response data, at peaks corresponding to the eigenfrequencies of the structure, are used for <span class="hlt">diagnosis</span> of abnormal sensor outputs at a given structural condition. Representing an entirely data-driven analytical redundancy approach that does not require any a priori knowledge of the monitored structure or of the SHM system, artificial neural networks (ANN) are embedded into the sensor nodes enabling cooperative <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in a fully decentralized manner. The distributed analytical redundancy approach is implemented into a wireless SHM system and validated in laboratory experiments, demonstrating the ability of wireless sensor nodes to self-diagnose sensor <span class="hlt">faults</span> accurately and efficiently with minimal data traffic. Besides enabling distributed autonomous <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, the embedded ANNs are able to adapt to the actual condition of the structure, thus ensuring accurate and efficient <span class="hlt">fault</span> <span class="hlt">diagnosis</span> even in case of structural changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AIPC.1834c0012Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AIPC.1834c0012Y"><span>Application of dynamic uncertain causality graph in spacecraft <span class="hlt">fault</span> <span class="hlt">diagnosis</span>: Logic cycle</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yao, Quanying; Zhang, Qin; Liu, Peng; Yang, Ping; Zhu, Ma; Wang, Xiaochen</p> <p>2017-04-01</p> <p>Intelligent <span class="hlt">diagnosis</span> system are applied to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in spacecraft. Dynamic Uncertain Causality Graph (DUCG) is a new probability graphic model with many advantages. In the knowledge expression of spacecraft <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, feedback among variables is frequently encountered, which may cause directed cyclic graphs (DCGs). Probabilistic graphical models (PGMs) such as bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning, but BN does not allow DCGs. In this paper, DUGG is applied to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in spacecraft: introducing the inference algorithm for the DUCG to deal with feedback. Now, DUCG has been tested in 16 typical <span class="hlt">faults</span> with 100% <span class="hlt">diagnosis</span> accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29195375','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29195375"><span>Sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of aero-engine based on divided flight status.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu</p> <p>2017-11-01</p> <p><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is presented to improve <span class="hlt">fault</span> <span class="hlt">diagnosis</span> precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a <span class="hlt">diagnosis</span> model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built <span class="hlt">diagnosis</span> models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017RScI...88k5007Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017RScI...88k5007Z"><span>Sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of aero-engine based on divided flight status</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu</p> <p>2017-11-01</p> <p><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is presented to improve <span class="hlt">fault</span> <span class="hlt">diagnosis</span> precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a <span class="hlt">diagnosis</span> model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built <span class="hlt">diagnosis</span> models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28282936','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28282936"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong</p> <p>2017-03-09</p> <p>Intelligent condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> by analyzing the sensor data can assure the safety of machinery. Conventional <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make <span class="hlt">diagnosis</span>. However, these conventional <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, <span class="hlt">fault</span> <span class="hlt">diagnosis</span> considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing <span class="hlt">faults</span> can get 100%. The proposed <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approach is effective in recognizing the type of bearing <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5375835','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5375835"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong</p> <p>2017-01-01</p> <p>Intelligent condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> by analyzing the sensor data can assure the safety of machinery. Conventional <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make <span class="hlt">diagnosis</span>. However, these conventional <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, <span class="hlt">fault</span> <span class="hlt">diagnosis</span> considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing <span class="hlt">faults</span> can get 100%. The proposed <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approach is effective in recognizing the type of bearing <span class="hlt">faults</span>. PMID:28282936</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JSV...370..394Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JSV...370..394Z"><span>EEMD-based multiscale ICA method for slewing bearing <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Žvokelj, Matej; Zupan, Samo; Prebil, Ivan</p> <p>2016-05-01</p> <p>A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing <span class="hlt">fault</span> detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real <span class="hlt">vibration</span> and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and <span class="hlt">vibration</span> signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> strategy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ChJME..30.1357W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ChJME..30.1357W"><span>Motor <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Short-time Fourier Transform and Convolutional Neural Network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong</p> <p>2017-11-01</p> <p>With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The <span class="hlt">vibration</span> signals of different <span class="hlt">fault</span> motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional <span class="hlt">diagnosis</span> methods. This paper proposes a new method, based on STFT and CNN, which can complete motor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> tasks more intelligently and accurately.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19920014122','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19920014122"><span>Intelligent <span class="hlt">fault</span> isolation and <span class="hlt">diagnosis</span> for communication satellite systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tallo, Donald P.; Durkin, John; Petrik, Edward J.</p> <p>1992-01-01</p> <p>Discussed here is a prototype <span class="hlt">diagnosis</span> expert system to provide the Advanced Communication Technology Satellite (ACTS) System with autonomous <span class="hlt">diagnosis</span> capability. The system, the <span class="hlt">Fault</span> Isolation and <span class="hlt">Diagnosis</span> EXpert (FIDEX) system, is a frame-based system that uses hierarchical structures to represent such items as the satellite's subsystems, components, sensors, and <span class="hlt">fault</span> states. This overall frame architecture integrates the hierarchical structures into a lattice that provides a flexible representation scheme and facilitates system maintenance. FIDEX uses an inexact reasoning technique based on the incrementally acquired evidence approach developed by Shortliffe. The system is designed with a primitive learning ability through which it maintains a record of past <span class="hlt">diagnosis</span> studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25760051','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25760051"><span>An SVM-based solution for <span class="hlt">fault</span> detection in wind turbines.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Santos, Pedro; Villa, Luisa F; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús</p> <p>2015-03-09</p> <p>Research into <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the <span class="hlt">diagnosis</span> of mechanical <span class="hlt">faults</span> in their mechanical transmission chain is insufficient. A successful <span class="hlt">diagnosis</span> requires the inclusion of accelerometers to evaluate <span class="hlt">vibrations</span>. This work presents a multi-sensory system for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which <span class="hlt">vibration</span> signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two <span class="hlt">fault</span> typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4435112','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4435112"><span>An SVM-Based Solution for <span class="hlt">Fault</span> Detection in Wind Turbines</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Santos, Pedro; Villa, Luisa F.; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús</p> <p>2015-01-01</p> <p>Research into <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the <span class="hlt">diagnosis</span> of mechanical <span class="hlt">faults</span> in their mechanical transmission chain is insufficient. A successful <span class="hlt">diagnosis</span> requires the inclusion of accelerometers to evaluate <span class="hlt">vibrations</span>. This work presents a multi-sensory system for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which <span class="hlt">vibration</span> signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two <span class="hlt">fault</span> typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets. PMID:25760051</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..322g2043H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..322g2043H"><span>Gear <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on BP Neural Network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huang, Yongsheng; Huang, Ruoshi</p> <p>2018-03-01</p> <p>Gear transmission is more complex, widely used in machinery fields, which form of <span class="hlt">fault</span> has some nonlinear characteristics. This paper uses BP neural network to train the gear of four typical failure modes, and achieves satisfactory results. Tested by using test data, test results have an agreement with the actual results. The results show that the BP neural network can effectively solve the complex state of gear <span class="hlt">fault</span> in the gear <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4146359','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4146359"><span>Satellite <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Using Support Vector Machines Based on a Hybrid Voting Mechanism</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang</p> <p>2014-01-01</p> <p>The satellite <span class="hlt">fault</span> <span class="hlt">diagnosis</span> has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple <span class="hlt">faults</span> makes a challenge to the satellite <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The interactions between parameters and misclassifications from multiple <span class="hlt">faults</span> will increase the false alarm rate and the false negative rate. On the other hand, for each satellite <span class="hlt">fault</span>, there is not enough <span class="hlt">fault</span> data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple <span class="hlt">faults</span>, and small samples. Many experimental results show that the accuracy of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using HVM-SVM is improved. PMID:25215324</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...80..533P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...80..533P"><span>Data-driven mono-component feature identification via modified nonlocal means and MEWT for mechanical drivetrain <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pan, Jun; Chen, Jinglong; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia</p> <p>2016-12-01</p> <p>It is significant to perform condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> on rolling mills in steel-making plant to ensure economic benefit. However, timely <span class="hlt">fault</span> identification of key parts in a complicated industrial system under operating condition is still a challenging task since acquired condition signals are usually multi-modulated and inevitably mixed with strong noise. Therefore, a new data-driven mono-component identification method is proposed in this paper for diagnostic purpose. First, the modified nonlocal means algorithm (NLmeans) is proposed to reduce noise in <span class="hlt">vibration</span> signals without destroying its original Fourier spectrum structure. During the modified NLmeans, two modifications are investigated and performed to improve denoising effect. Then, the modified empirical wavelet transform (MEWT) is applied on the de-noised signal to adaptively extract empirical mono-component modes. Finally, the modes are analyzed for mechanical <span class="hlt">fault</span> identification based on Hilbert transform. The results show that the proposed data-driven method owns superior performance during system operation compared with the MEWT method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29899242','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29899242"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Method for a Mine Hoist in the Internet of Things Environment.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Juanli; Xie, Jiacheng; Yang, Zhaojian; Li, Junjie</p> <p>2018-06-13</p> <p>To reduce the difficulty of acquiring and transmitting data in mining hoist <span class="hlt">fault</span> <span class="hlt">diagnosis</span> systems and to mitigate the low efficiency and unreasonable reasoning process problems, a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for mine hoisting equipment based on the Internet of Things (IoT) is proposed in this study. The IoT requires three basic architectural layers: a perception layer, network layer, and application layer. In the perception layer, we designed a collaborative acquisition system based on the ZigBee short distance wireless communication technology for key components of the mine hoisting equipment. Real-time data acquisition was achieved, and a network layer was created by using long-distance wireless General Packet Radio Service (GPRS) transmission. The transmission and reception platforms for remote data transmission were able to transmit data in real time. A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> reasoning method is proposed based on the improved Dezert-Smarandache Theory (DSmT) evidence theory, and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> reasoning is performed. Based on interactive technology, a humanized and visualized <span class="hlt">fault</span> <span class="hlt">diagnosis</span> platform is created in the application layer. The method is then verified. A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> test of the mine hoisting mechanism shows that the proposed <span class="hlt">diagnosis</span> method obtains complete diagnostic data, and the <span class="hlt">diagnosis</span> results have high accuracy and reliability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...94...14H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...94...14H"><span>A novel <span class="hlt">vibration</span>-based <span class="hlt">fault</span> diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hong, Liu; Qu, Yongzhi; Dhupia, Jaspreet Singh; Sheng, Shuangwen; Tan, Yuegang; Zhou, Zude</p> <p>2017-09-01</p> <p>The localized failures of gears introduce cyclic-transient impulses in the measured gearbox <span class="hlt">vibration</span> signals. These impulses are usually identified from the sidebands around gear-mesh harmonics through the spectral analysis of cyclo-stationary signals. However, in practice, several high-powered applications of gearboxes like wind turbines are intrinsically characterized by nonstationary processes that blur the measured <span class="hlt">vibration</span> spectra of a gearbox and deteriorate the efficacy of spectral diagnostic methods. Although order-tracking techniques have been proposed to improve the performance of spectral <span class="hlt">diagnosis</span> for nonstationary signals measured in such applications, the required hardware for the measurement of rotational speed of these machines is often unavailable in industrial settings. Moreover, existing tacho-less order-tracking approaches are usually limited by the high time-frequency resolution requirement, which is a prerequisite for the precise estimation of the instantaneous frequency. To address such issues, a novel <span class="hlt">fault</span>-signature enhancement algorithm is proposed that can alleviate the spectral smearing without the need of rotational speed measurement. This proposed tacho-less diagnostic technique resamples the measured acceleration signal of the gearbox based on the optimal warping path evaluated from the fast dynamic time-warping algorithm, which aligns a filtered shaft rotational harmonic signal with respect to a reference signal assuming a constant shaft rotational speed estimated from the approximation of operational speed. The effectiveness of this method is validated using both simulated signals from a fixed-axis gear pair under nonstationary conditions and experimental measurements from a 750-kW planetary wind turbine gearbox on a dynamometer test rig. The results demonstrate that the proposed algorithm can identify <span class="hlt">fault</span> information from typical gearbox <span class="hlt">vibration</span> measurements carried out in a resource-constrained industrial environment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19960002911','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19960002911"><span>A PC based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> expert system</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Marsh, Christopher A.</p> <p>1990-01-01</p> <p>The Integrated Status Assessment (ISA) prototype expert system performs system level <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using rules and models created by the user. The ISA evolved from concepts to a stand-alone demonstration prototype using OPS5 on a LISP Machine. The LISP based prototype was rewritten in C and the C Language Integrated Production System (CLIPS) to run on a Personal Computer (PC) and a graphics workstation. The ISA prototype has been used to demonstrate <span class="hlt">fault</span> <span class="hlt">diagnosis</span> functions of Space Station Freedom's Operation Management System (OMS). This paper describes the development of the ISA prototype from early concepts to the current PC/workstation version used today and describes future areas of development for the prototype.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...66..699R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...66..699R"><span>Combination of process and <span class="hlt">vibration</span> data for improved condition monitoring of industrial systems working under variable operating conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ruiz-Cárcel, C.; Jaramillo, V. H.; Mba, D.; Ottewill, J. R.; Cao, Y.</p> <p>2016-01-01</p> <p>The detection and <span class="hlt">diagnosis</span> of <span class="hlt">faults</span> in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using <span class="hlt">vibration</span> analysis or other specific techniques. Conventional <span class="hlt">vibration</span>-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements. In this paper, a technique based on merging process and <span class="hlt">vibration</span> data is proposed with the objective of improving the detection of mechanical <span class="hlt">faults</span> in industrial systems working under variable operating conditions. The capabilities of CVA for detection and <span class="hlt">diagnosis</span> of <span class="hlt">faults</span> were tested using experimental data acquired from a compressor test rig where different process <span class="hlt">faults</span> were introduced. Results suggest that the combination of process and <span class="hlt">vibration</span> data can effectively improve the detectability of mechanical <span class="hlt">faults</span> in systems working under variable operating conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19910016382','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19910016382"><span>A distributed <span class="hlt">fault</span>-detection and <span class="hlt">diagnosis</span> system using on-line parameter estimation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Guo, T.-H.; Merrill, W.; Duyar, A.</p> <p>1991-01-01</p> <p>The development of a model-based <span class="hlt">fault</span>-detection and <span class="hlt">diagnosis</span> system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the <span class="hlt">faults</span> of a system from comparison of the measurements of the system with a priori information represented by the model of the system. The method of modeling a complex system is described and a description of <span class="hlt">diagnosis</span> models which include process <span class="hlt">faults</span> is presented. There are three distinct classes of <span class="hlt">fault</span> modes covered by the system performance model equation: actuator <span class="hlt">faults</span>, sensor <span class="hlt">faults</span>, and performance degradation. A system equation for a complete model that describes all three classes of <span class="hlt">faults</span> is given. The strategy for detecting the <span class="hlt">fault</span> and estimating the <span class="hlt">fault</span> parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of <span class="hlt">faults</span>. The second step is the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> module which checks all the information obtained from the HTM level, isolates the <span class="hlt">fault</span>, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor <span class="hlt">faults</span> added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the <span class="hlt">faults</span> and estimate their magnitudes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3574731','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3574731"><span>Customized Multiwavelets for Planetary Gearbox <span class="hlt">Fault</span> Detection Based on <span class="hlt">Vibration</span> Sensor Signals</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue</p> <p>2013-01-01</p> <p>Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in <span class="hlt">vibration</span> signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make <span class="hlt">fault</span> features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate <span class="hlt">fault</span> feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The <span class="hlt">vibration</span> signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting <span class="hlt">faults</span> on two neighboring teeth in the planetary gearbox. PMID:23334609</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5191086','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5191086"><span>Integrated <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jeon, Namju; Lee, Hyeongcheol</p> <p>2016-01-01</p> <p>An integrated <span class="hlt">fault-diagnosis</span> algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level <span class="hlt">fault</span> diagnoses to improve the robustness and performance of the system. For the high-level <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the <span class="hlt">fault</span> of the drive motor system of each wheel. To diagnose the motor system for low-level <span class="hlt">faults</span>, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the <span class="hlt">fault</span> of the electric current and position sensors. The validity of the high-level <span class="hlt">fault-diagnosis</span> algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level <span class="hlt">fault</span> diagnoses, <span class="hlt">fault</span>-detection flags are defined. On the basis of this information, an integrated <span class="hlt">fault-diagnosis</span> strategy is proposed. PMID:27973431</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27973431','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27973431"><span>Integrated <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jeon, Namju; Lee, Hyeongcheol</p> <p>2016-12-12</p> <p>An integrated <span class="hlt">fault-diagnosis</span> algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level <span class="hlt">fault</span> diagnoses to improve the robustness and performance of the system. For the high-level <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the <span class="hlt">fault</span> of the drive motor system of each wheel. To diagnose the motor system for low-level <span class="hlt">faults</span>, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the <span class="hlt">fault</span> of the electric current and position sensors. The validity of the high-level <span class="hlt">fault-diagnosis</span> algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level <span class="hlt">fault</span> diagnoses, <span class="hlt">fault</span>-detection flags are defined. On the basis of this information, an integrated <span class="hlt">fault-diagnosis</span> strategy is proposed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26626623','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26626623"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju</p> <p>2016-01-01</p> <p>Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, the output voltages of the inverter is chosen as the <span class="hlt">fault</span> characteristic signals. To shorten the time of <span class="hlt">diagnosis</span> and improve the diagnostic accuracy, the main features of the <span class="hlt">fault</span> characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The <span class="hlt">fault</span> classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017FrME...12..427W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017FrME...12..427W"><span>Tacholess order-tracking approach for wind turbine gearbox <span class="hlt">fault</span> detection</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Yi; Xie, Yong; Xu, Guanghua; Zhang, Sicong; Hou, Chenggang</p> <p>2017-09-01</p> <p>Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex <span class="hlt">vibration</span> signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in <span class="hlt">vibration</span> signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine <span class="hlt">diagnosis</span> in industrial applications. Although order-tracking methods have been proposed for wind turbine <span class="hlt">fault</span> detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the <span class="hlt">vibration</span> signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine <span class="hlt">fault</span> identification. The effectiveness of the proposed method is experimentally validated with the <span class="hlt">vibration</span> signals of wind turbines.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MeScT..27b5017H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MeScT..27b5017H"><span>A new multiscale noise tuning stochastic resonance for enhanced <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in wind turbine drivetrains</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hu, Bingbing; Li, Bing</p> <p>2016-02-01</p> <p>It is very difficult to detect weak <span class="hlt">fault</span> signatures due to the large amount of noise in a wind turbine system. Multiscale noise tuning stochastic resonance (MSTSR) has proved to be an effective way to extract weak signals buried in strong noise. However, the MSTSR method originally based on discrete wavelet transform (DWT) has disadvantages such as shift variance and the aliasing effects in engineering application. In this paper, the dual-tree complex wavelet transform (DTCWT) is introduced into the MSTSR method, which makes it possible to further improve the system output signal-to-noise ratio and the accuracy of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> by the merits of DTCWT (nearly shift invariant and reduced aliasing effects). Moreover, this method utilizes the relationship between the two dual-tree wavelet basis functions, instead of matching the single wavelet basis function to the signal being analyzed, which may speed up the signal processing and be employed in on-line engineering monitoring. The proposed method is applied to the analysis of bearing outer ring and shaft coupling <span class="hlt">vibration</span> signals carrying <span class="hlt">fault</span> information. The results confirm that the method performs better in extracting the <span class="hlt">fault</span> features than the original DWT-based MSTSR, the wavelet transform with post spectral analysis, and EMD-based spectral analysis methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5375821','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5375821"><span>The Shock Pulse Index and Its Application in the <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Rolling Element Bearings</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Sun, Peng; Liao, Yuhe; Lin, Jin</p> <p>2017-01-01</p> <p>The properties of the time domain parameters of <span class="hlt">vibration</span> signals have been extensively studied for the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings (REBs). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are the most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock Pulse Index (SPI), is proposed in this paper. It integrates the mutual advantages of both the parameters mentioned above and can help effectively identify <span class="hlt">fault</span>-related impulse components under conditions of interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the transient information of interest contained in the filtered signal can be highlighted through demodulation with the Teager Energy Operator (TEO). <span class="hlt">Fault</span>-related impulse components can therefore be extracted accurately. Simulations show the SPI can correctly indicate the <span class="hlt">fault</span> impulses under the influence of strong background noises, other harmonic components and aperiodic impulse and experiment analyses verify the effectiveness and correctness of the proposed method. PMID:28282883</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...81..259L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...81..259L"><span>Centrifugal compressor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on qualitative simulation and thermal parameters</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, Yunsong; Wang, Fuli; Jia, Mingxing; Qi, Yuanchen</p> <p>2016-12-01</p> <p>This paper concerns <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of centrifugal compressor based on thermal parameters. An improved qualitative simulation (QSIM) based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is proposed to diagnose the <span class="hlt">faults</span> of centrifugal compressor in a gas-steam combined-cycle power plant (CCPP). The qualitative models under normal and two faulty conditions have been built through the analysis of the principle of centrifugal compressor. To solve the problem of qualitative description of the observations of system variables, a qualitative trend extraction algorithm is applied to extract the trends of the observations. For qualitative states matching, a sliding window based matching strategy which consists of variables operating ranges constraints and qualitative constraints is proposed. The matching results are used to determine which QSIM model is more consistent with the running state of system. The correct <span class="hlt">diagnosis</span> of two typical <span class="hlt">faults</span>: seal leakage and valve stuck in the centrifugal compressor has validated the targeted performance of the proposed method, showing the advantages of <span class="hlt">fault</span> roots containing in thermal parameters.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19900017997','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19900017997"><span>A <span class="hlt">diagnosis</span> system using object-oriented <span class="hlt">fault</span> tree models</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Iverson, David L.; Patterson-Hine, F. A.</p> <p>1990-01-01</p> <p>Spaceborne computing systems must provide reliable, continuous operation for extended periods. Due to weight, power, and volume constraints, these systems must manage resources very effectively. A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm is described which enables fast and flexible diagnoses in the dynamic distributed computing environments planned for future space missions. The algorithm uses a knowledge base that is easily changed and updated to reflect current system status. Augmented <span class="hlt">fault</span> trees represented in an object-oriented form provide deep system knowledge that is easy to access and revise as a system changes. Given such a <span class="hlt">fault</span> tree, a set of failure events that have occurred, and a set of failure events that have not occurred, this <span class="hlt">diagnosis</span> system uses forward and backward chaining to propagate causal and temporal information about other failure events in the system being diagnosed. Once the system has established temporal and causal constraints, it reasons backward from heuristically selected failure events to find a set of basic failure events which are a likely cause of the occurrence of the top failure event in the <span class="hlt">fault</span> tree. The <span class="hlt">diagnosis</span> system has been implemented in common LISP using Flavors.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5856166','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5856166"><span>Naive Bayes Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Enhanced Independence of Data</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong</p> <p>2018-01-01</p> <p>The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the <span class="hlt">fault</span> with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. PMID:29401730</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011PhDT........82W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011PhDT........82W"><span><span class="hlt">Fault</span> detection and <span class="hlt">diagnosis</span> of photovoltaic systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Xing</p> <p></p> <p>The rapid growth of the solar industry over the past several years has expanded the significance of photovoltaic (PV) systems. One of the primary aims of research in building-integrated PV systems is to improve the performance of the system's efficiency, availability, and reliability. Although much work has been done on technological design to increase a photovoltaic module's efficiency, there is little research so far on <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for PV systems. <span class="hlt">Faults</span> in a PV system, if not detected, may not only reduce power generation, but also threaten the availability and reliability, effectively the "security" of the whole system. In this paper, first a circuit-based simulation baseline model of a PV system with maximum power point tracking (MPPT) is developed using MATLAB software. MATLAB is one of the most popular tools for integrating computation, visualization and programming in an easy-to-use modeling environment. Second, data collection of a PV system at variable surface temperatures and insolation levels under normal operation is acquired. The developed simulation model of PV system is then calibrated and improved by comparing modeled I-V and P-V characteristics with measured I--V and P--V characteristics to make sure the simulated curves are close to those measured values from the experiments. Finally, based on the circuit-based simulation model, a PV model of various types of <span class="hlt">faults</span> will be developed by changing conditions or inputs in the MATLAB model, and the I--V and P--V characteristic curves, and the time-dependent voltage and current characteristics of the <span class="hlt">fault</span> modalities will be characterized for each type of <span class="hlt">fault</span>. These will be developed as benchmark I-V or P-V, or prototype transient curves. If a <span class="hlt">fault</span> occurs in a PV system, polling and comparing actual measured I--V and P--V characteristic curves with both normal operational curves and these baseline <span class="hlt">fault</span> curves will aid in <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015MS%26E...90a2081Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015MS%26E...90a2081Z"><span>A <span class="hlt">Fault</span> Alarm and <span class="hlt">Diagnosis</span> Method Based on Sensitive Parameters and Support Vector Machine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong</p> <p>2015-08-01</p> <p>Study on the extraction of <span class="hlt">fault</span> feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span> at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical <span class="hlt">fault</span> alarm and the accuracy of <span class="hlt">diagnosis</span> have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical <span class="hlt">fault</span> alarm and automatic <span class="hlt">diagnosis</span> in practical engineering is urgent task. The typical mechanical <span class="hlt">faults</span> of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract <span class="hlt">fault</span> feature parameters within 15 types in total; the inner sensitive connection between <span class="hlt">faults</span> and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different <span class="hlt">faults</span> have been obtained. On this basis, a method based on <span class="hlt">fault</span> feature parameters and support vector machine (SVM) is developed, which will be applied to practical <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. A better ability of early <span class="hlt">fault</span> warning has been proved by the experiment and the practical <span class="hlt">fault</span> cases. Automatic classification by using the SVM to the data of <span class="hlt">fault</span> alarm has obtained better diagnostic accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19730002713','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19730002713"><span>A survey of an introduction to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithms</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Mathur, F. P.</p> <p>1972-01-01</p> <p>This report surveys the field of <span class="hlt">diagnosis</span> and introduces some of the key algorithms and heuristics currently in use. <span class="hlt">Fault</span> <span class="hlt">diagnosis</span> is an important and a rapidly growing discipline. This is important in the design of self-repairable computers because the present <span class="hlt">diagnosis</span> resolution of its <span class="hlt">fault</span>-tolerant computer is limited to a functional unit or processor. Better resolution is necessary before failed units can become partially reuseable. The approach that holds the greatest promise is that of resident microdiagnostics; however, that presupposes a microprogrammable architecture for the computer being self-diagnosed. The presentation is tutorial and contains examples. An extensive bibliography of some 220 entries is included.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...85...56A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...85...56A"><span><span class="hlt">Diagnosis</span> of combined <span class="hlt">faults</span> in Rotary Machinery by Non-Naive Bayesian approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Asr, Mahsa Yazdanian; Ettefagh, Mir Mohammad; Hassannejad, Reza; Razavi, Seyed Naser</p> <p>2017-02-01</p> <p>When combined <span class="hlt">faults</span> happen in different parts of the rotating machines, their features are profoundly dependent. Experts are completely familiar with individuals <span class="hlt">faults</span> characteristics and enough data are available from single <span class="hlt">faults</span> but the problem arises, when the <span class="hlt">faults</span> combined and the separation of characteristics becomes complex. Therefore, the experts cannot declare exact information about the symptoms of combined <span class="hlt">fault</span> and its quality. In this paper to overcome this drawback, a novel method is proposed. The core idea of the method is about declaring combined <span class="hlt">fault</span> without using combined <span class="hlt">fault</span> features as training data set and just individual <span class="hlt">fault</span> features are applied in training step. For this purpose, after data acquisition and resampling the obtained <span class="hlt">vibration</span> signals, Empirical Mode Decomposition (EMD) is utilized to decompose multi component signals to Intrinsic Mode Functions (IMFs). With the use of correlation coefficient, proper IMFs for feature extraction are selected. In feature extraction step, Shannon energy entropy of IMFs was extracted as well as statistical features. It is obvious that most of extracted features are strongly dependent. To consider this matter, Non-Naive Bayesian Classifier (NNBC) is appointed, which release the fundamental assumption of Naive Bayesian, i.e., the independence among features. To demonstrate the superiority of NNBC, other counterpart methods, include Normal Naive Bayesian classifier, Kernel Naive Bayesian classifier and Back Propagation Neural Networks were applied and the classification results are compared. An experimental <span class="hlt">vibration</span> signals, collected from automobile gearbox, were used to verify the effectiveness of the proposed method. During the classification process, only the features, related individually to healthy state, bearing failure and gear failures, were assigned for training the classifier. But, combined <span class="hlt">fault</span> features (combined gear and bearing failures) were examined as test data. The achieved</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19960022972&hterms=deep+neural+network&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Ddeep%2Bneural%2Bnetwork','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19960022972&hterms=deep+neural+network&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Ddeep%2Bneural%2Bnetwork"><span>SSME <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> expert system</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ali, Moonis; Norman, Arnold M.; Gupta, U. K.</p> <p>1989-01-01</p> <p>An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based <span class="hlt">fault</span> pattern matching techniques. The <span class="hlt">fault</span> <span class="hlt">diagnosis</span> results obtained through the analyses of SSME ground test data are presented and discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19860063839&hterms=artificial+intelligence+diagnosis&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dartificial%2Bintelligence%2Bdiagnosis','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19860063839&hterms=artificial+intelligence+diagnosis&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dartificial%2Bintelligence%2Bdiagnosis"><span>An artificial intelligence approach to onboard <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> for aircraft applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schutte, P. C.; Abbott, K. H.</p> <p>1986-01-01</p> <p>Real-time onboard <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> for aircraft applications, whether performed by the human pilot or by automation, presents many difficult problems. Quick response to failures may be critical, the pilot often must compensate for the failure while diagnosing it, his information about the state of the aircraft is often incomplete, and the behavior of the aircraft changes as the effect of the failure propagates through the system. A research effort was initiated to identify guidelines for automation of onboard <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> and associated crew interfaces. The effort began by determining the flight crew's information requirements for <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> and the various reasoning strategies they use. Based on this information, a conceptual architecture was developed for the <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> process. This architecture represents an approach and a framework which, once incorporated with the necessary detail and knowledge, can be a fully operational <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> system, as well as providing the basis for comparison of this approach to other <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> concepts. The architecture encompasses all aspects of the aircraft's operation, including navigation, guidance and controls, and subsystem status. The portion of the architecture that encompasses subsystem monitoring and <span class="hlt">diagnosis</span> was implemented for an aircraft turbofan engine to explore and demonstrate the AI concepts involved. This paper describes the architecture and the implementation for the engine subsystem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19960052316&hterms=knowledge+power&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dknowledge%2Bpower','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19960052316&hterms=knowledge+power&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dknowledge%2Bpower"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Power Systems Using Intelligent Systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Momoh, James A.; Oliver, Walter E. , Jr.</p> <p>1996-01-01</p> <p>The power system operator's need for a reliable power delivery system calls for a real-time or near-real-time Al-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> tool. Such a tool will allow NASA ground controllers to re-establish a normal or near-normal degraded operating state of the EPS (a DC power system) for Space Station Alpha by isolating the <span class="hlt">faulted</span> branches and loads of the system. And after isolation, re-energizing those branches and loads that have been found not to have any <span class="hlt">faults</span> in them. A proposed solution involves using the <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Intelligent System (FDIS) to perform near-real time <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of Alpha's EPS by downloading power transient telemetry at <span class="hlt">fault</span>-time from onboard data loggers. The FDIS uses an ANN clustering algorithm augmented with a wavelet transform feature extractor. This combination enables this system to perform pattern recognition of the power transient signatures to diagnose the <span class="hlt">fault</span> type and its location down to the orbital replaceable unit. FDIS has been tested using a simulation of the LeRC Testbed Space Station Freedom configuration including the topology from the DDCU's to the electrical loads attached to the TPDU's. FDIS will work in conjunction with the Power Management Load Scheduler to determine what the state of the system was at the time of the <span class="hlt">fault</span> condition. This information is used to activate the appropriate diagnostic section, and to refine if necessary the solution obtained. In the latter case, if the FDIS reports back that it is equally likely that the faulty device as 'start tracker #1' and 'time generation unit,' then based on a priori knowledge of the system's state, the refined solution would be 'star tracker #1' located in cabinet ITAS2. It is concluded from the present studies that artificial intelligence diagnostic abilities are improved with the addition of the wavelet transform, and that when such a system such as FDIS is coupled to the Power Management Load Scheduler, a faulty device can be located and isolated</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1349216','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1349216"><span>A novel <span class="hlt">vibration</span>-based <span class="hlt">fault</span> diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hong, Liu; Qu, Yongzhi; Dhupia, Jaspreet Singh</p> <p></p> <p>The localized failures of gears introduce cyclic-transient impulses in the measured gearbox <span class="hlt">vibration</span> signals. These impulses are usually identified from the sidebands around gear-mesh harmonics through the spectral analysis of cyclo-stationary signals. However, in practice, several high-powered applications of gearboxes like wind turbines are intrinsically characterized by nonstationary processes that blur the measured <span class="hlt">vibration</span> spectra of a gearbox and deteriorate the efficacy of spectral diagnostic methods. Although order-tracking techniques have been proposed to improve the performance of spectral <span class="hlt">diagnosis</span> for nonstationary signals measured in such applications, the required hardware for the measurement of rotational speed of these machines is oftenmore » unavailable in industrial settings. Moreover, existing tacho-less order-tracking approaches are usually limited by the high time-frequency resolution requirement, which is a prerequisite for the precise estimation of the instantaneous frequency. To address such issues, a novel <span class="hlt">fault</span>-signature enhancement algorithm is proposed that can alleviate the spectral smearing without the need of rotational speed measurement. This proposed tacho-less diagnostic technique resamples the measured acceleration signal of the gearbox based on the optimal warping path evaluated from the fast dynamic time-warping algorithm, which aligns a filtered shaft rotational harmonic signal with respect to a reference signal assuming a constant shaft rotational speed estimated from the approximation of operational speed. The effectiveness of this method is validated using both simulated signals from a fixed-axis gear pair under nonstationary conditions and experimental measurements from a 750-kW planetary wind turbine gearbox on a dynamometer test rig. Lastly, the results demonstrate that the proposed algorithm can identify <span class="hlt">fault</span> information from typical gearbox <span class="hlt">vibration</span> measurements carried out in a resource-constrained industrial</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1349216-novel-vibration-based-fault-diagnostic-algorithm-gearboxes-under-speed-fluctuations-without-rotational-speed-measurement','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1349216-novel-vibration-based-fault-diagnostic-algorithm-gearboxes-under-speed-fluctuations-without-rotational-speed-measurement"><span>A novel <span class="hlt">vibration</span>-based <span class="hlt">fault</span> diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Hong, Liu; Qu, Yongzhi; Dhupia, Jaspreet Singh; ...</p> <p>2017-02-27</p> <p>The localized failures of gears introduce cyclic-transient impulses in the measured gearbox <span class="hlt">vibration</span> signals. These impulses are usually identified from the sidebands around gear-mesh harmonics through the spectral analysis of cyclo-stationary signals. However, in practice, several high-powered applications of gearboxes like wind turbines are intrinsically characterized by nonstationary processes that blur the measured <span class="hlt">vibration</span> spectra of a gearbox and deteriorate the efficacy of spectral diagnostic methods. Although order-tracking techniques have been proposed to improve the performance of spectral <span class="hlt">diagnosis</span> for nonstationary signals measured in such applications, the required hardware for the measurement of rotational speed of these machines is oftenmore » unavailable in industrial settings. Moreover, existing tacho-less order-tracking approaches are usually limited by the high time-frequency resolution requirement, which is a prerequisite for the precise estimation of the instantaneous frequency. To address such issues, a novel <span class="hlt">fault</span>-signature enhancement algorithm is proposed that can alleviate the spectral smearing without the need of rotational speed measurement. This proposed tacho-less diagnostic technique resamples the measured acceleration signal of the gearbox based on the optimal warping path evaluated from the fast dynamic time-warping algorithm, which aligns a filtered shaft rotational harmonic signal with respect to a reference signal assuming a constant shaft rotational speed estimated from the approximation of operational speed. The effectiveness of this method is validated using both simulated signals from a fixed-axis gear pair under nonstationary conditions and experimental measurements from a 750-kW planetary wind turbine gearbox on a dynamometer test rig. Lastly, the results demonstrate that the proposed algorithm can identify <span class="hlt">fault</span> information from typical gearbox <span class="hlt">vibration</span> measurements carried out in a resource-constrained industrial</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19930000401&hterms=tree+identification&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dtree%2Bidentification','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19930000401&hterms=tree+identification&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dtree%2Bidentification"><span>Software For <span class="hlt">Fault</span>-Tree <span class="hlt">Diagnosis</span> Of A System</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Iverson, Dave; Patterson-Hine, Ann; Liao, Jack</p> <p>1993-01-01</p> <p><span class="hlt">Fault</span> Tree <span class="hlt">Diagnosis</span> System (FTDS) computer program is automated-diagnostic-system program identifying likely causes of specified failure on basis of information represented in system-reliability mathematical models known as <span class="hlt">fault</span> trees. Is modified implementation of failure-cause-identification phase of Narayanan's and Viswanadham's methodology for acquisition of knowledge and reasoning in analyzing failures of systems. Knowledge base of if/then rules replaced with object-oriented <span class="hlt">fault</span>-tree representation. Enhancement yields more-efficient identification of causes of failures and enables dynamic updating of knowledge base. Written in C language, C++, and Common LISP.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4188586','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4188586"><span>Advanced <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Methods in Molecular Networks</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Habibi, Iman; Emamian, Effat S.; Abdi, Ali</p> <p>2014-01-01</p> <p>Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-<span class="hlt">fault</span> vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-<span class="hlt">fault</span> analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single <span class="hlt">fault</span> analysis. The ternary <span class="hlt">fault</span> analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally. PMID:25290670</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19880025313&hterms=artificial+intelligence+diagnosis&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dartificial%2Bintelligence%2Bdiagnosis','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19880025313&hterms=artificial+intelligence+diagnosis&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dartificial%2Bintelligence%2Bdiagnosis"><span>Implementation of a research prototype onboard <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> system</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Palmer, Michael T.; Abbott, Kathy H.; Schutte, Paul C.; Ricks, Wendell R.</p> <p>1987-01-01</p> <p>Due to the dynamic and complex nature of in-flight <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span>, a research effort was undertaken at NASA Langley Research Center to investigate the application of artificial intelligence techniques for improved situational awareness. Under this research effort, concepts were developed and a software architecture was designed to address the complexities of onboard monitoring and <span class="hlt">diagnosis</span>. This paper describes the implementation of these concepts in a computer program called <span class="hlt">Fault</span>Finder. The implementation of the monitoring, <span class="hlt">diagnosis</span>, and interface functions as separate modules is discussed, as well as the blackboard designed for the communication of these modules. Some related issues concerning the future installation of <span class="hlt">Fault</span>Finder in an aircraft are also discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/21153957-line-early-fault-detection-diagnosis-municipal-solid-waste-incinerators','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/21153957-line-early-fault-detection-diagnosis-municipal-solid-waste-incinerators"><span>On-line early <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> of municipal solid waste incinerators</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Zhao Jinsong; Huang Jianchao; Sun Wei</p> <p></p> <p>A <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> framework is proposed in this paper for early <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and consequence prediction, and also generates recommendations for <span class="hlt">fault</span> mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows thatmore » automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong <span class="hlt">diagnosis</span>, which has resulted in improved process continuity and environmental performance of the MSWI.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...94..464G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...94..464G"><span>Comparative investigation of <span class="hlt">vibration</span> and current monitoring for prediction of mechanical and electrical <span class="hlt">faults</span> in induction motor based on multiclass-support vector machine algorithms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gangsar, Purushottam; Tiwari, Rajiv</p> <p>2017-09-01</p> <p>This paper presents an investigation of <span class="hlt">vibration</span> and current monitoring for effective <span class="hlt">fault</span> prediction in induction motor (IM) by using multiclass support vector machine (MSVM) algorithms. Failures of IM may occur due to propagation of a mechanical or electrical <span class="hlt">fault</span>. Hence, for timely detection of these <span class="hlt">faults</span>, the <span class="hlt">vibration</span> as well as current signals was acquired after multiple experiments of varying speeds and external torques from an experimental test rig. Here, total ten different <span class="hlt">fault</span> conditions that frequently encountered in IM (four mechanical <span class="hlt">fault</span>, five electrical <span class="hlt">fault</span> conditions and one no defect condition) have been considered. In the case of stator winding <span class="hlt">fault</span>, and phase unbalance and single phasing <span class="hlt">fault</span>, different level of severity were also considered for the prediction. In this study, the identification has been performed of the mechanical and electrical <span class="hlt">faults</span>, individually and collectively. <span class="hlt">Fault</span> predictions have been performed using <span class="hlt">vibration</span> signal alone, current signal alone and <span class="hlt">vibration</span>-current signal concurrently. The one-versus-one MSVM has been trained at various operating conditions of IM using the radial basis function (RBF) kernel and tested for same conditions, which gives the result in the form of percentage <span class="hlt">fault</span> prediction. The prediction performance is investigated for the wide range of RBF kernel parameter, i.e. gamma, and selected the best result for one optimal value of gamma for each case. <span class="hlt">Fault</span> predictions has been performed and investigated for the wide range of operational speeds of the IM as well as external torques on the IM.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4701337','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4701337"><span>Early <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jia, Feng; Lei, Yaguo; Shan, Hongkai; Lin, Jing</p> <p>2015-01-01</p> <p>The early <span class="hlt">fault</span> characteristics of rolling element bearings carried by <span class="hlt">vibration</span> signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak <span class="hlt">fault</span> characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic <span class="hlt">fault</span> transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its <span class="hlt">fault</span> characteristic frequency. Through analyzing actual <span class="hlt">vibration</span> signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract <span class="hlt">fault</span> characteristics and diagnose early <span class="hlt">faults</span> of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early <span class="hlt">faults</span> of rolling element bearings. PMID:26610501</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...70....1C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...70....1C"><span>Wavelet transform based on inner product in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery: A review</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Jinglong; Li, Zipeng; Pan, Jun; Chen, Gaige; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia</p> <p>2016-03-01</p> <p>As a significant role in industrial equipment, rotating machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span> (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary <span class="hlt">vibration</span> signal with a large amount of noise on abnormal condition of weak <span class="hlt">fault</span> or compound <span class="hlt">fault</span> in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Numerous publications about the study and applications of WT for RMFD have been presented to academic journals, technical reports and conference proceedings. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation and field experiments. Then the development process of WT based on inner product is concluded and the applications of major developments in RMFD are also summarized. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed. It is expected that this paper can offer an in-depth and comprehensive references for researchers and help them with finding out further research topics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24806649','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24806649"><span>Learning in the model space for cognitive <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chen, Huanhuan; Tino, Peter; Rodan, Ali; Yao, Xin</p> <p>2014-01-01</p> <p>The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive <span class="hlt">fault</span> <span class="hlt">diagnosis</span> framework that tackles the above challenges. This framework investigates <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a <span class="hlt">fault</span> library when unknown <span class="hlt">faults</span> occur, which can be regarded as cognitive <span class="hlt">fault</span> isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4801562','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4801562"><span>Simultaneous-<span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Gearboxes Using Probabilistic Committee Machine</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin</p> <p>2016-01-01</p> <p>This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox <span class="hlt">fault</span> detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the <span class="hlt">fault</span> features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox <span class="hlt">fault</span>. The two PCRVMs serve as two different <span class="hlt">fault</span> detection committee members, and they are trained by using <span class="hlt">vibration</span> and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable <span class="hlt">faults</span> as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-<span class="hlt">faults</span> in the gearbox. PMID:26848665</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26848665','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26848665"><span>Simultaneous-<span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Gearboxes Using Probabilistic Committee Machine.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin</p> <p>2016-02-02</p> <p>This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox <span class="hlt">fault</span> detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the <span class="hlt">fault</span> features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox <span class="hlt">fault</span>. The two PCRVMs serve as two different <span class="hlt">fault</span> detection committee members, and they are trained by using <span class="hlt">vibration</span> and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable <span class="hlt">faults</span> as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-<span class="hlt">faults</span> in the gearbox.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3274107','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3274107"><span>Simplified Interval Observer Scheme: A New Approach for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Instruments</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Martínez-Sibaja, Albino; Astorga-Zaragoza, Carlos M.; Alvarado-Lassman, Alejandro; Posada-Gómez, Rubén; Aguila-Rodríguez, Gerardo; Rodríguez-Jarquin, José P.; Adam-Medina, Manuel</p> <p>2011-01-01</p> <p>There are different schemes based on observers to detect and isolate <span class="hlt">faults</span> in dynamic processes. In the case of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in instruments (FDI) there are different <span class="hlt">diagnosis</span> schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting <span class="hlt">faults</span> in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple <span class="hlt">faults</span> in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique <span class="hlt">faults</span>. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique <span class="hlt">faults</span> in sensors and because it does not require any input, it simplifies in an important way the <span class="hlt">diagnosis</span> of <span class="hlt">faults</span> in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors. PMID:22346593</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..339a2001M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..339a2001M"><span>A Power Transformers <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Model Based on Three DGA Ratios and PSO Optimization SVM</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan</p> <p>2018-03-01</p> <p>In order to make up for the shortcomings of existing transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span> model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span> combined with the cross validation principle. The <span class="hlt">fault</span> <span class="hlt">diagnosis</span> results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is proved.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015IJBC...2550042K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015IJBC...2550042K"><span>Study on Unified Chaotic System-Based Wind Turbine Blade <span class="hlt">Fault</span> Diagnostic System</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kuo, Ying-Che; Hsieh, Chin-Tsung; Yau, Her-Terng; Li, Yu-Chung</p> <p></p> <p>At present, <span class="hlt">vibration</span> signals are processed and analyzed mostly in the frequency domain. The spectrum clearly shows the signal structure and the specific characteristic frequency band is analyzed, but the number of calculations required is huge, resulting in delays. Therefore, this study uses the characteristics of a nonlinear system to load the complete <span class="hlt">vibration</span> signal to the unified chaotic system, applying the dynamic error to analyze the wind turbine <span class="hlt">vibration</span> signal, and adopting extenics theory for artificial intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of the analysis signal. Hence, a <span class="hlt">fault</span> diagnostor has been developed for wind turbine rotating blades. This study simulates three wind turbine blade states, namely stress rupture, screw loosening and blade loss, and validates the methods. The experimental results prove that the unified chaotic system used in this paper has a significant effect on <span class="hlt">vibration</span> signal analysis. Thus, the operating conditions of wind turbines can be quickly known from this <span class="hlt">fault</span> diagnostic system, and the maintenance schedule can be arranged before the <span class="hlt">faults</span> worsen, making the management and implementation of wind turbines smoother, so as to reduce many unnecessary costs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27370486','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27370486"><span><span class="hlt">Fault</span> detection, isolation, and <span class="hlt">diagnosis</span> of self-validating multifunctional sensors.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen</p> <p>2016-06-01</p> <p>A novel <span class="hlt">fault</span> detection, isolation, and <span class="hlt">diagnosis</span> (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect <span class="hlt">faults</span> by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the <span class="hlt">fault</span> signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the <span class="hlt">fault</span> signals. Multi-class support vector machine is introduced to identify the <span class="hlt">fault</span> mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other <span class="hlt">fault</span> detection strategies such as principal component analysis, independent component analysis, and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29659510','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29659510"><span>Multi-<span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yuan, Rui; Lv, Yong; Song, Gangbing</p> <p>2018-04-16</p> <p>Rolling bearings are important components in rotary machinery systems. In the field of multi-<span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling bearings, the <span class="hlt">vibration</span> signal collected from single channels tends to miss some <span class="hlt">fault</span> characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-<span class="hlt">fault</span> frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the <span class="hlt">fault</span> number. The <span class="hlt">fault</span> correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-<span class="hlt">faults</span> can then be extracted. Numerical simulations and the application of multi-<span class="hlt">fault</span> situation can demonstrate that the proposed method is promising in multi-<span class="hlt">fault</span> diagnoses of multivariate rolling bearing signal.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JPS...378..646L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JPS...378..646L"><span>Data-driven simultaneous <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for solid oxide fuel cell system using multi-label pattern identification</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Shuanghong; Cao, Hongliang; Yang, Yupu</p> <p>2018-02-01</p> <p><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> is a key process for the reliability and safety of solid oxide fuel cell (SOFC) systems. However, it is difficult to rapidly and accurately identify <span class="hlt">faults</span> for complicated SOFC systems, especially when simultaneous <span class="hlt">faults</span> appear. In this research, a data-driven Multi-Label (ML) pattern identification approach is proposed to address the simultaneous <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of SOFC systems. The framework of the simultaneous-<span class="hlt">fault</span> <span class="hlt">diagnosis</span> primarily includes two components: feature extraction and ML-SVM classifier. The simultaneous-<span class="hlt">fault</span> <span class="hlt">diagnosis</span> approach can be trained to diagnose simultaneous SOFC <span class="hlt">faults</span>, such as fuel leakage, air leakage in different positions in the SOFC system, by just using simple training data sets consisting only single <span class="hlt">fault</span> and not demanding simultaneous <span class="hlt">faults</span> data. The experimental result shows the proposed framework can diagnose the simultaneous SOFC system <span class="hlt">faults</span> with high accuracy requiring small number training data and low computational burden. In addition, <span class="hlt">Fault</span> Inference Tree Analysis (FITA) is employed to identify the correlations among possible <span class="hlt">faults</span> and their corresponding symptoms at the system component level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016E%26ES...40a2030W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016E%26ES...40a2030W"><span>A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system for PV power station based on global partitioned gradually approximation method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, S.; Zhang, X. N.; Gao, D. D.; Liu, H. X.; Ye, J.; Li, L. R.</p> <p>2016-08-01</p> <p>As the solar photovoltaic (PV) power is applied extensively, more attentions are paid to the maintenance and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of PV power plants. Based on analysis of the structure of PV power station, the global partitioned gradually approximation method is proposed as a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm to determine and locate the <span class="hlt">fault</span> of PV panels. The PV array is divided into 16x16 blocks and numbered. On the basis of modularly processing of the PV array, the current values of each block are analyzed. The mean current value of each block is used for calculating the <span class="hlt">fault</span> weigh factor. The <span class="hlt">fault</span> threshold is defined to determine the <span class="hlt">fault</span>, and the shade is considered to reduce the probability of misjudgments. A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system is designed and implemented with LabVIEW. And it has some functions including the data realtime display, online check, statistics, real-time prediction and <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Through the data from PV plants, the algorithm is verified. The results show that the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> results are accurate, and the system works well. The validity and the possibility of the system are verified by the results as well. The developed system will be benefit for the maintenance and management of large scale PV array.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19960000402&hterms=spices&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dspices','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19960000402&hterms=spices&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dspices"><span>Artificial neural network application for space station power system <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Momoh, James A.; Oliver, Walter E.; Dias, Lakshman G.</p> <p>1995-01-01</p> <p>This study presents a methodology for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using a Two-Stage Artificial Neural Network Clustering Algorithm. Previously, SPICE models of a 5-bus DC power distribution system with assumed constant output power during contingencies from the DDCU were used to evaluate the ANN's <span class="hlt">fault</span> <span class="hlt">diagnosis</span> capabilities. This on-going study uses EMTP models of the components (distribution lines, SPDU, TPDU, loads) and power sources (DDCU) of Space Station Alpha's electrical Power Distribution System as a basis for the ANN <span class="hlt">fault</span> diagnostic tool. The results from the two studies are contrasted. In the event of a major <span class="hlt">fault</span>, ground controllers need the ability to identify the type of <span class="hlt">fault</span>, isolate the <span class="hlt">fault</span> to the orbital replaceable unit level and provide the necessary information for the power management expert system to optimally determine a degraded-mode load schedule. To accomplish these goals, the electrical power distribution system's architecture can be subdivided into three major classes: DC-DC converter to loads, DC Switching Unit (DCSU) to Main bus Switching Unit (MBSU), and Power Sources to DCSU. Each class which has its own electrical characteristics and operations, requires a unique <span class="hlt">fault</span> analysis philosophy. This study identifies these philosophies as Riddles 1, 2 and 3 respectively. The results of the on-going study addresses Riddle-1. It is concluded in this study that the combination of the EMTP models of the DDCU, distribution cables and electrical loads yields a more accurate model of the behavior and in addition yielded more accurate <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using ANN versus the results obtained with the SPICE models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26753616','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26753616"><span>A hybrid <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on second generation wavelet de-noising and local mean decomposition for rotating machinery.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Zhiwen; He, Zhengjia; Guo, Wei; Tang, Zhangchun</p> <p>2016-03-01</p> <p>In order to extract <span class="hlt">fault</span> features of large-scale power equipment from strong background noise, a hybrid <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery <span class="hlt">vibration</span> signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the <span class="hlt">vibration</span> signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..100..827L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..100..827L"><span>Average combination difference morphological filters for <span class="hlt">fault</span> feature extraction of bearing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lv, Jingxiang; Yu, Jianbo</p> <p>2018-02-01</p> <p>In order to extract impulse components from <span class="hlt">vibration</span> signals with much noise and harmonics, a new morphological filter called average combination difference morphological filter (ACDIF) is proposed in this paper. ACDIF constructs firstly several new combination difference (CDIF) operators, and then integrates the best two CDIFs as the final morphological filter. This design scheme enables ACIDF to extract positive and negative impacts existing in <span class="hlt">vibration</span> signals to enhance accuracy of bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The length of structure element (SE) that affects the performance of ACDIF is determined adaptively by a new indicator called Teager energy kurtosis (TEK). TEK further improves the effectiveness of ACDIF for <span class="hlt">fault</span> feature extraction. Experimental results on the simulation and bearing <span class="hlt">vibration</span> signals demonstrate that ACDIF can effectively suppress noise and extract periodic impulses from bearing <span class="hlt">vibration</span> signals.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28098822','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28098822"><span>Application of <span class="hlt">Fault</span> Tree Analysis and Fuzzy Neural Networks to <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in the Internet of Things (IoT) for Aquaculture.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chen, Yingyi; Zhen, Zhumi; Yu, Huihui; Xu, Jing</p> <p>2017-01-14</p> <p>In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. <span class="hlt">Faults</span> occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once <span class="hlt">faults</span> happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on <span class="hlt">fault</span> tree analysis and a fuzzy neural network. In the proposed method, first, the <span class="hlt">fault</span> tree presents a logic structure of <span class="hlt">fault</span> symptoms and <span class="hlt">faults</span>. Second, rules extracted from the <span class="hlt">fault</span> trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between <span class="hlt">fault</span> symptoms and <span class="hlt">faults</span>. In the aquaculture IoT, one <span class="hlt">fault</span> can cause various <span class="hlt">fault</span> symptoms, and one symptom can be caused by a variety of <span class="hlt">faults</span>. Four <span class="hlt">fault</span> relationships are obtained. Results show that one symptom-to-one <span class="hlt">fault</span>, two symptoms-to-two <span class="hlt">faults</span>, and two symptoms-to-one <span class="hlt">fault</span> relationships can be rapidly diagnosed with high precision, while one symptom-to-two <span class="hlt">faults</span> patterns perform not so well, but are still worth researching. This model implements <span class="hlt">diagnosis</span> for most kinds of <span class="hlt">faults</span> in the aquaculture IoT.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5298726','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5298726"><span>Application of <span class="hlt">Fault</span> Tree Analysis and Fuzzy Neural Networks to <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in the Internet of Things (IoT) for Aquaculture</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Chen, Yingyi; Zhen, Zhumi; Yu, Huihui; Xu, Jing</p> <p>2017-01-01</p> <p>In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. <span class="hlt">Faults</span> occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once <span class="hlt">faults</span> happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on <span class="hlt">fault</span> tree analysis and a fuzzy neural network. In the proposed method, first, the <span class="hlt">fault</span> tree presents a logic structure of <span class="hlt">fault</span> symptoms and <span class="hlt">faults</span>. Second, rules extracted from the <span class="hlt">fault</span> trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between <span class="hlt">fault</span> symptoms and <span class="hlt">faults</span>. In the aquaculture IoT, one <span class="hlt">fault</span> can cause various <span class="hlt">fault</span> symptoms, and one symptom can be caused by a variety of <span class="hlt">faults</span>. Four <span class="hlt">fault</span> relationships are obtained. Results show that one symptom-to-one <span class="hlt">fault</span>, two symptoms-to-two <span class="hlt">faults</span>, and two symptoms-to-one <span class="hlt">fault</span> relationships can be rapidly diagnosed with high precision, while one symptom-to-two <span class="hlt">faults</span> patterns perform not so well, but are still worth researching. This model implements <span class="hlt">diagnosis</span> for most kinds of <span class="hlt">faults</span> in the aquaculture IoT. PMID:28098822</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...85..278S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...85..278S"><span>Frequency domain averaging based experimental evaluation of gear <span class="hlt">fault</span> without tachometer for fluctuating speed conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sharma, Vikas; Parey, Anand</p> <p>2017-02-01</p> <p>In the purview of fluctuating speeds, gear <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is challenging due to dynamic behavior of forces. Various industrial applications employing gearbox which operate under fluctuating speed conditions. For diagnostics of a gearbox, various <span class="hlt">vibrations</span> based signal processing techniques viz FFT, time synchronous averaging and time-frequency based wavelet transform, etc. are majorly employed. Most of the time, theories about data or computational complexity limits the use of these methods. In order to perform <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of a gearbox for fluctuating speeds, frequency domain averaging (FDA) of intrinsic mode functions (IMFs) after their dynamic time warping (DTW) has been done in this paper. This will not only attenuate the effect of fluctuating speeds but will also extract the weak <span class="hlt">fault</span> feature those masked in <span class="hlt">vibration</span> signal. Experimentally signals were acquired from Drivetrain Diagnostic Simulator for different gear health conditions i.e., healthy pinion, pinion with tooth crack, chipped tooth and missing tooth and were analyzed for the different fluctuating profiles of speed. Kurtosis was calculated for warped IMFs before DTW and after DTW of the acquired <span class="hlt">vibration</span> signals. Later on, the application of FDA highlights the <span class="hlt">fault</span> frequencies present in the FFT of faulty gears. The result suggests that proposed approach is more effective towards the <span class="hlt">fault</span> diagnosing with fluctuating speed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25152929','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25152929"><span>SOM neural network <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method of polymerization kettle equipment optimized by improved PSO algorithm.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Jie-sheng; Li, Shu-xia; Gao, Jie</p> <p>2014-01-01</p> <p>For meeting the real-time <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the <span class="hlt">fault</span> pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The <span class="hlt">fault</span> pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to <span class="hlt">fault</span> set according to the given symptom set. Finally, the simulation experiments of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM <span class="hlt">fault</span> <span class="hlt">diagnosis</span> strategy is effective.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060051814&hterms=1062&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3D%2526%25231062','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060051814&hterms=1062&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3D%2526%25231062"><span>Optimal Sensor Allocation for <span class="hlt">Fault</span> Detection and Isolation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Azam, Mohammad; Pattipati, Krishna; Patterson-Hine, Ann</p> <p>2004-01-01</p> <p>Automatic <span class="hlt">fault</span> diagnostic schemes rely on various types of sensors (e.g., temperature, pressure, <span class="hlt">vibration</span>, etc) to measure the system parameters. Efficacy of a diagnostic scheme is largely dependent on the amount and quality of information available from these sensors. The reliability of sensors, as well as the weight, volume, power, and cost constraints, often makes it impractical to monitor a large number of system parameters. An optimized sensor allocation that maximizes the <span class="hlt">fault</span> diagnosibility, subject to specified weight, volume, power, and cost constraints is required. Use of optimal sensor allocation strategies during the design phase can ensure better diagnostics at a reduced cost for a system incorporating a high degree of built-in testing. In this paper, we propose an approach that employs multiple <span class="hlt">fault</span> <span class="hlt">diagnosis</span> (MFD) and optimization techniques for optimal sensor placement for <span class="hlt">fault</span> detection and isolation (FDI) in complex systems. Keywords: sensor allocation, multiple <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, Lagrangian relaxation, approximate belief revision, multidimensional knapsack problem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JSV...379..213Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JSV...379..213Z"><span>Time-varying singular value decomposition for periodic transient identification in bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Shangbin; Lu, Siliang; He, Qingbo; Kong, Fanrang</p> <p>2016-09-01</p> <p>For rotating machines, the defective <span class="hlt">faults</span> of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. However, the background noise reduces identification performance of periodic <span class="hlt">faults</span> in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic <span class="hlt">faults</span>. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance <span class="hlt">fault</span> frequency for <span class="hlt">diagnosis</span>. The effectiveness of the TSVD is verified by means of simulation studies and applications to <span class="hlt">diagnosis</span> of bearing <span class="hlt">faults</span>. Results indicate that the proposed method is superior to traditional methods for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26229526','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26229526"><span>A Novel Mittag-Leffler Kernel Based Hybrid <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Method for Wheeled Robot Driving System.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan</p> <p>2015-01-01</p> <p>The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid <span class="hlt">fault</span> <span class="hlt">diagnosis</span> framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for <span class="hlt">fault</span> feature extraction. The <span class="hlt">fault</span> feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary <span class="hlt">fault</span> <span class="hlt">diagnosis</span> results and their confidence values. Eventually, the final <span class="hlt">fault</span> <span class="hlt">diagnosis</span> result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the <span class="hlt">faults</span> in the robot driving system, but also has better performance in stability and <span class="hlt">diagnosis</span> accuracy compared with the traditional methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SPIE10322E..43Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SPIE10322E..43Y"><span>Analysis and control of the <span class="hlt">vibration</span> of doubly fed wind turbine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yu, Manye; Lin, Ying</p> <p>2017-01-01</p> <p>The <span class="hlt">fault</span> phenomena of the violent <span class="hlt">vibration</span> of certain doubly-fed wind turbine were researched comprehensively, and the dynamic characteristics, load and <span class="hlt">fault</span> conditions of the system were discussed. Firstly, the structural dynamics analysis of wind turbine is made, and the dynamics mold is built. Secondly, the <span class="hlt">vibration</span> testing of wind turbine is done with the German test and analysis systems BBM. Thirdly, signal should be analyzed and dealt with. Based on the experiment, spectrum analysis of the motor dynamic balance can be made by using signal processing toolbox of MATLAB software, and the analysis conclusions show that the <span class="hlt">vibration</span> of wind turbine is caused by dynamic imbalance. The results show that integrating mechanical system dynamics theory with advanced test technology can solve the <span class="hlt">vibration</span> problem more successfully, which is important in <span class="hlt">vibration</span> <span class="hlt">diagnosis</span> of mechanical equipment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880007050','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880007050"><span>A data structure and algorithm for <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bosworth, Edward L., Jr.</p> <p>1987-01-01</p> <p>Results of preliminary research on the design of a knowledge based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system for use with on-orbit spacecraft such as the Hubble Space Telescope are presented. A candidate data structure and associated search algorithm from which the knowledge based system can evolve is discussed. This algorithmic approach will then be examined in view of its inability to diagnose certain common <span class="hlt">faults</span>. From that critique, a design for the corresponding knowledge based system will be given.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP...98..951W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP...98..951W"><span><span class="hlt">Fault</span> feature analysis of cracked gear based on LOD and analytical-FE method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Jiateng; Yang, Yu; Yang, Xingkai; Cheng, Junsheng</p> <p>2018-01-01</p> <p>At present, there are two main ideas for gear <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. One is the model-based gear dynamic analysis; the other is signal-based gear <span class="hlt">vibration</span> <span class="hlt">diagnosis</span>. In this paper, a method for <span class="hlt">fault</span> feature analysis of gear crack is presented, which combines the advantages of dynamic modeling and signal processing. Firstly, a new time-frequency analysis method called local oscillatory-characteristic decomposition (LOD) is proposed, which has the attractive feature of extracting <span class="hlt">fault</span> characteristic efficiently and accurately. Secondly, an analytical-finite element (analytical-FE) method which is called assist-stress intensity factor (assist-SIF) gear contact model, is put forward to calculate the time-varying mesh stiffness (TVMS) under different crack states. Based on the dynamic model of the gear system with 6 degrees of freedom, the dynamic simulation response was obtained for different tooth crack depths. For the dynamic model, the corresponding relation between the characteristic parameters and the degree of the tooth crack is established under a specific condition. On the basis of the methods mentioned above, a novel gear tooth root crack <span class="hlt">diagnosis</span> method which combines the LOD with the analytical-FE is proposed. Furthermore, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) are contrasted with the LOD by gear crack <span class="hlt">fault</span> <span class="hlt">vibration</span> signals. The analysis results indicate that the proposed method performs effectively and feasibility for the tooth crack stiffness calculation and the gear tooth crack <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...72..316F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...72..316F"><span><span class="hlt">Fault</span> detection and <span class="hlt">diagnosis</span> of diesel engine valve trains</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Flett, Justin; Bone, Gary M.</p> <p>2016-05-01</p> <p>This paper presents the development of a <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) system for use with a diesel internal combustion engine (ICE) valve train. A novel feature is generated for each of the valve closing and combustion impacts. Deformed valve spring <span class="hlt">faults</span> and abnormal valve clearance <span class="hlt">faults</span> were seeded on a diesel engine instrumented with one accelerometer. Five classification methods were implemented experimentally and compared. The FDD system using the Naïve-Bayes classification method produced the best overall performance, with a lowest detection accuracy (DA) of 99.95% and a lowest classification accuracy (CA) of 99.95% for the spring <span class="hlt">faults</span> occurring on individual valves. The lowest DA and CA values for multiple <span class="hlt">faults</span> occurring simultaneously were 99.95% and 92.45%, respectively. The DA and CA results demonstrate the accuracy of our FDD system for diesel ICE valve train <span class="hlt">fault</span> scenarios not previously addressed in the literature.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SPIE10322E..3IL','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SPIE10322E..3IL"><span>Study on <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and load feedback control system of combine harvester</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Ying; Wang, Kun</p> <p>2017-01-01</p> <p>In order to timely gain working status parameters of operating parts in combine harvester and improve its operating efficiency, <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and load feedback control system is designed. In the system, rotation speed sensors were used to gather these signals of forward speed and rotation speeds of intermediate shaft, conveying trough, tangential and longitudinal flow threshing rotors, grain conveying auger. Using C8051 single chip microcomputer (SCM) as processor for main control unit, <span class="hlt">faults</span> <span class="hlt">diagnosis</span> and forward speed control were carried through by rotation speed ratio analysis of each channel rotation speed and intermediate shaft rotation speed by use of multi-sensor fused fuzzy control algorithm, and these processing results would be sent to touch screen and display work status of combine harvester. Field trials manifest that <span class="hlt">fault</span> monitoring and load feedback control system has good man-machine interaction and the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on rotation speed ratios has low false alarm rate, and the system can realize automation control of forward speed for combine harvester.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948897','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948897"><span>Multi-<span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lv, Yong; Song, Gangbing</p> <p>2018-01-01</p> <p>Rolling bearings are important components in rotary machinery systems. In the field of multi-<span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling bearings, the <span class="hlt">vibration</span> signal collected from single channels tends to miss some <span class="hlt">fault</span> characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-<span class="hlt">fault</span> frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the <span class="hlt">fault</span> number. The <span class="hlt">fault</span> correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-<span class="hlt">faults</span> can then be extracted. Numerical simulations and the application of multi-<span class="hlt">fault</span> situation can demonstrate that the proposed method is promising in multi-<span class="hlt">fault</span> diagnoses of multivariate rolling bearing signal. PMID:29659510</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4504124','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4504124"><span>A Novel Mittag-Leffler Kernel Based Hybrid <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Method for Wheeled Robot Driving System</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yuan, Xianfeng; Song, Mumin; Chen, Zhumin; Li, Yan</p> <p>2015-01-01</p> <p>The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid <span class="hlt">fault</span> <span class="hlt">diagnosis</span> framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for <span class="hlt">fault</span> feature extraction. The <span class="hlt">fault</span> feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary <span class="hlt">fault</span> <span class="hlt">diagnosis</span> results and their confidence values. Eventually, the final <span class="hlt">fault</span> <span class="hlt">diagnosis</span> result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the <span class="hlt">faults</span> in the robot driving system, but also has better performance in stability and <span class="hlt">diagnosis</span> accuracy compared with the traditional methods. PMID:26229526</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007MSSP...21.1300S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007MSSP...21.1300S"><span>Decision tree and PCA-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sun, Weixiang; Chen, Jin; Li, Jiaqing</p> <p>2007-04-01</p> <p>After analysing the flaws of conventional <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods, data mining technology is introduced to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with <span class="hlt">diagnosis</span> knowledge. At last the tree model is used to make <span class="hlt">diagnosis</span> analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based <span class="hlt">diagnosis</span> method has higher accuracy and needs less training time than BPNN.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100039638','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100039638"><span>Runtime Verification in Context : Can Optimizing Error Detection Improve <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dwyer, Matthew B.; Purandare, Rahul; Person, Suzette</p> <p>2010-01-01</p> <p>Runtime verification has primarily been developed and evaluated as a means of enriching the software testing process. While many researchers have pointed to its potential applicability in online approaches to software <span class="hlt">fault</span> tolerance, there has been a dearth of work exploring the details of how that might be accomplished. In this paper, we describe how a component-oriented approach to software health management exposes the connections between program execution, error detection, <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, and recovery. We identify both research challenges and opportunities in exploiting those connections. Specifically, we describe how recent approaches to reducing the overhead of runtime monitoring aimed at error detection might be adapted to reduce the overhead and improve the effectiveness of <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JSV...385...16L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JSV...385...16L"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of motor bearing with speed fluctuation via angular resampling of transient sound signals</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin</p> <p>2016-12-01</p> <p>Transient signal analysis (TSA) has been proven an effective tool for motor bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, but has yet to be applied in processing bearing <span class="hlt">fault</span> signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the <span class="hlt">fault</span> characteristic frequency is exposed in the envelope spectrum for bearing <span class="hlt">fault</span> recognition. The TSAAR method can accurately estimate the phase information of the <span class="hlt">fault</span>-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJTJE..33..253L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJTJE..33..253L"><span>A Comparison of Hybrid Approaches for Turbofan Engine Gas Path <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, Feng; Wang, Yafan; Huang, Jinquan; Wang, Qihang</p> <p>2016-09-01</p> <p>A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The NN needs to be trained off-line with a large number of prior <span class="hlt">fault</span> mode data. When new <span class="hlt">fault</span> mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of turbofan engine among the algorithms discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011IJSyS..42..587H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011IJSyS..42..587H"><span>Fuzzy model-based <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> for a pilot heat exchanger</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Habbi, Hacene; Kidouche, Madjid; Kinnaert, Michel; Zelmat, Mimoun</p> <p>2011-04-01</p> <p>This article addresses the design and real-time implementation of a fuzzy model-based <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...95..158Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...95..158Y"><span>Discriminative non-negative matrix factorization (DNMF) and its application to the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of diesel engine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, Yong-sheng; Ming, An-bo; Zhang, You-yun; Zhu, Yong-sheng</p> <p>2017-10-01</p> <p>Diesel engines, widely used in engineering, are very important for the running of equipments and their <span class="hlt">fault</span> <span class="hlt">diagnosis</span> have attracted much attention. In the past several decades, the image based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods have provided efficient ways for the diesel engine <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. By introducing the class information into the traditional non-negative matrix factorization (NMF), an improved NMF algorithm named as discriminative NMF (DNMF) was developed and a novel imaged based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method was proposed by the combination of the DNMF and the KNN classifier. Experiments performed on the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of diesel engine were used to validate the efficacy of the proposed method. It is shown that the <span class="hlt">fault</span> conditions of diesel engine can be efficiently classified by the proposed method using the coefficient matrix obtained by DNMF. Compared with the original NMF (ONMF) and principle component analysis (PCA), the DNMF can represent the class information more efficiently because the class characters of basis matrices obtained by the DNMF are more visible than those in the basis matrices obtained by the ONMF and PCA.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AIPC.1955c0008W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AIPC.1955c0008W"><span>Research on bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of large machinery based on mathematical morphology</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Yu</p> <p>2018-04-01</p> <p>To study the automatic <span class="hlt">diagnosis</span> of large machinery <span class="hlt">fault</span> based on support vector machine, combining the four common <span class="hlt">faults</span> of the large machinery, the support vector machine is used to classify and identify the <span class="hlt">fault</span>. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JSV...374..297X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JSV...374..297X"><span>Fan <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on symmetrized dot pattern analysis and image matching</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xu, Xiaogang; Liu, Haixiao; Zhu, Hao; Wang, Songling</p> <p>2016-07-01</p> <p>To detect the mechanical failure of fans, a new diagnostic method based on the symmetrized dot pattern (SDP) analysis and image matching is proposed. <span class="hlt">Vibration</span> signals of 13 kinds of running states are acquired on a centrifugal fan test bed and reconstructed by the SDP technique. The SDP pattern templates of each running state are established. An image matching method is performed to diagnose the <span class="hlt">fault</span>. In order to improve the diagnostic accuracy, the single template, multiple templates and clustering <span class="hlt">fault</span> templates are used to perform the image matching.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27119052','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27119052"><span>Transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using continuous sparse autoencoder.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou</p> <p>2016-01-01</p> <p>This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer <span class="hlt">fault</span> recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer <span class="hlt">fault</span>. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5621050','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5621050"><span>An Improved Evidential-IOWA Sensor Data Fusion Approach in <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zhou, Deyun; Zhuang, Miaoyan; Fang, Xueyi; Xie, Chunhe</p> <p>2017-01-01</p> <p>As an important tool of information fusion, Dempster–Shafer evidence theory is widely applied in handling the uncertain information in <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. However, an incorrect result may be obtained if the combined evidence is highly conflicting, which may leads to failure in locating the <span class="hlt">fault</span>. To deal with the problem, an improved evidential-Induced Ordered Weighted Averaging (IOWA) sensor data fusion approach is proposed in the frame of Dempster–Shafer evidence theory. In the new method, the IOWA operator is used to determine the weight of different sensor data source, while determining the parameter of the IOWA, both the distance of evidence and the belief entropy are taken into consideration. First, based on the global distance of evidence and the global belief entropy, the α value of IOWA is obtained. Simultaneously, a weight vector is given based on the maximum entropy method model. Then, according to IOWA operator, the evidence are modified before applying the Dempster’s combination rule. The proposed method has a better performance in conflict management and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> due to the fact that the information volume of each evidence is taken into consideration. A numerical example and a case study in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are presented to show the rationality and efficiency of the proposed method. PMID:28927017</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29349378','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29349378"><span>Hand-arm <span class="hlt">vibration</span> syndrome: A rarely seen <span class="hlt">diagnosis</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Campbell, Rebecca A; Janko, Matthew R; Hacker, Robert I</p> <p>2017-06-01</p> <p>Hand-arm <span class="hlt">vibration</span> syndrome (HAVS) is a collection of sensory, vascular, and musculoskeletal symptoms caused by repetitive trauma from <span class="hlt">vibration</span>. This case report demonstrates how to diagnose HAVS on the basis of history, physical examination, and vascular imaging and its treatment options. A 41-year-old man who regularly used <span class="hlt">vibrating</span> tools presented with nonhealing wounds on his right thumb and third digit. Arteriography revealed occlusions of multiple arteries in his hand with formation of collaterals. We diagnosed HAVS, and his wounds healed after several weeks with appropriate treatment. HAVS is a debilitating condition with often irreversible vascular damage, requiring early <span class="hlt">diagnosis</span> and treatment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018IJSS...49..179H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018IJSS...49..179H"><span>Sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of singular delayed LPV systems with inexact parameters: an uncertain system approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hassanabadi, Amir Hossein; Shafiee, Masoud; Puig, Vicenc</p> <p>2018-01-01</p> <p>In this paper, sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of a singular delayed linear parameter varying (LPV) system is considered. In the considered system, the model matrices are dependent on some parameters which are real-time measurable. The case of inexact parameter measurements is considered which is close to real situations. <span class="hlt">Fault</span> <span class="hlt">diagnosis</span> in this system is achieved via <span class="hlt">fault</span> estimation. For this purpose, an augmented system is created by including sensor <span class="hlt">faults</span> as additional system states. Then, an unknown input observer (UIO) is designed which estimates both the system states and the <span class="hlt">faults</span> in the presence of measurement noise, disturbances and uncertainty induced by inexact measured parameters. Error dynamics and the original system constitute an uncertain system due to inconsistencies between real and measured values of the parameters. Then, the robust estimation of the system states and the <span class="hlt">faults</span> are achieved with H∞ performance and formulated with a set of linear matrix inequalities (LMIs). The designed UIO is also applicable for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of singular delayed LPV systems with unmeasurable scheduling variables. The efficiency of the proposed approach is illustrated with an example.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MeScT..29d5004H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MeScT..29d5004H"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> for analog circuits utilizing time-frequency features and improved VVRKFA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>He, Wei; He, Yigang; Luo, Qiwu; Zhang, Chaolong</p> <p>2018-04-01</p> <p>This paper proposes a novel scheme for analog circuit <span class="hlt">fault</span> <span class="hlt">diagnosis</span> utilizing features extracted from the time-frequency representations of signals and an improved vector-valued regularized kernel function approximation (VVRKFA). First, the cross-wavelet transform is employed to yield the energy-phase distribution of the <span class="hlt">fault</span> signals over the time and frequency domain. Since the distribution is high-dimensional, a supervised dimensionality reduction technique—the bilateral 2D linear discriminant analysis—is applied to build a concise feature set from the distributions. Finally, VVRKFA is utilized to locate the <span class="hlt">fault</span>. In order to improve the classification performance, the quantum-behaved particle swarm optimization technique is employed to gradually tune the learning parameter of the VVRKFA classifier. The experimental results for the analog circuit <span class="hlt">faults</span> classification have demonstrated that the proposed <span class="hlt">diagnosis</span> scheme has an advantage over other approaches.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29655844','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29655844"><span>On-line <span class="hlt">diagnosis</span> of inter-turn short circuit <span class="hlt">fault</span> for DC brushed motor.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhang, Jiayuan; Zhan, Wei; Ehsani, Mehrdad</p> <p>2018-06-01</p> <p>Extensive research effort has been made in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of motors and related components such as winding and ball bearing. In this paper, a new concept of inter-turn short circuit <span class="hlt">fault</span> for DC brushed motors is proposed to include the short circuit ratio and short circuit resistance. A first-principle model is derived for motors with inter-turn short circuit <span class="hlt">fault</span>. A statistical model based on Hidden Markov Model is developed for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> purpose. This new method not only allows detection of motor winding short circuit <span class="hlt">fault</span>, it can also provide estimation of the <span class="hlt">fault</span> severity, as indicated by estimation of the short circuit ratio and the short circuit resistance. The estimated <span class="hlt">fault</span> severity can be used for making appropriate decisions in response to the <span class="hlt">fault</span> condition. The feasibility of the proposed methodology is studied for inter-turn short circuit of DC brushed motors using simulation in MATLAB/Simulink environment. In addition, it is shown that the proposed methodology is reliable with the presence of small random noise in the system parameters and measurement. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017FrME...12..406K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017FrME...12..406K"><span><span class="hlt">Fault</span> feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kong, Yun; Wang, Tianyang; Li, Zheng; Chu, Fulei</p> <p>2017-09-01</p> <p>Planetary transmission plays a vital role in wind turbine drivetrains, and its <span class="hlt">fault</span> <span class="hlt">diagnosis</span> has been an important and challenging issue. Owing to the complicated and coupled <span class="hlt">vibration</span> source, time-variant <span class="hlt">vibration</span> transfer path, and heavy background noise masking effect, the <span class="hlt">vibration</span> signal of planet gear in wind turbine gearboxes exhibits several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak <span class="hlt">fault</span> feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the <span class="hlt">fault</span> detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the <span class="hlt">fault</span> feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum (SK-TWES) in the paper. Firstly, the spectral kurtosis (SK) and kurtogram of raw <span class="hlt">vibration</span> signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for the planet gear with a localized defect.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011arec.conf...35W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011arec.conf...35W"><span>The Realization of Drilling <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Hybrid Programming with Matlab and VB</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Jiangping; Hu, Yingcai</p> <p></p> <p>This paper presents a method using hybrid programming with Matlab and VB based on ActiveX to design the system of drilling accident prediction and <span class="hlt">diagnosis</span>. So that the powerful calculating function and graphical display function of Matlab and visual development interface of VB are combined fully. The main interface of the <span class="hlt">diagnosis</span> system is compiled in VB,and the analysis and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are implemented by neural network tool boxes in Matlab.The system has favorable interactive interface,and the <span class="hlt">fault</span> example validation shows that the <span class="hlt">diagnosis</span> result is feasible and can meet the demands of drilling accident prediction and <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JIEIC..99...79G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JIEIC..99...79G"><span><span class="hlt">Diagnosis</span> of Misalignment in Overhung Rotor using the K-S Statistic and A2 Test</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Garikapati, Diwakar; Pacharu, RaviKumar; Munukurthi, Rama Satya Satyanarayana</p> <p>2018-02-01</p> <p><span class="hlt">Vibration</span> measurement at the bearings of rotating machinery has become a useful technique for diagnosing incipient <span class="hlt">fault</span> conditions. In particular, <span class="hlt">vibration</span> measurement can be used to detect unbalance in rotor, bearing failure, gear problems or misalignment between a motor shaft and coupled shaft. This is a particular problem encountered in turbines, ID fans and FD fans used for power generation. For successful <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, it is important to adopt motor current signature analysis (MCSA) techniques capable of identifying the <span class="hlt">faults</span>. It is also useful to develop techniques for inferring information such as the severity of <span class="hlt">fault</span>. It is proposed that modeling the cumulative distribution function of motor current signals with respect to appropriate theoretical distributions, and quantifying the goodness of fit with the Kolmogorov-Smirnov (KS) statistic and A2 test offers a suitable signal feature for <span class="hlt">diagnosis</span>. This paper demonstrates the successful comparison of the K-S feature and A2 test for discriminating the misalignment <span class="hlt">fault</span> from normal function.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..100..743S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..100..743S"><span>Rolling bearing <span class="hlt">fault</span> feature learning using improved convolutional deep belief network with compressed sensing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shao, Haidong; Jiang, Hongkai; Zhang, Haizhou; Duan, Wenjing; Liang, Tianchen; Wu, Shuaipeng</p> <p>2018-02-01</p> <p>The <span class="hlt">vibration</span> signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative <span class="hlt">fault</span> features of the collected <span class="hlt">vibration</span> signals. In this paper, a novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling bearing. Firstly, CS is adopted for reducing the <span class="hlt">vibration</span> data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed deep model. The developed method is applied to analyze the experimental rolling bearing <span class="hlt">vibration</span> signals. The results confirm that the developed method is more effective than the traditional methods.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950009555','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950009555"><span>Sensor <span class="hlt">Fault</span> Detection and <span class="hlt">Diagnosis</span> Simulation of a Helicopter Engine in an Intelligent Control Framework</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Litt, Jonathan; Kurtkaya, Mehmet; Duyar, Ahmet</p> <p>1994-01-01</p> <p>This paper presents an application of a <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> scheme for the sensor <span class="hlt">faults</span> of a helicopter engine. The scheme utilizes a model-based approach with real time identification and hypothesis testing which can provide early detection, isolation, and <span class="hlt">diagnosis</span> of failures. It is an integral part of a proposed intelligent control system with health monitoring capabilities. The intelligent control system will allow for accommodation of <span class="hlt">faults</span>, reduce maintenance cost, and increase system availability. The scheme compares the measured outputs of the engine with the expected outputs of an engine whose sensor suite is functioning normally. If the differences between the real and expected outputs exceed threshold values, a <span class="hlt">fault</span> is detected. The isolation of sensor failures is accomplished through a <span class="hlt">fault</span> parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The <span class="hlt">fault</span> parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and <span class="hlt">diagnosis</span> of sensor <span class="hlt">faults</span> of a T700 turboshaft engine. Sensor failures are induced in a T700 nonlinear performance simulation and data obtained are used with the scheme to detect, isolate, and estimate the magnitude of the <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..372a2030Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..372a2030Z"><span>Application of improved wavelet total variation denoising for rolling bearing incipient <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, W.; Jia, M. P.</p> <p>2018-06-01</p> <p>When incipient <span class="hlt">fault</span> appear in the rolling bearing, the <span class="hlt">fault</span> feature is too small and easily submerged in the strong background noise. In this paper, wavelet total variation denoising based on kurtosis (Kurt-WATV) is studied, which can extract the incipient <span class="hlt">fault</span> feature of the rolling bearing more effectively. The proposed algorithm contains main steps: a) establish a sparse <span class="hlt">diagnosis</span> model, b) represent periodic impulses based on the redundant wavelet dictionary, c) solve the joint optimization problem by alternating direction method of multipliers (ADMM), d) obtain the reconstructed signal using kurtosis value as criterion and then select optimal wavelet subbands. This paper uses overcomplete rational-dilation wavelet transform (ORDWT) as a dictionary, and adjusts the control parameters to achieve the concentration in the time-frequency plane. Incipient <span class="hlt">fault</span> of rolling bearing is used as an example, and the result shows that the effectiveness and superiority of the proposed Kurt- WATV bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AcAau.126..517C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AcAau.126..517C"><span>Application of <span class="hlt">fault</span> factor method to <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> for space shuttle main engine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cha, Jihyoung; Ha, Chulsu; Ko, Sangho; Koo, Jaye</p> <p>2016-09-01</p> <p>This paper deals with an application of the multiple linear regression algorithm to <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> for the space shuttle main engine (SSME) during a steady state. In order to develop the algorithm, the energy balance equations, which balances the relation among pressure, mass flow rate and power at various locations within the SSME, are obtained. Then using the measurement data of some important parameters of the engine, <span class="hlt">fault</span> factors which reflects the deviation of each equation from the normal state are estimated. The probable location of each <span class="hlt">fault</span> and the levels of severity can be obtained from the estimated <span class="hlt">fault</span> factors. This process is numerically demonstrated for the SSME at 104% Rated Propulsion Level (RPL) by using the simulated measurement data from the mathematical models of the engine. The result of the current study is particularly important considering that the recently developed reusable Liquid Rocket Engines (LREs) have staged-combustion cycles similarly to the SSME.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018IJTJE..35...49X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018IJTJE..35...49X"><span>Sliding Mode <span class="hlt">Fault</span> Tolerant Control with Adaptive <span class="hlt">Diagnosis</span> for Aircraft Engines</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xiao, Lingfei; Du, Yanbin; Hu, Jixiang; Jiang, Bin</p> <p>2018-03-01</p> <p>In this paper, a novel sliding mode <span class="hlt">fault</span> tolerant control method is presented for aircraft engine systems with uncertainties and disturbances on the basis of adaptive diagnostic observer. By taking both sensors <span class="hlt">faults</span> and actuators <span class="hlt">faults</span> into account, the general model of aircraft engine control systems which is subjected to uncertainties and disturbances, is considered. Then, the corresponding augmented dynamic model is established in order to facilitate the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and <span class="hlt">fault</span> tolerant controller design. Next, a suitable detection observer is designed to detect the <span class="hlt">faults</span> effectively. Through creating an adaptive diagnostic observer and based on sliding mode strategy, the sliding mode <span class="hlt">fault</span> tolerant controller is constructed. Robust stabilization is discussed and the closed-loop system can be stabilized robustly. It is also proven that the adaptive diagnostic observer output errors and the estimations of <span class="hlt">faults</span> converge to a set exponentially, and the converge rate greater than some value which can be adjusted by choosing designable parameters properly. The simulation on a twin-shaft aircraft engine verifies the applicability of the proposed <span class="hlt">fault</span> tolerant control method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013MSSP...34..259S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013MSSP...34..259S"><span>3D fluid-structure modelling and <span class="hlt">vibration</span> analysis for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of Francis turbine using multiple ANN and multiple ANFIS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Saeed, R. A.; Galybin, A. N.; Popov, V.</p> <p>2013-01-01</p> <p>This paper discusses condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in Francis turbine based on integration of numerical modelling with several different artificial intelligence (AI) techniques. In this study, a numerical approach for fluid-structure (turbine runner) analysis is presented. The results of numerical analysis provide frequency response functions (FRFs) data sets along x-, y- and z-directions under different operating load and different position and size of <span class="hlt">faults</span> in the structure. To extract features and reduce the dimensionality of the obtained FRF data, the principal component analysis (PCA) has been applied. Subsequently, the extracted features are formulated and fed into multiple artificial neural networks (ANN) and multiple adaptive neuro-fuzzy inference systems (ANFIS) in order to identify the size and position of the damage in the runner and estimate the turbine operating conditions. The results demonstrated the effectiveness of this approach and provide satisfactory accuracy even when the input data are corrupted with certain level of noise.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018E3SWC..3602007P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018E3SWC..3602007P"><span>The influence of the <span class="hlt">fault</span> zone width on land surface <span class="hlt">vibrations</span> after the high-energy tremor in the "Rydułtowy-Anna" hard coal mine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pilecka, Elżbieta; Szwarkowski, Dariusz</p> <p>2018-04-01</p> <p>In the article, a numerical analysis of the impact of the width of the <span class="hlt">fault</span> zone on land surface tremors on the area of the "Rydułtowy - Anna" hard coal mine was performed. The analysis covered the dynamic impact of the actual seismic wave after the high-energy tremor of 7 June 2013. <span class="hlt">Vibrations</span> on the land surface are a measure of the mining damage risk. It is particularly the horizontal components of land <span class="hlt">vibrations</span> that are dangerous to buildings which is reflected in the Mining Scales of Intensity (GSI) of <span class="hlt">vibrations</span>. The run of a seismic wave in the rock mass from the hypocenter to the area's surface depends on the lithology of the area and the presence of <span class="hlt">fault</span> zones. The rock mass network cut by <span class="hlt">faults</span> of various widths influences the amplitude of tremor reaching the area's surface. The analysis of the impact of the width of the <span class="hlt">fault</span> zone was done for three alternatives.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27000630','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27000630"><span>The use of SESK as a trend parameter for localized bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in induction machines.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Saidi, Lotfi; Ben Ali, Jaouher; Benbouzid, Mohamed; Bechhoefer, Eric</p> <p>2016-07-01</p> <p>A critical work of bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing. However, the main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with the highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In this paper, we present a squared envelope based spectral kurtosis method to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. We have verified the potential of the spectral kurtosis diagnostic strategy in performance improvements for single-defect <span class="hlt">diagnosis</span> using real laboratory-collected <span class="hlt">vibration</span> data sets. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MeScT..28i5008L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MeScT..28i5008L"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of rolling element bearings with a spectrum searching method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Wei; Qiu, Mingquan; Zhu, Zhencai; Jiang, Fan; Zhou, Gongbo</p> <p>2017-09-01</p> <p>Rolling element bearing <span class="hlt">faults</span> in rotating systems are observed as impulses in the <span class="hlt">vibration</span> signals, which are usually buried in noise. In order to effectively detect <span class="hlt">faults</span> in bearings, a novel spectrum searching method is proposed in this paper. The structural information of the spectrum (SIOS) on a predefined frequency grid is constructed through a searching algorithm, such that the harmonics of the impulses generated by <span class="hlt">faults</span> can be clearly identified and analyzed. Local peaks of the spectrum are projected onto certain components of the frequency grid, and then the SIOS can interpret the spectrum via the number and power of harmonics projected onto components of the frequency grid. Finally, bearings can be diagnosed based on the SIOS by identifying its dominant or significant components. The mathematical formulation is developed to guarantee the correct construction of the SIOS through searching. The effectiveness of the proposed method is verified with both simulated and experimental bearing signals.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27754386','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27754386"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Chemical Sensor Data with an Active Deep Neural Network.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng</p> <p>2016-10-13</p> <p>Big sensor data provide significant potential for chemical <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative <span class="hlt">fault</span> characteristics for <span class="hlt">diagnosis</span> in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of <span class="hlt">diagnosis</span> model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior <span class="hlt">diagnosis</span> accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011TRACE..13...15N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011TRACE..13...15N"><span><span class="hlt">Fault</span> Detection and <span class="hlt">Diagnosis</span> System for the Air-conditioning</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nakahara, Nobuo</p> <p></p> <p>The <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> system, the FDD system, for the HVAC was initiated around the middle of 1970s in Japan but it still remains at the elementary stage. The HVAC is really one of the most complicated and large scaled system for the FDD system. Besides, the maintenance engineering was never focussed as the target of the academic study since after the war, but the FDD system for some kinds of the components and subsystems has been developed for the sake of the practical industrial needs. Recently, international cooperative study in the IEA Annex 25 on the energy conservation for the building and community targetted on the BOFD, the building optimization, <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span>. Not a few academic peaple from various engineering field got interested and, moreover, some national projects seem to start in the European countries. The author has reviewed the state of the art of the FDD and BO as well based on the references and the experience at the IEA study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26556358','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26556358"><span>State Tracking and <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> for Dynamic Systems Using Labeled Uncertainty Graph.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhou, Gan; Feng, Wenquan; Zhao, Qi; Zhao, Hongbo</p> <p>2015-11-05</p> <p>Cyber-physical systems such as autonomous spacecraft, power plants and automotive systems become more vulnerable to unanticipated failures as their complexity increases. Accurate tracking of system dynamics and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are essential. This paper presents an efficient state estimation method for dynamic systems modeled as concurrent probabilistic automata. First, the Labeled Uncertainty Graph (LUG) method in the planning domain is introduced to describe the state tracking and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> processes. Because the system model is probabilistic, the Monte Carlo technique is employed to sample the probability distribution of belief states. In addition, to address the sample impoverishment problem, an innovative look-ahead technique is proposed to recursively generate most likely belief states without exhaustively checking all possible successor modes. The overall algorithms incorporate two major steps: a roll-forward process that estimates system state and identifies <span class="hlt">faults</span>, and a roll-backward process that analyzes possible system trajectories once the <span class="hlt">faults</span> have been detected. We demonstrate the effectiveness of this approach by applying it to a real world domain: the power supply control unit of a spacecraft.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...84..642L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...84..642L"><span><span class="hlt">Fault</span> detection method for railway wheel flat using an adaptive multiscale morphological filter</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Yifan; Zuo, Ming J.; Lin, Jianhui; Liu, Jianxin</p> <p>2017-02-01</p> <p>This study explores the capacity of the morphology analysis for railway wheel flat <span class="hlt">fault</span> detection. A dynamic model of vehicle systems with 56 degrees of freedom was set up along with a wheel flat model to calculate the dynamic responses of axle box. The vehicle axle box <span class="hlt">vibration</span> signal is complicated because it not only contains the information of wheel defect, but also includes track condition information. Thus, how to extract the influential features of wheels from strong background noise effectively is a typical key issue for railway wheel <span class="hlt">fault</span> detection. In this paper, an algorithm for adaptive multiscale morphological filtering (AMMF) was proposed, and its effect was evaluated by a simulated signal. And then this algorithm was employed to study the axle box <span class="hlt">vibration</span> caused by wheel flats, as well as the influence of track irregularity and vehicle running speed on <span class="hlt">diagnosis</span> results. Finally, the effectiveness of the proposed method was verified by bench testing. Research results demonstrate that the AMMF extracts the influential characteristic of axle box <span class="hlt">vibration</span> signals effectively and can diagnose wheel flat <span class="hlt">faults</span> in real time.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19880047636&hterms=aircraft+diagnosis+expert+system&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Daircraft%2Bdiagnosis%2Bexpert%2Bsystem','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19880047636&hterms=aircraft+diagnosis+expert+system&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Daircraft%2Bdiagnosis%2Bexpert%2Bsystem"><span>An evaluation of a real-time <span class="hlt">fault</span> <span class="hlt">diagnosis</span> expert system for aircraft applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schutte, Paul C.; Abbott, Kathy H.; Palmer, Michael T.; Ricks, Wendell R.</p> <p>1987-01-01</p> <p>A <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in <span class="hlt">fault</span> monitoring, <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, and recovery planning. The present implementation of this concept performs monitoring and <span class="hlt">diagnosis</span> for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...82..461M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...82..461M"><span>Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to <span class="hlt">vibration</span> <span class="hlt">fault</span> detection</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McDonald, Geoff L.; Zhao, Qing</p> <p>2017-01-01</p> <p>Minimum Entropy Deconvolution (MED) has been applied successfully to rotating machine <span class="hlt">fault</span> detection from <span class="hlt">vibration</span> data, however this method has limitations. A convolution adjustment to the MED definition and solution is proposed in this paper to address the discontinuity at the start of the signal - in some cases causing spurious impulses to be erroneously deconvolved. A problem with the MED solution is that it is an iterative selection process, and will not necessarily design an optimal filter for the posed problem. Additionally, the problem goal in MED prefers to deconvolve a single-impulse, while in rotating machine <span class="hlt">faults</span> we expect one impulse-like <span class="hlt">vibration</span> source per rotational period of the faulty element. Maximum Correlated Kurtosis Deconvolution was proposed to address some of these problems, and although it solves the target goal of multiple periodic impulses, it is still an iterative non-optimal solution to the posed problem and only solves for a limited set of impulses in a row. Ideally, the problem goal should target an impulse train as the output goal, and should directly solve for the optimal filter in a non-iterative manner. To meet these goals, we propose a non-iterative deconvolution approach called Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). MOMEDA proposes a deconvolution problem with an infinite impulse train as the goal and the optimal filter solution can be solved for directly. From experimental data on a gearbox with and without a gear tooth chip, we show that MOMEDA and its deconvolution spectrums according to the period between the impulses can be used to detect <span class="hlt">faults</span> and study the health of rotating machine elements effectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1995MSSP....9..527J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1995MSSP....9..527J"><span>High pressure air compressor valve <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using feedforward neural networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>James Li, C.; Yu, Xueli</p> <p>1995-09-01</p> <p>Feedforward neural networks (FNNs) are developed and implemented to classify a four-stage high pressure air compressor into one of the following conditions: baseline, suction or exhaust valve <span class="hlt">faults</span>. These FNNs are used for the compressor's automatic condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Measurements of 39 variables are obtained under different baseline conditions and third-stage suction and exhaust valve <span class="hlt">faults</span>. These variables include pressures and temperatures at all stages, voltage between phase aand phase b, voltage between phase band phase c, total three-phase real power, cooling water flow rate, etc. To reduce the number of variables, the amount of their discriminatory information is quantified by scattering matrices to identify statistical significant ones. Measurements of the selected variables are then used by a fully automatic structural and weight learning algorithm to construct three-layer FNNs to classify the compressor's condition. This learning algorithm requires neither guesses of initial weight values nor number of neurons in the hidden layer of an FNN. It takes an incremental approach in which a hidden neuron is trained by exemplars and then augmented to the existing network. These exemplars are then made orthogonal to the newly identified hidden neuron. They are subsequently used for the training of the next hidden neuron. The betterment continues until a desired accuracy is reached. After the neural networks are established, novel measurements from various conditions that haven't been previously seen by the FNNs are then used to evaluate their ability in <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The trained neural networks provide very accurate <span class="hlt">diagnosis</span> for suction and discharge valve defects.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...72..105C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...72..105C"><span><span class="hlt">Fault</span> detection in rotor bearing systems using time frequency techniques</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chandra, N. Harish; Sekhar, A. S.</p> <p>2016-05-01</p> <p><span class="hlt">Faults</span> such as misalignment, rotor cracks and rotor to stator rub can exist collectively in rotor bearing systems. It is an important task for rotor dynamic personnel to monitor and detect <span class="hlt">faults</span> in rotating machinery. In this paper, the rotor startup <span class="hlt">vibrations</span> are utilized to solve the <span class="hlt">fault</span> identification problem using time frequency techniques. Numerical simulations are performed through finite element analysis of the rotor bearing system with individual and collective combinations of <span class="hlt">faults</span> as mentioned above. Three signal processing tools namely Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Hilbert Huang Transform (HHT) are compared to evaluate their detection performance. The effect of addition of Signal to Noise ratio (SNR) on three time frequency techniques is presented. The comparative study is focused towards detecting the least possible level of the <span class="hlt">fault</span> induced and the computational time consumed. The computation time consumed by HHT is very less when compared to CWT based <span class="hlt">diagnosis</span>. However, for noisy data CWT is more preferred over HHT. To identify <span class="hlt">fault</span> characteristics using wavelets a procedure to adjust resolution of the mother wavelet is presented in detail. Experiments are conducted to obtain the run-up data of a rotor bearing setup for <span class="hlt">diagnosis</span> of shaft misalignment and rotor stator rubbing <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MeScT..28c5003F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MeScT..28c5003F"><span>A diagnostic signal selection scheme for planetary gearbox <span class="hlt">vibration</span> monitoring under non-stationary operational conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Feng, Ke; Wang, KeSheng; Zhang, Mian; Ni, Qing; Zuo, Ming J.</p> <p>2017-03-01</p> <p>The planetary gearbox, due to its unique mechanical structures, is an important rotating machine for transmission systems. Its engineering applications are often in non-stationary operational conditions, such as helicopters, wind energy systems, etc. The unique physical structures and working conditions make the <span class="hlt">vibrations</span> measured from planetary gearboxes exhibit a complex time-varying modulation and therefore yield complicated spectral structures. As a result, traditional signal processing methods, such as Fourier analysis, and the selection of characteristic <span class="hlt">fault</span> frequencies for <span class="hlt">diagnosis</span> face serious challenges. To overcome this drawback, this paper proposes a signal selection scheme for <span class="hlt">fault</span>-emphasized diagnostics based upon two order tracking techniques. The basic procedures for the proposed scheme are as follows. (1) Computed order tracking is applied to reveal the order contents and identify the order(s) of interest. (2) Vold-Kalman filter order tracking is used to extract the order(s) of interest—these filtered order(s) constitute the so-called selected <span class="hlt">vibrations</span>. (3) Time domain statistic indicators are applied to the selected <span class="hlt">vibrations</span> for faulty information-emphasized diagnostics. The proposed scheme is explained and demonstrated in a signal simulation model and experimental studies and the method proves to be effective for planetary gearbox <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5087483','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5087483"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Chemical Sensor Data with an Active Deep Neural Network</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng</p> <p>2016-01-01</p> <p>Big sensor data provide significant potential for chemical <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative <span class="hlt">fault</span> characteristics for <span class="hlt">diagnosis</span> in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of <span class="hlt">diagnosis</span> model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior <span class="hlt">diagnosis</span> accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods. PMID:27754386</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29316668','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29316668"><span>Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Wind Turbine Planetary Gearbox under Nonstationary Conditions.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hu, Yue; Tu, Xiaotong; Li, Fucai; Meng, Guang</p> <p>2018-01-07</p> <p>Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the <span class="hlt">vibration</span> sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the <span class="hlt">fault</span> component may be masked by other unrelated components. The MTEWT is used to separate the <span class="hlt">fault</span> feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5795751','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5795751"><span>Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Wind Turbine Planetary Gearbox under Nonstationary Conditions</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Li, Fucai; Meng, Guang</p> <p>2018-01-01</p> <p>Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the <span class="hlt">vibration</span> sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the <span class="hlt">fault</span> component may be masked by other unrelated components. The MTEWT is used to separate the <span class="hlt">fault</span> feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing <span class="hlt">faults</span>. PMID:29316668</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5539661','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5539661"><span>Unsupervised <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of a Gear Transmission Chain Using a Deep Belief Network</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>He, Jun; Yang, Shixi; Gan, Chunbiao</p> <p>2017-01-01</p> <p>Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of <span class="hlt">fault</span> data and automatically provide accurate <span class="hlt">diagnosis</span> results, have been widely applied to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the <span class="hlt">faults</span> by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The <span class="hlt">fault</span> classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods. PMID:28677638</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28677638','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28677638"><span>Unsupervised <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of a Gear Transmission Chain Using a Deep Belief Network.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>He, Jun; Yang, Shixi; Gan, Chunbiao</p> <p>2017-07-04</p> <p>Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of <span class="hlt">fault</span> data and automatically provide accurate <span class="hlt">diagnosis</span> results, have been widely applied to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the <span class="hlt">faults</span> by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The <span class="hlt">fault</span> classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3292134','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3292134"><span>Intelligent Gearbox <span class="hlt">Diagnosis</span> Methods Based on SVM, Wavelet Lifting and RBR</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng</p> <p>2010-01-01</p> <p>Given the problems in intelligent gearbox <span class="hlt">diagnosis</span> methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same <span class="hlt">fault</span>; moreover, the <span class="hlt">fault</span> features can also vary. Therefore, a SVM could be used for the initial <span class="hlt">diagnosis</span>. First, gearbox <span class="hlt">vibration</span> signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the <span class="hlt">fault</span>; thus effectively extracting the <span class="hlt">fault</span> frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed <span class="hlt">fault</span> type. Results have shown that SVM is a powerful tool to accomplish gearbox <span class="hlt">fault</span> pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract <span class="hlt">fault</span> features, and rule-based reasoning can be used to identify the detailed <span class="hlt">fault</span> type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. PMID:22399894</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22399894','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22399894"><span>Intelligent gearbox <span class="hlt">diagnosis</span> methods based on SVM, wavelet lifting and RBR.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng</p> <p>2010-01-01</p> <p>Given the problems in intelligent gearbox <span class="hlt">diagnosis</span> methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same <span class="hlt">fault</span>; moreover, the <span class="hlt">fault</span> features can also vary. Therefore, a SVM could be used for the initial <span class="hlt">diagnosis</span>. First, gearbox <span class="hlt">vibration</span> signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the <span class="hlt">fault</span>; thus effectively extracting the <span class="hlt">fault</span> frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed <span class="hlt">fault</span> type. Results have shown that SVM is a powerful tool to accomplish gearbox <span class="hlt">fault</span> pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract <span class="hlt">fault</span> features, and rule-based reasoning can be used to identify the detailed <span class="hlt">fault</span> type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29364856','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29364856"><span>New <span class="hlt">Fault</span> Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jiang, Quansheng; Shen, Yehu; Li, Hua; Xu, Fengyu</p> <p>2018-01-24</p> <p>Feature recognition and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the <span class="hlt">vibration</span> signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in <span class="hlt">vibration</span> signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the <span class="hlt">fault</span> signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the <span class="hlt">fault</span> features. The experimental results on simulated <span class="hlt">fault</span> signals verified better performances of our proposed approach. In real two-span rotor data, the <span class="hlt">fault</span> detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective <span class="hlt">fault</span> recognition method for rotating machinery.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..100..662S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..100..662S"><span>Rolling element bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on Over-Complete rational dilation wavelet transform and auto-correlation of analytic energy operator</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Singh, Jaskaran; Darpe, A. K.; Singh, S. P.</p> <p>2018-02-01</p> <p>Local damage in rolling element bearings usually generates periodic impulses in <span class="hlt">vibration</span> signals. The severity, repetition frequency and the <span class="hlt">fault</span> excited resonance zone by these impulses are the key indicators for diagnosing bearing <span class="hlt">faults</span>. In this paper, a methodology based on over complete rational dilation wavelet transform (ORDWT) is proposed, as it enjoys a good shift invariance. ORDWT offers flexibility in partitioning the frequency spectrum to generate a number of subbands (filters) with diverse bandwidths. The selection of the optimal filter that perfectly overlaps with the bearing <span class="hlt">fault</span> excited resonance zone is based on the maximization of a proposed impulse detection measure "Temporal energy operated auto correlated kurtosis". The proposed indicator is robust and consistent in evaluating the impulsiveness of <span class="hlt">fault</span> signals in presence of interfering <span class="hlt">vibration</span> such as heavy background noise or sporadic shocks unrelated to the <span class="hlt">fault</span> or normal operation. The structure of the proposed indicator enables it to be sensitive to <span class="hlt">fault</span> severity. For enhanced <span class="hlt">fault</span> classification, an autocorrelation of the energy time series of the signal filtered through the optimal subband is proposed. The application of the proposed methodology is validated on simulated and experimental data. The study shows that the performance of the proposed technique is more robust and consistent in comparison to the original fast kurtogram and wavelet kurtogram.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012PhDT.......209A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012PhDT.......209A"><span>Sliding Mode Approaches for Robust Control, State Estimation, Secure Communication, and <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Nuclear Systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ablay, Gunyaz</p> <p></p> <p>Using traditional control methods for controller design, parameter estimation and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> may lead to poor results with nuclear systems in practice because of approximations and uncertainties in the system models used, possibly resulting in unexpected plant unavailability. This experience has led to an interest in development of robust control, estimation and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods. One particularly robust approach is the sliding mode control methodology. Sliding mode approaches have been of great interest and importance in industry and engineering in the recent decades due to their potential for producing economic, safe and reliable designs. In order to utilize these advantages, sliding mode approaches are implemented for robust control, state estimation, secure communication and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in nuclear plant systems. In addition, a sliding mode output observer is developed for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in dynamical systems. To validate the effectiveness of the methodologies, several nuclear plant system models are considered for applications, including point reactor kinetics, xenon concentration dynamics, an uncertain pressurizer model, a U-tube steam generator model and a coupled nonlinear nuclear reactor model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013MSSP...41..141M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013MSSP...41..141M"><span>Spectrum auto-correlation analysis and its application to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ming, A. B.; Qin, Z. Y.; Zhang, W.; Chu, F. L.</p> <p>2013-12-01</p> <p>Bearing failure is one of the most common reasons of machine breakdowns and accidents. Therefore, the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings is of great significance to the safe and efficient operation of machines owing to its <span class="hlt">fault</span> indication and accident prevention capability in engineering applications. Based on the orthogonal projection theory, a novel method is proposed to extract the <span class="hlt">fault</span> characteristic frequency for the incipient <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings in this paper. With the capability of exposing the oscillation frequency of the signal energy, the proposed method is a generalized form of the squared envelope analysis and named as spectral auto-correlation analysis (SACA). Meanwhile, the SACA is a simplified form of the cyclostationary analysis as well and can be iteratively carried out in applications. Simulations and experiments are used to evaluate the efficiency of the proposed method. Comparing the results of SACA, the traditional envelope analysis and the squared envelope analysis, it is found that the result of SACA is more legible due to the more prominent harmonic amplitudes of the <span class="hlt">fault</span> characteristic frequency and that the SACA with the proper iteration will further enhance the <span class="hlt">fault</span> features.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4062996','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4062996"><span>Wayside Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang</p> <p>2014-01-01</p> <p>A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing <span class="hlt">faults</span>, the transient model analysis method is employed to detect localized bearing <span class="hlt">faults</span> after the embedded Doppler effect is eliminated. The effectiveness of the proposed <span class="hlt">fault</span> <span class="hlt">diagnosis</span> strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27993356','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27993356"><span>Multiple incipient sensor <span class="hlt">faults</span> <span class="hlt">diagnosis</span> with application to high-speed railway traction devices.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wu, Yunkai; Jiang, Bin; Lu, Ningyun; Yang, Hao; Zhou, Yang</p> <p>2017-03-01</p> <p>This paper deals with the problem of incipient <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable <span class="hlt">fault</span> information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor <span class="hlt">faults</span>, while the second subsystem contains sensor <span class="hlt">faults</span> but without any system disturbances. Sensor <span class="hlt">faults</span> in the second subsystem are then formed as actuator <span class="hlt">faults</span> by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor <span class="hlt">faults</span> can be detected by constructing ToMFIR, and the <span class="hlt">fault</span> detectability condition is then derived for discriminating the detectable incipient sensor <span class="hlt">faults</span>. Further, a sliding-mode observers (SMOs) based <span class="hlt">fault</span> isolation scheme is designed to guarantee accurate isolation of multiple sensor <span class="hlt">faults</span>. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1168761-editorial-mathematical-methods-modeling-machine-fault-diagnosis','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1168761-editorial-mathematical-methods-modeling-machine-fault-diagnosis"><span>Editorial: Mathematical Methods and Modeling in Machine <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; ...</p> <p>2014-12-18</p> <p>Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine <span class="hlt">fault</span> <span class="hlt">diagnosis</span> through applications of the modern mathematical methods.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100002237','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100002237"><span>Hypothetical Scenario Generator for <span class="hlt">Fault</span>-Tolerant <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>James, Mark</p> <p>2007-01-01</p> <p>The Hypothetical Scenario Generator for <span class="hlt">Fault</span>-tolerant Diagnostics (HSG) is an algorithm being developed in conjunction with other components of artificial- intelligence systems for automated <span class="hlt">diagnosis</span> and prognosis of <span class="hlt">faults</span> in spacecraft, aircraft, and other complex engineering systems. By incorporating prognostic capabilities along with advanced diagnostic capabilities, these developments hold promise to increase the safety and affordability of the affected engineering systems by making it possible to obtain timely and accurate information on the statuses of the systems and predicting impending failures well in advance. The HSG is a specific instance of a hypothetical- scenario generator that implements an innovative approach for performing diagnostic reasoning when data are missing. The special purpose served by the HSG is to (1) look for all possible ways in which the present state of the engineering system can be mapped with respect to a given model and (2) generate a prioritized set of future possible states and the scenarios of which they are parts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24434125','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24434125"><span>A data-driven multiplicative <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approach for automation processes.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hao, Haiyang; Zhang, Kai; Ding, Steven X; Chen, Zhiwen; Lei, Yaguo</p> <p>2014-09-01</p> <p>This paper presents a new data-driven method for diagnosing multiplicative key performance degradation in automation processes. Different from the well-established additive <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approaches, the proposed method aims at identifying those low-level components which increase the variability of process variables and cause performance degradation. Based on process data, features of multiplicative <span class="hlt">fault</span> are extracted. To identify the root cause, the impact of <span class="hlt">fault</span> on each process variable is evaluated in the sense of contribution to performance degradation. Then, a numerical example is used to illustrate the functionalities of the method and Monte-Carlo simulation is performed to demonstrate the effectiveness from the statistical viewpoint. Finally, to show the practical applicability, a case study on the Tennessee Eastman process is presented. Copyright © 2013. Published by Elsevier Ltd.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25701084','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25701084"><span>Work disability after <span class="hlt">diagnosis</span> of hand-arm <span class="hlt">vibration</span> syndrome.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sauni, Riitta; Toivio, Pauliina; Pääkkönen, Rauno; Malmström, Jari; Uitti, Jukka</p> <p>2015-11-01</p> <p>Our aim was to study the course of vasospastic and sensorineural symptoms after the clinical <span class="hlt">diagnosis</span> of hand-arm <span class="hlt">vibration</span> syndrome (HAVS), and the association of current HAVS symptoms with occupational status, self-evaluation of health, quality of life, and work ability. We gathered all HAVS cases diagnosed at the Finnish Institute of Occupational Health in Helsinki and Tampere during 1990-2008. A questionnaire was sent to all these patients (n = 241). Altogether 149 of them (62 %) returned the questionnaire. Cumulative lifelong <span class="hlt">vibration</span> exposure was evaluated on the basis of the data in the patient files. On average, 8.5 years after the <span class="hlt">diagnosis</span> of HAVS, approximately one-third of the patients reported improvement in symptoms of <span class="hlt">vibration</span>-induced white finger (VWF) and the sensorineural symptoms. Young age and shorter exposure time were associated with improvement in VWF symptoms (p = 0.033 and p < 0.001, respectively). Persistent or deteriorated symptoms of both VWF and sensorineural symptoms were associated with lowered work ability, quality of life (EQ-5D), and general health, also after adjusting for age, smoking, and diseases other than HAVS. The patients' own prediction of work ability in 2 years was more negative if the VWF symptoms or sensorineural symptoms had continued after <span class="hlt">diagnosis</span> of HAVS (p = 0.065 and p = 0.001, respectively). Our results suggest that in about two-thirds of the patients, the HAVS symptoms may stabilize or deteriorate in the follow-up. Considering the effects on work ability, timely prevention measures should be taken more actively to help patients continue their working careers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MeScT..28f5012W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MeScT..28f5012W"><span>A computer-vision-based rotating speed estimation method for motor bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Xiaoxian; Guo, Jie; Lu, Siliang; Shen, Changqing; He, Qingbo</p> <p>2017-06-01</p> <p><span class="hlt">Diagnosis</span> of motor bearing <span class="hlt">faults</span> under variable speed is a problem. In this study, a new computer-vision-based order tracking method is proposed to address this problem. First, a video recorded by a high-speed camera is analyzed with the speeded-up robust feature extraction and matching algorithm to obtain the instantaneous rotating speed (IRS) of the motor. Subsequently, an audio signal recorded by a microphone is equi-angle resampled for order tracking in accordance with the IRS curve, through which the frequency-domain signal is transferred to an angular-domain one. The envelope order spectrum is then calculated to determine the <span class="hlt">fault</span> characteristic order, and finally the bearing <span class="hlt">fault</span> pattern is determined. The effectiveness and robustness of the proposed method are verified with two brushless direct-current motor test rigs, in which two defective bearings and a healthy bearing are tested separately. This study provides a new noninvasive measurement approach that simultaneously avoids the installation of a tachometer and overcomes the disadvantages of tacholess order tracking methods for motor bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under variable speed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948518','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948518"><span>Intelligent <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan</p> <p>2018-01-01</p> <p>Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the <span class="hlt">fault</span> of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 <span class="hlt">fault</span> types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose <span class="hlt">fault</span> using the same data. The best <span class="hlt">fault</span> <span class="hlt">diagnosis</span> accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of delta 3D printers. PMID:29690641</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29690641','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29690641"><span>Intelligent <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan</p> <p>2018-04-23</p> <p>Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the <span class="hlt">fault</span> of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 <span class="hlt">fault</span> types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose <span class="hlt">fault</span> using the same data. The best <span class="hlt">fault</span> <span class="hlt">diagnosis</span> accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of delta 3D printers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017InPhT..83..182J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017InPhT..83..182J"><span>Scheme for predictive <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in photo-voltaic modules using thermal imaging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jaffery, Zainul Abdin; Dubey, Ashwani Kumar; Irshad; Haque, Ahteshamul</p> <p>2017-06-01</p> <p>Degradation of PV modules can cause excessive overheating which results in a reduced power output and eventually failure of solar panel. To maintain the long term reliability of solar modules and maximize the power output, <span class="hlt">faults</span> in modules need to be diagnosed at an early stage. This paper provides a comprehensive algorithm for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in solar modules using infrared thermography. Infrared Thermography (IRT) is a reliable, non-destructive, fast and cost effective technique which is widely used to identify where and how <span class="hlt">faults</span> occurred in an electrical installation. Infrared images were used for condition monitoring of solar modules and fuzzy logic have been used to incorporate intelligent classification of <span class="hlt">faults</span>. An automatic approach has been suggested for <span class="hlt">fault</span> detection, classification and analysis. IR images were acquired using an IR camera. To have an estimation of thermal condition of PV module, the faulty panel images were compared to a healthy PV module thermal image. A fuzzy rule-base was used to classify <span class="hlt">faults</span> automatically. Maintenance actions have been advised based on type of <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3892877','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3892877"><span>Reliability of Measured Data for pH Sensor Arrays with <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> and Data Fusion Based on LabVIEW</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi</p> <p>2013-01-01</p> <p><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. <span class="hlt">Fault</span> <span class="hlt">diagnosis</span> is used to avoid sensor <span class="hlt">faults</span> and to measure errors in the electrochemical measurement system, therefore, in this study, we use <span class="hlt">fault</span> <span class="hlt">diagnosis</span> to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study. PMID:24351636</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24351636','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24351636"><span>Reliability of measured data for pH sensor arrays with <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and data fusion based on LabVIEW.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi</p> <p>2013-12-13</p> <p><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. <span class="hlt">Fault</span> <span class="hlt">diagnosis</span> is used to avoid sensor <span class="hlt">faults</span> and to measure errors in the electrochemical measurement system, therefore, in this study, we use <span class="hlt">fault</span> <span class="hlt">diagnosis</span> to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1995nasa.reptS....B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1995nasa.reptS....B"><span>Accelerometer having integral <span class="hlt">fault</span> null</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bozeman, Richard J., Jr.</p> <p>1995-08-01</p> <p>An improved accelerometer is introduced. It comprises a transducer responsive to <span class="hlt">vibration</span> in machinery which produces an electrical signal related to the magnitude and frequency of the <span class="hlt">vibration</span>; and a decoding circuit responsive to the transducer signal which produces a first <span class="hlt">fault</span> signal to produce a second <span class="hlt">fault</span> signal in which ground shift effects are nullified.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19960002678','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19960002678"><span>Accelerometer having integral <span class="hlt">fault</span> null</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bozeman, Richard J., Jr. (Inventor)</p> <p>1995-01-01</p> <p>An improved accelerometer is introduced. It comprises a transducer responsive to <span class="hlt">vibration</span> in machinery which produces an electrical signal related to the magnitude and frequency of the <span class="hlt">vibration</span>; and a decoding circuit responsive to the transducer signal which produces a first <span class="hlt">fault</span> signal to produce a second <span class="hlt">fault</span> signal in which ground shift effects are nullified.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100023448','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100023448"><span>Methods for Probabilistic <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span>: An Electrical Power System Case Study</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ricks, Brian W.; Mengshoel, Ole J.</p> <p>2009-01-01</p> <p>Health management systems that more accurately and quickly diagnose <span class="hlt">faults</span> that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the <span class="hlt">diagnosis</span> of abrupt continuous (or parametric) <span class="hlt">faults</span> within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on <span class="hlt">diagnosis</span> of abrupt discrete <span class="hlt">faults</span>. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ChJME..30.1296L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ChJME..30.1296L"><span>An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Induction Motors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, De Z.; Wang, Wilson; Ismail, Fathy</p> <p>2017-11-01</p> <p>Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM <span class="hlt">fault</span> at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity <span class="hlt">fault</span> detection in IMs. The <span class="hlt">fault</span> harmonic series are synthesized to enhance <span class="hlt">fault</span> features. <span class="hlt">Fault</span> related local spectra are processed to derive <span class="hlt">fault</span> indicators for IM air-gap eccentricity <span class="hlt">diagnosis</span>. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract <span class="hlt">fault</span> features effectively for initial IM air-gap eccentricity <span class="hlt">fault</span> detection.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27472926','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27472926"><span>A combined approach of generalized additive model and bootstrap with small sample sets for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in fermentation process of glutamate.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Chunbo; Pan, Feng; Li, Yun</p> <p>2016-07-29</p> <p>Glutamate is of great importance in food and pharmaceutical industries. There is still lack of effective statistical approaches for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in the fermentation process of glutamate. To date, the statistical approach based on generalized additive model (GAM) and bootstrap has not been used for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in fermentation processes, much less the fermentation process of glutamate with small samples sets. A combined approach of GAM and bootstrap was developed for the online <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in the fermentation process of glutamate with small sample sets. GAM was first used to model the relationship between glutamate production and different fermentation parameters using online data from four normal fermentation experiments of glutamate. The fitted GAM with fermentation time, dissolved oxygen, oxygen uptake rate and carbon dioxide evolution rate captured 99.6 % variance of glutamate production during fermentation process. Bootstrap was then used to quantify the uncertainty of the estimated production of glutamate from the fitted GAM using 95 % confidence interval. The proposed approach was then used for the online <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in the abnormal fermentation processes of glutamate, and a <span class="hlt">fault</span> was defined as the estimated production of glutamate fell outside the 95 % confidence interval. The online <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on the proposed approach identified not only the start of the <span class="hlt">fault</span> in the fermentation process, but also the end of the <span class="hlt">fault</span> when the fermentation conditions were back to normal. The proposed approach only used a small sample sets from normal fermentations excitements to establish the approach, and then only required online recorded data on fermentation parameters for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in the fermentation process of glutamate. The proposed approach based on GAM and bootstrap provides a new and effective way for the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in the fermentation process of glutamate with small sample sets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19890027974&hterms=knowledge+power&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dknowledge%2Bpower','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19890027974&hterms=knowledge+power&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dknowledge%2Bpower"><span>Development of a component centered <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> knowledge based system for space power system</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lee, S. C.; Lollar, Louis F.</p> <p>1988-01-01</p> <p>The overall approach currently being taken in the development of AMPERES (Autonomously Managed Power System Extendable Real-time Expert System), a knowledge-based expert system for <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> of space power systems, is discussed. The system architecture, knowledge representation, and <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> strategy are examined. A 'component-centered' approach developed in this project is described. Critical issues requiring further study are identified.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29642466','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29642466"><span>Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Duong, Bach Phi; Kim, Jong-Myon</p> <p>2018-04-07</p> <p>The simultaneous occurrence of various types of defects in bearings makes their <span class="hlt">diagnosis</span> more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined <span class="hlt">faults</span> in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single <span class="hlt">fault</span> and it classifies both single <span class="hlt">faults</span> and multiple combined <span class="hlt">faults</span>. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> performance.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948782','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948782"><span>Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kim, Jong-Myon</p> <p>2018-01-01</p> <p>The simultaneous occurrence of various types of defects in bearings makes their <span class="hlt">diagnosis</span> more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined <span class="hlt">faults</span> in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single <span class="hlt">fault</span> and it classifies both single <span class="hlt">faults</span> and multiple combined <span class="hlt">faults</span>. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> performance. PMID:29642466</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22902083','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22902083"><span><span class="hlt">Fault</span> detection and <span class="hlt">diagnosis</span> for non-Gaussian stochastic distribution systems with time delays via RBF neural networks.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yi, Qu; Zhan-ming, Li; Er-chao, Li</p> <p>2012-11-01</p> <p>A new <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) problem via the output probability density functions (PDFs) for non-gausian stochastic distribution systems (SDSs) is investigated. The PDFs can be approximated by radial basis functions (RBFs) neural networks. Different from conventional FDD problems, the measured information for FDD is the output stochastic distributions and the stochastic variables involved are not confined to Gaussian ones. A (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. In this work, a nonlinear adaptive observer-based <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the <span class="hlt">fault</span>. Stability and Convergency analysis is performed in <span class="hlt">fault</span> detection and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> analysis for the error dynamic system. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...72..303J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...72..303J"><span>Deep neural networks: A promising tool for <span class="hlt">fault</span> characteristic mining and intelligent <span class="hlt">diagnosis</span> of rotating machinery with massive data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na</p> <p>2016-05-01</p> <p>Aiming to promptly process the massive <span class="hlt">fault</span> data and automatically provide accurate <span class="hlt">diagnosis</span> results, numerous studies have been conducted on intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying <span class="hlt">faults</span>. Though these methods did work in intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific <span class="hlt">diagnosis</span> issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent <span class="hlt">diagnosis</span> methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The <span class="hlt">diagnosis</span> results show that the proposed method is able to not only adaptively mine available <span class="hlt">fault</span> characteristics from the measured signals, but also obtain superior <span class="hlt">diagnosis</span> accuracy compared with the existing methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005SPIE.6041...33D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005SPIE.6041...33D"><span>Development and realization of the open <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system based on XPE</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Deng, Hui; Wang, TaiYong; He, HuiLong; Xu, YongGang; Zeng, JuXiang</p> <p>2005-12-01</p> <p>To make the complex mechanical equipment work in good service, the technology for realizing an embedded open system is introduced systematically, including open hardware configuration, customized embedded operation system and open software structure. The ETX technology is adopted in this system, integrating the CPU main-board functions, and achieving the quick, real-time signal acquisition and intelligent data analysis with applying DSP and CPLD data acquisition card. Under the open configuration, the signal bus mode such as PCI, ISA and PC/104 can be selected and the styles of the signals can be chosen too. In addition, through customizing XPE system, adopting the EWF (Enhanced Write Filter), and realizing the open system authentically, the stability of the system is enhanced. Multi-thread and multi-task programming techniques are adopted in the software programming process. Interconnecting with the remote <span class="hlt">fault</span> <span class="hlt">diagnosis</span> center via the net interface, cooperative <span class="hlt">diagnosis</span> is conducted and the intelligent degree of the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is improved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4379147','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4379147"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> for the Heat Exchanger of the Aircraft Environmental Control System Based on the Strong Tracking Filter</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Ma, Jian; Lu, Chen; Liu, Hongmei</p> <p>2015-01-01</p> <p>The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system’s efficiency, which can lead to catastrophic consequences. <span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the <span class="hlt">fault</span> occurrence, whereas the heat exchanger <span class="hlt">faults</span> are usually depicted by utilizing the corresponding <span class="hlt">fault</span>-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a <span class="hlt">fault</span>-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for <span class="hlt">fault</span> detection and failure mode classification of the heat exchanger, respectively. Heat exchanger <span class="hlt">fault</span> simulation is conducted to generate <span class="hlt">fault</span> data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of the heat exchanger. PMID:25823010</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25823010','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25823010"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ma, Jian; Lu, Chen; Liu, Hongmei</p> <p>2015-01-01</p> <p>The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. <span class="hlt">Fault</span> <span class="hlt">diagnosis</span> of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the <span class="hlt">fault</span> occurrence, whereas the heat exchanger <span class="hlt">faults</span> are usually depicted by utilizing the corresponding <span class="hlt">fault</span>-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a <span class="hlt">fault</span>-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for <span class="hlt">fault</span> detection and failure mode classification of the heat exchanger, respectively. Heat exchanger <span class="hlt">fault</span> simulation is conducted to generate <span class="hlt">fault</span> data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of the heat exchanger.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013MSSP...39..342L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013MSSP...39..342L"><span>Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liang, B.; Iwnicki, S. D.; Zhao, Y.</p> <p>2013-08-01</p> <p>The power spectrum is defined as the square of the magnitude of the Fourier transform (FT) of a signal. The advantage of FT analysis is that it allows the decomposition of a signal into individual periodic frequency components and establishes the relative intensity of each component. It is the most commonly used signal processing technique today. If the same principle is applied for the detection of periodicity components in a Fourier spectrum, the process is called the cepstrum analysis. Cepstrum analysis is a very useful tool for detection families of harmonics with uniform spacing or the families of sidebands commonly found in gearbox, bearing and engine <span class="hlt">vibration</span> <span class="hlt">fault</span> spectra. Higher order spectra (HOS) (also known as polyspectra) consist of higher order moment of spectra which are able to detect non-linear interactions between frequency components. For HOS, the most commonly used is the bispectrum. The bispectrum is the third-order frequency domain measure, which contains information that standard power spectral analysis techniques cannot provide. It is well known that neural networks can represent complex non-linear relationships, and therefore they are extremely useful for <span class="hlt">fault</span> identification and classification. This paper presents an application of power spectrum, cepstrum, bispectrum and neural network for <span class="hlt">fault</span> pattern extraction of induction motors. The potential for using the power spectrum, cepstrum, bispectrum and neural network as a means for differentiating between healthy and faulty induction motor operation is examined. A series of experiments is done and the advantages and disadvantages between them are discussed. It has been found that a combination of power spectrum, cepstrum and bispectrum plus neural network analyses could be a very useful tool for condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of induction motors.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27890125','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27890125"><span>Human-centered design (HCD) of a <span class="hlt">fault</span>-finding application for mobile devices and its impact on the reduction of time in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in the manufacturing industry.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kluge, Annette; Termer, Anatoli</p> <p>2017-03-01</p> <p>The present article describes the design process of a <span class="hlt">fault</span>-finding application for mobile devices, which was built to support workers' performance by guiding them through a systematic strategy to stay focused during a <span class="hlt">fault</span>-finding process. In collaboration with a project partner in the manufacturing industry, a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> application was conceptualized based on a human-centered design approach (ISO 9241-210:2010). A field study with 42 maintenance workers was conducted for the purpose of evaluating the performance enhancement of <span class="hlt">fault</span> finding in three different scenarios as well as for assessing the workers' acceptance of the technology. Workers using the mobile device application were twice as fast at <span class="hlt">fault</span> finding as the control group without the application and perceived the application as very useful. The results indicate a vast potential of the mobile application for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in contemporary manufacturing systems. Copyright © 2016 Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017IJC....90.2227Q','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017IJC....90.2227Q"><span><span class="hlt">Fault</span>-tolerant cooperative output regulation for multi-vehicle systems with sensor <span class="hlt">faults</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Qin, Liguo; He, Xiao; Zhou, D. H.</p> <p>2017-10-01</p> <p>This paper presents a unified framework of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and <span class="hlt">fault</span>-tolerant cooperative output regulation (FTCOR) for a linear discrete-time multi-vehicle system with sensor <span class="hlt">faults</span>. The FTCOR control law is designed through three steps. A cooperative output regulation (COR) controller is designed based on the internal mode principle when there are no sensor <span class="hlt">faults</span>. A sufficient condition on the existence of the COR controller is given based on the discrete-time algebraic Riccati equation (DARE). Then, a decentralised <span class="hlt">fault</span> <span class="hlt">diagnosis</span> scheme is designed to cope with sensor <span class="hlt">faults</span> occurring in followers. A residual generator is developed to detect sensor <span class="hlt">faults</span> of each follower, and a bank of <span class="hlt">fault</span>-matching estimators are proposed to isolate and estimate sensor <span class="hlt">faults</span> of each follower. Unlike the current distributed <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for multi-vehicle systems, the presented decentralised <span class="hlt">fault</span> <span class="hlt">diagnosis</span> scheme in each vehicle reduces the communication and computation load by only using the information of the vehicle. By combing the sensor <span class="hlt">fault</span> estimation and the COR control law, an FTCOR controller is proposed. Finally, the simulation results demonstrate the effectiveness of the FTCOR controller.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26549566','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26549566"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> and <span class="hlt">fault</span>-tolerant finite control set-model predictive control of a multiphase voltage-source inverter supplying BLDC motor.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Salehifar, Mehdi; Moreno-Equilaz, Manuel</p> <p>2016-01-01</p> <p>Due to its <span class="hlt">fault</span> tolerance, a multiphase brushless direct current (BLDC) motor can meet high reliability demand for application in electric vehicles. The voltage-source inverter (VSI) supplying the motor is subjected to open circuit <span class="hlt">faults</span>. Therefore, it is necessary to design a <span class="hlt">fault</span>-tolerant (FT) control algorithm with an embedded <span class="hlt">fault</span> <span class="hlt">diagnosis</span> (FD) block. In this paper, finite control set-model predictive control (FCS-MPC) is developed to implement the <span class="hlt">fault</span>-tolerant control algorithm of a five-phase BLDC motor. The developed control method is fast, simple, and flexible. A FD method based on available information from the control block is proposed; this method is simple, robust to common transients in motor and able to localize multiple open circuit <span class="hlt">faults</span>. The proposed FD and FT control algorithm are embedded in a five-phase BLDC motor drive. In order to validate the theory presented, simulation and experimental results are conducted on a five-phase two-level VSI supplying a five-phase BLDC motor. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008MeScT..19d5105F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008MeScT..19d5105F"><span>Machine <span class="hlt">fault</span> feature extraction based on intrinsic mode functions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fan, Xianfeng; Zuo, Ming J.</p> <p>2008-04-01</p> <p>This work employs empirical mode decomposition (EMD) to decompose raw <span class="hlt">vibration</span> signals into intrinsic mode functions (IMFs) that represent the oscillatory modes generated by the components that make up the mechanical systems generating the <span class="hlt">vibration</span> signals. The motivation here is to develop <span class="hlt">vibration</span> signal analysis programs that are self-adaptive and that can detect machine <span class="hlt">faults</span> at the earliest onset of deterioration. The change in velocity of the amplitude of some IMFs over a particular unit time will increase when the <span class="hlt">vibration</span> is stimulated by a component <span class="hlt">fault</span>. Therefore, the amplitude acceleration energy in the intrinsic mode functions is proposed as an indicator of the impulsive features that are often associated with mechanical component <span class="hlt">faults</span>. The periodicity of the amplitude acceleration energy for each IMF is extracted by spectrum analysis. A spectrum amplitude index is introduced as a method to select the optimal result. A comparison study of the method proposed here and some well-established techniques for detecting machinery <span class="hlt">faults</span> is conducted through the analysis of both gear and bearing <span class="hlt">vibration</span> signals. The results indicate that the proposed method has superior capability to extract machine <span class="hlt">fault</span> features from <span class="hlt">vibration</span> signals.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JPhCS.910a2031L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPhCS.910a2031L"><span>An Efficient Algorithm for Server Thermal <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Infrared Image</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Hang; Xie, Ting; Ran, Jian; Gao, Shan</p> <p>2017-10-01</p> <p>It is essential for a data center to maintain server security and stability. Long-time overload operation or high room temperature may cause service disruption even a server crash, which would result in great economic loss for business. Currently, the methods to avoid server outages are monitoring and forecasting. Thermal camera can provide fine texture information for monitoring and intelligent thermal management in large data center. This paper presents an efficient method for server thermal <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> based on infrared image. Initially thermal distribution of server is standardized and the interest regions of the image are segmented manually. Then the texture feature, Hu moments feature as well as modified entropy feature are extracted from the segmented regions. These characteristics are applied to analyze and classify thermal <span class="hlt">faults</span>, and then make efficient energy-saving thermal management decisions such as job migration. For the larger feature space, the principal component analysis is employed to reduce the feature dimensions, and guarantee high processing speed without losing the <span class="hlt">fault</span> feature information. Finally, different feature vectors are taken as input for SVM training, and do the thermal <span class="hlt">fault</span> <span class="hlt">diagnosis</span> after getting the optimized SVM classifier. This method supports suggestions for optimizing data center management, it can improve air conditioning efficiency and reduce the energy consumption of the data center. The experimental results show that the maximum detection accuracy is 81.5%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...94..148L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...94..148L"><span>An underdamped stochastic resonance method with stable-state matching for incipient <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lei, Yaguo; Qiao, Zijian; Xu, Xuefang; Lin, Jing; Niu, Shantao</p> <p>2017-09-01</p> <p>Most traditional overdamped monostable, bistable and even tristable stochastic resonance (SR) methods have three shortcomings in weak characteristic extraction: (1) their potential structures characterized by single stable-state type are insufficient to match with the complicated and diverse mechanical <span class="hlt">vibration</span> signals; (2) they vulnerably suffer the interference from multiscale noise and largely depend on the help of highpass filters whose parameters are selected subjectively, probably resulting in false detection; and (3) their rescaling factors are fixed as constants generally, thereby ignoring the synergistic effect among <span class="hlt">vibration</span> signals, potential structures and rescaling factors. These three shortcomings have limited the enhancement ability of SR. To explore the SR potential, this paper initially investigates the SR in a multistable system by calculating its output spectral amplification, further analyzes its output frequency response numerically, then examines the effect of both damping and rescaling factors on output responses and finally presents a promising underdamped SR method with stable-state matching for incipient bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. This method has three advantages: (1) the diversity of stable-state types in a multistable potential makes it easy to match with various <span class="hlt">vibration</span> signals; (2) the underdamped multistable SR, equivalent to a moving nonlinear bandpass filter that is dependent on the rescaling factors, is able to suppress the multiscale noise; and (3) the synergistic effect among <span class="hlt">vibration</span> signals, potential structures and rescaling and damping factors is achieved using quantum genetic algorithms whose fitness functions are new weighted signal-to-noise ratio (WSNR) instead of SNR. Therefore, the proposed method is expected to possess good enhancement ability. Simulated and experimental data of rolling element bearings demonstrate its effectiveness. The comparison results show that the proposed method is able to obtain higher</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhSen...5..128L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhSen...5..128L"><span>Study on the non-contact FBG <span class="hlt">vibration</span> sensor and its application</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Tianliang; Tan, Yuegang; Zhou, Zude; Cai, Li; Liu, Sai; He, Zhongting; Zheng, Kai</p> <p>2015-06-01</p> <p>A non-contact <span class="hlt">vibration</span> sensor based on the fiber Bragg grating (FBG) sensor has been presented, and it is used to monitor the <span class="hlt">vibration</span> of rotating shaft. In the paper, we describe the principle of the sensor and make some experimental analyses. The analysis results show that the sensitivity and linearity of the sensor are -1.5 pm/μm and 4.11% within a measuring range of 2 mm-2.6 mm, respectively. When it is used to monitor the <span class="hlt">vibration</span> of the rotating shaft, the analysis signals of <span class="hlt">vibration</span> of the rotating shaft and the critical speed of rotation obtained are the same as that obtained from the eddy current sensor. It verifies that the sensor can be used for the non-contact measurement of <span class="hlt">vibration</span> of the rotating shaft system and for <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> of rotating machinery.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011MSSP...25.2102B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011MSSP...25.2102B"><span>Detection and <span class="hlt">diagnosis</span> of bearing and cutting tool <span class="hlt">faults</span> using hidden Markov models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Boutros, Tony; Liang, Ming</p> <p>2011-08-01</p> <p>Over the last few decades, the research for new <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical <span class="hlt">faults</span>. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing <span class="hlt">faults</span>. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the <span class="hlt">fault</span> seeded in two different engine bearings. The success rate obtained in our tests for <span class="hlt">fault</span> severity classification was above 95%. In addition to the <span class="hlt">fault</span> severity, a location index was developed to determine the <span class="hlt">fault</span> location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing <span class="hlt">fault</span> with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/12901208','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/12901208"><span><span class="hlt">Diagnosis</span> and treatment of hand-arm <span class="hlt">vibration</span> syndrome and its relationship to carpal tunnel syndrome.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Falkiner, Sonja</p> <p>2003-07-01</p> <p>Hand-arm <span class="hlt">vibration</span> syndrome (HAVS) is a condition associated with the use of <span class="hlt">vibrating</span> tools that occurs mainly in men. It consists primarily of 'occupational' Raynaud disease and digital polyneuropathy. Carpal tunnel syndrome (CTS) is also associated with hand transmitted <span class="hlt">vibration</span> exposure and can coexist with HAVS. This article examines recent papers on causation, <span class="hlt">diagnosis</span>, relationship to CTS and treatment. A Medline search was conducted, as was a search of UK, USA and Australian government occupational health and safety websites. Published papers that were single case studies or of poor design were not included. There are no 'gold standard' diagnostic tests for HAVS. It can mimic CTS in temperate climates and can occur with CTS. This is the diagnostic challenge when a male worker presents with apparent CTS symptoms. If he has worked with <span class="hlt">vibrating</span> tools for many years, a <span class="hlt">diagnosis</span> of HAVS or co-<span class="hlt">diagnosis</span> of HAVS should be considered before a <span class="hlt">diagnosis</span> of pure CTS is made. Nonwork risk factors for HAVS are predisposition, smoking, and exposure to <span class="hlt">vibration</span> outside work. Cessation of exposure (and smoking) and redeployment is a critical part of treatment due to the dose response relationship of HAVS. This contrasts with adequately treated CTS, where the vast majority of workers can return to pre-injury duties. In severe cases, calcium antagonists are also used, but treatment is often ineffective. Few workplaces in Australia manage <span class="hlt">vibration</span> risk or conduct screening to identify workers with early HAVS who should be redeployed. Local doctors have an important opportunity to diagnose HAVS and to make recommendations to the workplace on redeployment as part of treatment before symptoms become irreversible.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010PhDT.......106K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010PhDT.......106K"><span>A Bayesian least squares support vector machines based framework for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and failure prognosis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khawaja, Taimoor Saleem</p> <p></p> <p>A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of <span class="hlt">fault</span> indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for <span class="hlt">diagnosis</span> and prognosis but also provides a solid theoretical framework to address key concepts related to classification for <span class="hlt">diagnosis</span> and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for <span class="hlt">diagnosis</span> and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and failure prognosis. With the goal of designing an efficient and reliable <span class="hlt">fault</span> <span class="hlt">diagnosis</span> scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018IJE...105..559R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018IJE...105..559R"><span><span class="hlt">Fault</span> detection and <span class="hlt">diagnosis</span> in asymmetric multilevel inverter using artificial neural network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Raj, Nithin; Jagadanand, G.; George, Saly</p> <p>2018-04-01</p> <p>The increased component requirement to realise multilevel inverter (MLI) fallout in a higher <span class="hlt">fault</span> prospect due to power semiconductors. In this scenario, efficient <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) strategies to detect and locate the power semiconductor <span class="hlt">faults</span> have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the <span class="hlt">fault</span> in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the <span class="hlt">fault</span> in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MeScT..29f5107J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MeScT..29f5107J"><span>Intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling bearings using an improved deep recurrent neural network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiang, Hongkai; Li, Xingqiu; Shao, Haidong; Zhao, Ke</p> <p>2018-06-01</p> <p>Traditional intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21954208','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21954208"><span>Data-based hybrid tension estimation and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of cold rolling continuous annealing processes.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Qiang; Chai, Tianyou; Wang, Hong; Qin, Si-Zhao Joe</p> <p>2011-12-01</p> <p>The continuous annealing process line (CAPL) of cold rolling is an important unit to improve the mechanical properties of steel strips in steel making. In continuous annealing processes, strip tension is an important factor, which indicates whether the line operates steadily. Abnormal tension profile distribution along the production line can lead to strip break and roll slippage. Therefore, it is essential to estimate the whole tension profile in order to prevent the occurrence of <span class="hlt">faults</span>. However, in real annealing processes, only a limited number of strip tension sensors are installed along the machine direction. Since the effects of strip temperature, gas flow, bearing friction, strip inertia, and roll eccentricity can lead to nonlinear tension dynamics, it is difficult to apply the first-principles induced model to estimate the tension profile distribution. In this paper, a novel data-based hybrid tension estimation and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is proposed to estimate the unmeasured tension between two neighboring rolls. The main model is established by an observer-based method using a limited number of measured tensions, speeds, and currents of each roll, where the tension error compensation model is designed by applying neural networks principal component regression. The corresponding tension <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is designed using the estimated tensions. Finally, the proposed tension estimation and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method was applied to a real CAPL in a steel-making company, demonstrating the effectiveness of the proposed method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..331a2032N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..331a2032N"><span>Various Indices for <span class="hlt">Diagnosis</span> of Air-gap Eccentricity <span class="hlt">Fault</span> in Induction Motor-A Review</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nikhil; Mathew, Lini, Dr.; Sharma, Amandeep</p> <p>2018-03-01</p> <p>From the past few years, research has gained an ardent pace in the field of <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> in induction motors. In the current scenario, software is being introduced with diagnostic features to improve stability and reliability in <span class="hlt">fault</span> diagnostic techniques. Human involvement in decision making for <span class="hlt">fault</span> detection is slowly being replaced by Artificial Intelligence techniques. In this paper, a brief introduction of eccentricity <span class="hlt">fault</span> is presented along with their causes and effects on the health of induction motors. Various indices used to detect eccentricity are being introduced along with their boundary conditions and their future scope of research. At last, merits and demerits of all indices are discussed and a comparison is made between them.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/21293339-infinity-fault-detection-diagnosis-scheme-discrete-nonlinear-system-using-output-probability-density-estimation','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/21293339-infinity-fault-detection-diagnosis-scheme-discrete-nonlinear-system-using-output-probability-density-estimation"><span>A H-infinity <span class="hlt">Fault</span> Detection and <span class="hlt">Diagnosis</span> Scheme for Discrete Nonlinear System Using Output Probability Density Estimation</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Zhang Yumin; Lum, Kai-Yew; Wang Qingguo</p> <p></p> <p>In this paper, a H-infinity <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) scheme for a class of discrete nonlinear system <span class="hlt">fault</span> using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus,more » the classical nonlinear filter approach can be used to detect and diagnose the <span class="hlt">fault</span> in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm is further investigated to estimate the <span class="hlt">fault</span>. Simulation example is given to demonstrate the effectiveness of the proposed approaches.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AIPC.1107...79Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AIPC.1107...79Z"><span>A H-infinity <span class="hlt">Fault</span> Detection and <span class="hlt">Diagnosis</span> Scheme for Discrete Nonlinear System Using Output Probability Density Estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Yumin; Wang, Qing-Guo; Lum, Kai-Yew</p> <p>2009-03-01</p> <p>In this paper, a H-infinity <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) scheme for a class of discrete nonlinear system <span class="hlt">fault</span> using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear filter approach can be used to detect and diagnose the <span class="hlt">fault</span> in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm is further investigated to estimate the <span class="hlt">fault</span>. Simulation example is given to demonstrate the effectiveness of the proposed approaches.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/6090740','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/6090740"><span>[A study on the thermographic <span class="hlt">diagnosis</span> of <span class="hlt">vibration</span> disease of tie-tamper operators in the Japanese National Railways].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hirahata, H</p> <p>1984-01-01</p> <p>There have been many studies of thermographic <span class="hlt">diagnosis</span> of <span class="hlt">vibration</span> disease, but few of them seem to have discussed tie-tamping machines as a cause. This study focuses on thermographic <span class="hlt">diagnosis</span> of <span class="hlt">vibration</span> disease in tie-tamper operators of the Japanese National Railways. In the <span class="hlt">diagnosis</span> the subject's both hands were immersed in water at 10 degrees C for 3 minutes before being examined. Variables such as season, age, type of <span class="hlt">vibration</span> tool used and total operating time were considered. These were selected as outside variables and thermographic results as dependent variables, in Quantification Method II. Season and confirmation of <span class="hlt">vibration</span> disease were found to have a relationship to thermographic scaling, but no such relationship was found for age, type of <span class="hlt">vibration</span> tool used, or total operating time. A cross-analysis of variables confirmed the relationship with season, and revealed that there were fewer confirmed cases of <span class="hlt">vibration</span> disease in spring and summer than in fall and winter. It was finally concluded that thermographic analysis is more reliable in colder weather.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4188612','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4188612"><span>A Compound <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi</p> <p>2014-01-01</p> <p>A Compound <span class="hlt">fault</span> signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week <span class="hlt">fault</span> signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound <span class="hlt">faults</span> diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound <span class="hlt">faults</span> from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a <span class="hlt">vibration</span> signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound <span class="hlt">faults</span> can be separated effectively by executing ICA method, which makes the <span class="hlt">fault</span> features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound <span class="hlt">fault</span> separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance <span class="hlt">fault</span> of the experimental system. PMID:25289644</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MS%26E..217a2031A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MS%26E..217a2031A"><span>Bearing <span class="hlt">faults</span> identification and resonant band demodulation based on wavelet de-noising methods and envelope analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Abdelrhman, Ahmed M.; Sei Kien, Yong; Salman Leong, M.; Meng Hee, Lim; Al-Obaidi, Salah M. Ali</p> <p>2017-07-01</p> <p>The <span class="hlt">vibration</span> signals produced by rotating machinery contain useful information for condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. <span class="hlt">Fault</span> severities assessment is a challenging task. Wavelet Transform (WT) as a multivariate analysis tool is able to compromise between the time and frequency information in the signals and served as a de-noising method. The CWT scaling function gives different resolutions to the discretely signals such as very fine resolution at lower scale but coarser resolution at a higher scale. However, the computational cost increased as it needs to produce different signal resolutions. DWT has better low computation cost as the dilation function allowed the signals to be decomposed through a tree of low and high pass filters and no further analysing the high-frequency components. In this paper, a method for bearing <span class="hlt">faults</span> identification is presented by combing Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) with envelope analysis for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The experimental data was sampled by Case Western Reserve University. The analysis result showed that the proposed method is effective in bearing <span class="hlt">faults</span> detection, identify the exact fault’s location and severity assessment especially for the inner race and outer race <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1063057','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1063057"><span>Sideband Algorithm for Automatic Wind Turbine Gearbox <span class="hlt">Fault</span> Detection and <span class="hlt">Diagnosis</span>: Preprint</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Zappala, D.; Tavner, P.; Crabtree, C.</p> <p>2013-01-01</p> <p>Improving the availability of wind turbines (WT) is critical to minimize the cost of wind energy, especially for offshore installations. As gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of great concern to the wind industry. Timely detection and <span class="hlt">diagnosis</span> of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary as turbine load and speed vary continuously with time. Time-consuming and costly manual handling of large amounts of monitoring data representmore » one of the main limitations of most current CMSs, so automated algorithms are required. This paper presents a <span class="hlt">fault</span> detection algorithm for incorporation into a commercial CMS for automatic gear <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span>. The algorithm allowed the assessment of gear <span class="hlt">fault</span> severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation reducing the quantity of information that WT operators must handle.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1986SPIE..635...58A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1986SPIE..635...58A"><span>A Flight Expert System (FLES) For On-Board <span class="hlt">Fault</span> Monitoring And <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ali, M.; Scharnhorst, D...; Ai, C. S.; Ferber, H. J.</p> <p>1986-03-01</p> <p>The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher work loads for the pilot and tend to induce a higher probability of errors. Therefore, we have developed an architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing and recovering from in-flight <span class="hlt">faults</span>. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Lockheed Advanced Concept System (ACS), a future generation airplane, and partly on the Boeing 737, an existing airplane. A distinction between two types of <span class="hlt">faults</span>, maladjustments and malfunctions, has led us to take two approaches to <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to assess abnormal situations during airplane flight. This paper describes our reasons for handling malfunctions and maladjustments separately and the use of domain knowledge in the <span class="hlt">diagnosis</span> of each.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19870055355&hterms=aircraft+diagnosis+expert+system&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Daircraft%2Bdiagnosis%2Bexpert%2Bsystem','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870055355&hterms=aircraft+diagnosis+expert+system&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Daircraft%2Bdiagnosis%2Bexpert%2Bsystem"><span>A flight expert system (FLES) for on-board <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ali, M.; Scharnhorst, D. A.; Ai, C. S.; Ferber, H. J.</p> <p>1986-01-01</p> <p>The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher work loads for the pilot and tend to induce a higher probability of errors. Therefore, an architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing and recovering from in-flight <span class="hlt">faults</span> has been developed. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Lockheed Advanced Concept System (ACS), a future generation airplane, and partly on the Boeing 737, an existing airplane. A distinction between two types of <span class="hlt">faults</span>, maladjustments and malfunctions, has led us to take two approaches to <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to assess abnormal situations during airplane flight. This paper describes the reasons for handling malfunctions and maladjustments separately and the use of domain knowledge in the <span class="hlt">diagnosis</span> of each.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005SPIE.6040E..0QQ','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005SPIE.6040E..0QQ"><span>Research into a distributed <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system and its application</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Qian, Suxiang; Jiao, Weidong; Lou, Yongjian; Shen, Xiaomei</p> <p>2005-12-01</p> <p>CORBA (Common Object Request Broker Architecture) is a solution to distributed computing methods over heterogeneity systems, which establishes a communication protocol between distributed objects. It takes great emphasis on realizing the interoperation between distributed objects. However, only after developing some application approaches and some practical technology in monitoring and <span class="hlt">diagnosis</span>, can the customers share the monitoring and <span class="hlt">diagnosis</span> information, so that the purpose of realizing remote multi-expert cooperation <span class="hlt">diagnosis</span> online can be achieved. This paper aims at building an open <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> platform combining CORBA, Web and agent. Heterogeneity <span class="hlt">diagnosis</span> object interoperate in independent thread through the CORBA (soft-bus), realizing sharing resource and multi-expert cooperation <span class="hlt">diagnosis</span> online, solving the disadvantage such as lack of <span class="hlt">diagnosis</span> knowledge, oneness of <span class="hlt">diagnosis</span> technique and imperfectness of analysis function, so that more complicated and further <span class="hlt">diagnosis</span> can be carried on. Take high-speed centrifugal air compressor set for example, we demonstrate a distributed <span class="hlt">diagnosis</span> based on CORBA. It proves that we can find out more efficient approaches to settle the problems such as real-time monitoring and <span class="hlt">diagnosis</span> on the net and the break-up of complicated tasks, inosculating CORBA, Web technique and agent frame model to carry on complemental research. In this system, Multi-<span class="hlt">diagnosis</span> Intelligent Agent helps improve <span class="hlt">diagnosis</span> efficiency. Besides, this system offers an open circumstances, which is easy for the <span class="hlt">diagnosis</span> objects to upgrade and for new <span class="hlt">diagnosis</span> server objects to join in.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AIPC.1949q0003P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AIPC.1949q0003P"><span>Trackside acoustic <span class="hlt">diagnosis</span> of axle box bearing based on kurtosis-optimization wavelet denoising</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, Chaoyong; Gao, Xiaorong; Peng, Jianping; Wang, Ai</p> <p>2018-04-01</p> <p>As one of the key components of railway vehicles, the operation condition of the axle box bearing has a significant effect on traffic safety. The acoustic <span class="hlt">diagnosis</span> is more suitable than <span class="hlt">vibration</span> <span class="hlt">diagnosis</span> for trackside monitoring. The acoustic signal generated by the train axle box bearing is an amplitude modulation and frequency modulation signal with complex train running noise. Although empirical mode decomposition (EMD) and some improved time-frequency algorithms have proved to be useful in bearing <span class="hlt">vibration</span> signal processing, it is hard to extract the bearing <span class="hlt">fault</span> signal from serious trackside acoustic background noises by using those algorithms. Therefore, a kurtosis-optimization-based wavelet packet (KWP) denoising algorithm is proposed, as the kurtosis is the key indicator of bearing <span class="hlt">fault</span> signal in time domain. Firstly, the geometry based Doppler correction is applied to signals of each sensor, and with the signal superposition of multiple sensors, random noises and impulse noises, which are the interference of the kurtosis indicator, are suppressed. Then, the KWP is conducted. At last, the EMD and Hilbert transform is applied to extract the <span class="hlt">fault</span> feature. Experiment results indicate that the proposed method consisting of KWP and EMD is superior to the EMD.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/1296514-development-testing-fault-diagnosis-algorithms-reactor-plant-systems','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1296514-development-testing-fault-diagnosis-algorithms-reactor-plant-systems"><span>DEVELOPMENT AND TESTING OF <span class="hlt">FAULT-DIAGNOSIS</span> ALGORITHMS FOR REACTOR PLANT SYSTEMS</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Grelle, Austin L.; Park, Young S.; Vilim, Richard B.</p> <p></p> <p>Argonne National Laboratory is further developing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithms for use by the operator of a nuclear plant to aid in improved monitoring of overall plant condition and performance. The objective is better management of plant upsets through more timely, informed decisions on control actions with the ultimate goal of improved plant safety, production, and cost management. Integration of these algorithms with visual aids for operators is taking place through a collaboration under the concept of an operator advisory system. This is a software entity whose purpose is to manage and distill the enormous amount of information an operator mustmore » process to understand the plant state, particularly in off-normal situations, and how the state trajectory will unfold in time. The <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithms were exhaustively tested using computer simulations of twenty different <span class="hlt">faults</span> introduced into the chemical and volume control system (CVCS) of a pressurized water reactor (PWR). The algorithms are unique in that each new application to a facility requires providing only the piping and instrumentation diagram (PID) and no other plant-specific information; a subject-matter expert is not needed to install and maintain each instance of an application. The testing approach followed accepted procedures for verifying and validating software. It was shown that the code satisfies its functional requirement which is to accept sensor information, identify process variable trends based on this sensor information, and then to return an accurate <span class="hlt">diagnosis</span> based on chains of rules related to these trends. The validation and verification exercise made use of GPASS, a one-dimensional systems code, for simulating CVCS operation. Plant components were failed and the code generated the resulting plant response. Parametric studies with respect to the severity of the <span class="hlt">fault</span>, the richness of the plant sensor set, and the accuracy of sensors were performed as part of the validation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JSV...425...53C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JSV...425...53C"><span>Application of an improved minimum entropy deconvolution method for railway rolling element bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cheng, Yao; Zhou, Ning; Zhang, Weihua; Wang, Zhiwei</p> <p>2018-07-01</p> <p>Minimum entropy deconvolution is a widely-used tool in machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, because it enhances the impulse component of the signal. The filter coefficients that greatly influence the performance of the minimum entropy deconvolution are calculated by an iterative procedure. This paper proposes an improved deconvolution method for the <span class="hlt">fault</span> detection of rolling element bearings. The proposed method solves the filter coefficients by the standard particle swarm optimization algorithm, assisted by a generalized spherical coordinate transformation. When optimizing the filters performance for enhancing the impulses in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> (namely, faulty rolling element bearings), the proposed method outperformed the classical minimum entropy deconvolution method. The proposed method was validated in simulation and experimental signals from railway bearings. In both simulation and experimental studies, the proposed method delivered better deconvolution performance than the classical minimum entropy deconvolution method, especially in the case of low signal-to-noise ratio.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5335931','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5335931"><span>An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Planetary Gearbox</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng</p> <p>2017-01-01</p> <p>A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific <span class="hlt">fault</span> <span class="hlt">diagnosis</span> task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any <span class="hlt">fault</span> <span class="hlt">diagnosis</span> task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best <span class="hlt">diagnosis</span> accuracy among all comparative methods in the experiment. PMID:28230767</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050031079','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050031079"><span>A New On-Line <span class="hlt">Diagnosis</span> Protocol for the SPIDER Family of Byzantine <span class="hlt">Fault</span> Tolerant Architectures</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Geser, Alfons; Miner, Paul S.</p> <p>2004-01-01</p> <p>This paper presents the formal verification of a new protocol for online distributed <span class="hlt">diagnosis</span> for the SPIDER family of architectures. An instance of the Scalable Processor-Independent Design for Electromagnetic Resilience (SPIDER) architecture consists of a collection of processing elements communicating over a Reliable Optical Bus (ROBUS). The ROBUS is a specialized <span class="hlt">fault</span>-tolerant device that guarantees Interactive Consistency, Distributed <span class="hlt">Diagnosis</span> (Group Membership), and Synchronization in the presence of a bounded number of physical <span class="hlt">faults</span>. Formal verification of the original SPIDER <span class="hlt">diagnosis</span> protocol provided a detailed understanding that led to the discovery of a significantly more efficient protocol. The original protocol was adapted from the formally verified protocol used in the MAFT architecture. It required O(N) message exchanges per defendant to correctly diagnose failures in a system with N nodes. The new protocol achieves the same diagnostic fidelity, but only requires O(1) exchanges per defendant. This paper presents this new <span class="hlt">diagnosis</span> protocol and a formal proof of its correctness using PVS.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25610897','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25610897"><span>A modular neural network scheme applied to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in electric power systems.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio</p> <p>2014-01-01</p> <p>This work proposes a new method for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in electric power systems based on neural modules. With this method the <span class="hlt">diagnosis</span> is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of <span class="hlt">fault</span> voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a <span class="hlt">fault</span>. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29283398','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29283398"><span>Application of a Multimedia Service and Resource Management Architecture for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Castro, Alfonso; Sedano, Andrés A; García, Fco Javier; Villoslada, Eduardo; Villagrá, Víctor A</p> <p>2017-12-28</p> <p>Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system and Business Processes for Telefónica's global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/870030','DOE-PATENT-XML'); return false;" href="https://www.osti.gov/servlets/purl/870030"><span>Combined expert system/neural networks method for process <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Reifman, Jaques; Wei, Thomas Y. C.</p> <p>1995-01-01</p> <p>A two-level hierarchical approach for process <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the <span class="hlt">diagnosis</span> and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the <span class="hlt">fault</span> can be determined, i.e., a component characteristic-oriented approach.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/100994','DOE-PATENT-XML'); return false;" href="https://www.osti.gov/biblio/100994"><span>Combined expert system/neural networks method for process <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Reifman, J.; Wei, T.Y.C.</p> <p>1995-08-15</p> <p>A two-level hierarchical approach for process <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the <span class="hlt">diagnosis</span> and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the <span class="hlt">fault</span> can be determined, i.e., a component characteristic-oriented approach. 9 figs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20180001179','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20180001179"><span>Early Oscillation Detection for Hybrid DC/DC Converter <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wang, Bright L.</p> <p>2011-01-01</p> <p>This paper describes a novel <span class="hlt">fault</span> detection technique for hybrid DC/DC converter oscillation <span class="hlt">diagnosis</span>. The technique is based on principles of feedback control loop oscillation and RF signal modulations, and Is realized by using signal spectral analysis. Real-circuit simulation and analytical study reveal critical factors of the oscillation and indicate significant correlations between the spectral analysis method and the gain/phase margin method. A stability <span class="hlt">diagnosis</span> index (SDI) is developed as a quantitative measure to accurately assign a degree of stability to the DC/DC converter. This technique Is capable of detecting oscillation at an early stage without interfering with DC/DC converter's normal operation and without limitations of probing to the converter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AIPC.1839b0111Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AIPC.1839b0111Z"><span>Method of gear <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on EEMD and improved Elman neural network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Qi; Zhao, Wei; Xiao, Shungen; Song, Mengmeng</p> <p>2017-05-01</p> <p>Aiming at crack and wear and so on of gears <span class="hlt">Fault</span> information is difficult to diagnose usually due to its weak, a gear <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method that is based on EEMD and improved Elman neural network fusion is proposed. A number of IMF components are obtained by decomposing denoised all kinds of <span class="hlt">fault</span> signals with EEMD, and the pseudo IMF components is eliminated by using the correlation coefficient method to obtain the effective IMF component. The energy characteristic value of each effective component is calculated as the input feature quantity of Elman neural network, and the improved Elman neural network is based on standard network by adding a feedback factor. The <span class="hlt">fault</span> data of normal gear, broken teeth, cracked gear and attrited gear were collected by field collecting. The results were analyzed by the diagnostic method proposed in this paper. The results show that compared with the standard Elman neural network, Improved Elman neural network has the advantages of high diagnostic efficiency.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17672519','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17672519"><span><span class="hlt">Fault</span> detection and <span class="hlt">diagnosis</span> in an industrial fed-batch cell culture process.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gunther, Jon C; Conner, Jeremy S; Seborg, Dale E</p> <p>2007-01-01</p> <p>A flexible process monitoring method was applied to industrial pilot plant cell culture data for the purpose of <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span>. Data from 23 batches, 20 normal operating conditions (NOC) and three abnormal, were available. A principal component analysis (PCA) model was constructed from 19 NOC batches, and the remaining NOC batch was used for model validation. Subsequently, the model was used to successfully detect (both offline and online) abnormal process conditions and to diagnose the root causes. This research demonstrates that data from a relatively small number of batches (approximately 20) can still be used to monitor for a wide range of process <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JPhCS.305a2058W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JPhCS.305a2058W"><span>Rule Extracting based on MCG with its Application in Helicopter Power Train <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, M.; Hu, N. Q.; Qin, G. J.</p> <p>2011-07-01</p> <p>In order to extract decision rules for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012PhDT.......262K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012PhDT.......262K"><span>Combinatorial Optimization Algorithms for Dynamic Multiple <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Automotive and Aerospace Applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kodali, Anuradha</p> <p></p> <p>In this thesis, we develop dynamic multiple <span class="hlt">fault</span> <span class="hlt">diagnosis</span> (DMFD) algorithms to diagnose <span class="hlt">faults</span> that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent <span class="hlt">faults</span> occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) <span class="hlt">fault</span> states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent <span class="hlt">fault</span> states over time. We demonstrate the algorithm on simulated and real-world systems with coupled <span class="hlt">faults</span>; the results show that this approach improves the correct isolation rate as compared to the formulation where independent <span class="hlt">fault</span> states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the <span class="hlt">fault</span>-test dependency matrix that couples the failed tests and <span class="hlt">faults</span> via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple <span class="hlt">fault</span> <span class="hlt">diagnosis</span> problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19910009760','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19910009760"><span>Robust <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of physical systems in operation. Ph.D. Thesis - Rutgers - The State Univ.</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Abbott, Kathy Hamilton</p> <p>1991-01-01</p> <p>Ideas are presented and demonstrated for improved robustness in diagnostic problem solving of complex physical systems in operation, or operative <span class="hlt">diagnosis</span>. The first idea is that graceful degradation can be viewed as reasoning at higher levels of abstraction whenever the more detailed levels proved to be incomplete or inadequate. A form of abstraction is defined that applies this view to the problem of <span class="hlt">diagnosis</span>. In this form of abstraction, named status abstraction, two levels are defined. The lower level of abstraction corresponds to the level of detail at which most current knowledge-based <span class="hlt">diagnosis</span> systems reason. At the higher level, a graph representation is presented that describes the real-world physical system. An incremental, constructive approach to manipulating this graph representation is demonstrated that supports certain characteristics of operative <span class="hlt">diagnosis</span>. The suitability of this constructive approach is shown for diagnosing <span class="hlt">fault</span> propagation behavior over time, and for sometimes diagnosing systems with feedback. A way is shown to represent different semantics in the same type of graph representation to characterize different types of <span class="hlt">fault</span> propagation behavior. An approach is demonstrated that threats these different behaviors as different <span class="hlt">fault</span> classes, and the approach moves to other classes when previous classes fail to generate suitable hypotheses. These ideas are implemented in a computer program named Draphys (Diagnostic Reasoning About Physical Systems) and demonstrated for the domain of inflight aircraft subsystems, specifically a propulsion system (containing two turbofan systems and a fuel system) and hydraulic subsystem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19970015852','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19970015852"><span>Improving the Performance of the Structure-Based Connectionist Network for <span class="hlt">Diagnosis</span> of Helicopter Gearboxes</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jammu, Vinay B.; Danai, Koroush; Lewicki, David G.</p> <p>1996-01-01</p> <p>A diagnostic method is introduced for helicopter gearboxes that uses knowledge of the gear-box structure and characteristics of the 'features' of <span class="hlt">vibration</span> to define the influences of <span class="hlt">faults</span> on features. The 'structural influences' in this method are defined based on the root mean square value of <span class="hlt">vibration</span> obtained from a simplified lumped-mass model of the gearbox. The structural influences are then converted to fuzzy variables, to account for the approximate nature of the lumped-mass model, and used as the weights of a connectionist network. <span class="hlt">Diagnosis</span> in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal <span class="hlt">vibration</span> features through the weights of SBCN to obtain <span class="hlt">fault</span> possibility values for each component in the gearbox. Upon occurrence of misdiagnoses, the SBCN also has the ability to improve its diagnostic performance. For this, a supervised training method is presented which adapts the weights of SBCN to minimize the number of misdiagnoses. For experimental evaluation of the SBCN, <span class="hlt">vibration</span> data from a OH-58A helicopter gearbox collected at NASA Lewis Research Center is used. Diagnostic results indicate that the SBCN is able to diagnose about 80% of the <span class="hlt">faults</span> without training, and is able to improve its performance to nearly 100% after training.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3231334','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3231334"><span>Intelligent Method for Diagnosing Structural <span class="hlt">Faults</span> of Rotating Machinery Using Ant Colony Optimization</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Li, Ke; Chen, Peng</p> <p>2011-01-01</p> <p>Structural <span class="hlt">faults</span>, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These <span class="hlt">faults</span> may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural <span class="hlt">faults</span> of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect <span class="hlt">faults</span> and distinguish <span class="hlt">fault</span> types at an early stage. New symptom parameters called “relative ratio symptom parameters” are defined for reflecting the features of <span class="hlt">vibration</span> signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural <span class="hlt">faults</span> often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these <span class="hlt">faults</span> are difficult to detect using conventional neural networks. PMID:22163833</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22163833','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22163833"><span>Intelligent method for diagnosing structural <span class="hlt">faults</span> of rotating machinery using ant colony optimization.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Ke; Chen, Peng</p> <p>2011-01-01</p> <p>Structural <span class="hlt">faults</span>, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These <span class="hlt">faults</span> may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural <span class="hlt">faults</span> of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect <span class="hlt">faults</span> and distinguish <span class="hlt">fault</span> types at an early stage. New symptom parameters called "relative ratio symptom parameters" are defined for reflecting the features of <span class="hlt">vibration</span> signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural <span class="hlt">faults</span> often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these <span class="hlt">faults</span> are difficult to detect using conventional neural networks.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27649171','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27649171"><span>Weak <span class="hlt">Fault</span> Feature Extraction of Rolling Bearings Based on an Improved Kurtogram.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi</p> <p>2016-09-13</p> <p>Kurtograms have been verified to be an efficient tool in bearing <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak <span class="hlt">fault</span> features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak <span class="hlt">fault</span> features from bearing <span class="hlt">vibration</span> signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak <span class="hlt">fault</span> features.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5038758','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5038758"><span>Weak <span class="hlt">Fault</span> Feature Extraction of Rolling Bearings Based on an Improved Kurtogram</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi</p> <p>2016-01-01</p> <p>Kurtograms have been verified to be an efficient tool in bearing <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak <span class="hlt">fault</span> features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak <span class="hlt">fault</span> features from bearing <span class="hlt">vibration</span> signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak <span class="hlt">fault</span> features. PMID:27649171</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4182697','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4182697"><span>A Modular Neural Network Scheme Applied to <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Electric Power Systems</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Flores, Agustín; Morant, Francisco</p> <p>2014-01-01</p> <p>This work proposes a new method for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in electric power systems based on neural modules. With this method the <span class="hlt">diagnosis</span> is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of <span class="hlt">fault</span> voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a <span class="hlt">fault</span>. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JPhCS.842a2046S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPhCS.842a2046S"><span>Non-negative Matrix Factorization and Co-clustering: A Promising Tool for Multi-tasks Bearing <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shen, Fei; Chen, Chao; Yan, Ruqiang</p> <p>2017-05-01</p> <p>Classical bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods, being designed according to one specific task, always pay attention to the effectiveness of extracted features and the final diagnostic performance. However, most of these approaches suffer from inefficiency when multiple tasks exist, especially in a real-time diagnostic scenario. A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on Non-negative Matrix Factorization (NMF) and Co-clustering strategy is proposed to overcome this limitation. Firstly, some high-dimensional matrixes are constructed using the Short-Time Fourier Transform (STFT) features, where the dimension of each matrix equals to the number of target tasks. Then, the NMF algorithm is carried out to obtain different components in each dimension direction through optimized matching, such as Euclidean distance and divergence distance. Finally, a Co-clustering technique based on information entropy is utilized to realize classification of each component. To verity the effectiveness of the proposed approach, a series of bearing data sets were analysed in this research. The tests indicated that although the diagnostic performance of single task is comparable to traditional clustering methods such as K-mean algorithm and Guassian Mixture Model, the accuracy and computational efficiency in multi-tasks <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are improved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015MSSP...58..160B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015MSSP...58..160B"><span>Clustering for unsupervised <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in nuclear turbine shut-down transients</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Baraldi, Piero; Di Maio, Francesco; Rigamonti, Marco; Zio, Enrico; Seraoui, Redouane</p> <p>2015-06-01</p> <p>Empirical methods for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> usually entail a process of supervised training based on a set of examples of signal evolutions "labeled" with the corresponding, known classes of <span class="hlt">fault</span>. However, in practice, the signals collected during plant operation may be, very often, "unlabeled", i.e., the information on the corresponding type of occurred <span class="hlt">fault</span> is not available. To cope with this practical situation, in this paper we develop a methodology for the identification of transient signals showing similar characteristics, under the conjecture that operational/faulty transient conditions of the same type lead to similar behavior in the measured signals evolution. The methodology is founded on a feature extraction procedure, which feeds a spectral clustering technique, embedding the unsupervised fuzzy C-means (FCM) algorithm, which evaluates the functional similarity among the different operational/faulty transients. A procedure for validating the plausibility of the obtained clusters is also propounded based on physical considerations. The methodology is applied to a real industrial case, on the basis of 148 shut-down transients of a Nuclear Power Plant (NPP) steam turbine.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA086592','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA086592"><span>An Annotated Selective Bibliography on Human Performance in <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Tasks</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1980-01-01</p> <p>and automated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> has been omitted from this bibliography. Where possible, we used the abstract that accompanied an item. However, we...system failure, ignition system failure, drive, transmission, and rear axle trouble, and power brake and power steering failure. 98 pages Howard W. Sams...knowledge. The second section discusses some of the pedagogical issues that have emerged from the use of diagnostic models within an instructional</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1992SPIE.1706..228S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1992SPIE.1706..228S"><span>Adaptive neural network/expert system that learns <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for different structures</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Simon, Solomon H.</p> <p>1992-08-01</p> <p>Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5795546','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5795546"><span>Application of a Multimedia Service and Resource Management Architecture for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Castro, Alfonso; Sedano, Andrés A.; García, Fco. Javier; Villoslada, Eduardo</p> <p>2017-01-01</p> <p>Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system and Business Processes for Telefónica’s global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam. PMID:29283398</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...94..499Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...94..499Z"><span>Sparsity-aware tight frame learning with adaptive subspace recognition for multiple <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yang, Boyuan</p> <p>2017-09-01</p> <p>It is a challenging problem to design excellent dictionaries to sparsely represent diverse <span class="hlt">fault</span> information and simultaneously discriminate different <span class="hlt">fault</span> sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed <span class="hlt">fault</span> related sensitive indexes, latent <span class="hlt">fault</span> feature subspaces can be adaptively recognized and multiple <span class="hlt">faults</span> are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of motor bearings. Compared with the state-of-the-art <span class="hlt">fault</span> detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple <span class="hlt">fault</span> feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of <span class="hlt">fault</span> features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..103..312X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..103..312X"><span>Repetitive transient extraction for machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using multiscale fractional order entropy infogram</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xu, Xuefang; Qiao, Zijian; Lei, Yaguo</p> <p>2018-03-01</p> <p>The presence of repetitive transients in <span class="hlt">vibration</span> signals is a typical symptom of local <span class="hlt">faults</span> of rotating machinery. Infogram was developed to extract the repetitive transients from <span class="hlt">vibration</span> signals based on Shannon entropy. Unfortunately, the Shannon entropy is maximized for random processes and unable to quantify the repetitive transients buried in heavy random noise. In addition, the <span class="hlt">vibration</span> signals always contain multiple intrinsic oscillatory modes due to interaction and coupling effects between machine components. Under this circumstance, high values of Shannon entropy appear in several frequency bands or high value of Shannon entropy doesn't appear in the optimal frequency band, and the infogram becomes difficult to interpret. Thus, it also becomes difficult to select the optimal frequency band for extracting the repetitive transients from the whole frequency bands. To solve these problems, multiscale fractional order entropy (MSFE) infogram is proposed in this paper. With the help of MSFE infogram, the complexity and nonlinear signatures of the <span class="hlt">vibration</span> signals can be evaluated by quantifying spectral entropy over a range of scales in fractional domain. Moreover, the similarity tolerance of MSFE infogram is helpful for assessing the regularity of signals. A simulation and two experiments concerning a locomotive bearing and a wind turbine gear are used to validate the MSFE infogram. The results demonstrate that the MSFE infogram is more robust to the heavy noise than infogram and the high value is able to only appear in the optimal frequency band for the repetitive transient extraction.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013PhDT.......199S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013PhDT.......199S"><span>Real-Time Condition Monitoring and <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Gear Train Systems Using Instantaneous Angular Speed (IAS) Analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sait, Abdulrahman S.</p> <p></p> <p>This dissertation presents a reliable technique for monitoring the condition of rotating machinery by applying instantaneous angular speed (IAS) analysis. A new analysis of the effects of changes in the orientation of the line of action and the pressure angle of the resultant force acting on gear tooth profile of spur gear under different levels of tooth damage is utilized. The analysis and experimental work discussed in this dissertation provide a clear understating of the effects of damage on the IAS by analyzing the digital signals output of rotary incremental optical encoder. A comprehensive literature review of state of the knowledge in condition monitoring and <span class="hlt">fault</span> diagnostics of rotating machinery, including gearbox system is presented. Progress and new developments over the past 30 years in failure detection techniques of rotating machinery including engines, bearings and gearboxes are thoroughly reviewed. This work is limited to the analysis of a gear train system with gear tooth surface <span class="hlt">faults</span> utilizing angular motion analysis technique. Angular motion data were acquired using an incremental optical encoder. Results are compared to a <span class="hlt">vibration</span>-based technique. The <span class="hlt">vibration</span> data were acquired using an accelerometer. The signals were obtained and analyzed in the phase domains using signal averaging to determine the existence and position of <span class="hlt">faults</span> on the gear train system. Forces between the mating teeth surfaces are analyzed and simulated to validate the influence of the presence of damage on the pressure angle and the IAS. National Instruments hardware is used and NI LabVIEW software code is developed for real-time, online condition monitoring systems and <span class="hlt">fault</span> detection techniques. The sensitivity of optical encoders to gear <span class="hlt">fault</span> detection techniques is experimentally investigated by applying IAS analysis under different gear damage levels and different operating conditions. A reliable methodology is developed for selecting appropriate testing</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1990esfd.book.....J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1990esfd.book.....J"><span>Expert systems for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in nuclear reactor control</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jalel, N. A.; Nicholson, H.</p> <p>1990-11-01</p> <p>An expert system for accident analysis and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for the Loss Of Fluid Test (LOFT) reactor, a small scale pressurized water reactor, was developed for a personal computer. The knowledge of the system is presented using a production rule approach with a backward chaining inference engine. The data base of the system includes simulated dependent state variables of the LOFT reactor model. Another system is designed to assist the operator in choosing the appropriate cooling mode and to diagnose the <span class="hlt">fault</span> in the selected cooling system. The response tree, which is used to provide the link between a list of very specific accident sequences and a set of generic emergency procedures which help the operator in monitoring system status, and to differentiate between different accident sequences and select the correct procedures, is used to build the system knowledge base. Both systems are written in TURBO PROLOG language and can be run on an IBM PC compatible with 640k RAM, 40 Mbyte hard disk and color graphics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25993810','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25993810"><span>[Application of optimized parameters SVM based on photoacoustic spectroscopy method in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of power transformer].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing</p> <p>2015-01-01</p> <p>In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest <span class="hlt">fault</span> <span class="hlt">diagnosis</span> accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span> by gas chromatography and meets the actual project needs in transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060051821&hterms=1094&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3D%2526%25231094','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060051821&hterms=1094&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3D%2526%25231094"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in HVAC Chillers</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Choi, Kihoon; Namuru, Setu M.; Azam, Mohammad S.; Luo, Jianhui; Pattipati, Krishna R.; Patterson-Hine, Ann</p> <p>2005-01-01</p> <p>Modern buildings are being equipped with increasingly sophisticated power and control systems with substantial capabilities for monitoring and controlling the amenities. Operational problems associated with heating, ventilation, and air-conditioning (HVAC) systems plague many commercial buildings, often the result of degraded equipment, failed sensors, improper installation, poor maintenance, and improperly implemented controls. Most existing HVAC <span class="hlt">fault</span>-diagnostic schemes are based on analytical models and knowledge bases. These schemes are adequate for generic systems. However, real-world systems significantly differ from the generic ones and necessitate modifications of the models and/or customization of the standard knowledge bases, which can be labor intensive. Data-driven techniques for <span class="hlt">fault</span> detection and isolation (FDI) have a close relationship with pattern recognition, wherein one seeks to categorize the input-output data into normal or faulty classes. Owing to the simplicity and adaptability, customization of a data-driven FDI approach does not require in-depth knowledge of the HVAC system. It enables the building system operators to improve energy efficiency and maintain the desired comfort level at a reduced cost. In this article, we consider a data-driven approach for FDI of chillers in HVAC systems. To diagnose the <span class="hlt">faults</span> of interest in the chiller, we employ multiway dynamic principal component analysis (MPCA), multiway partial least squares (MPLS), and support vector machines (SVMs). The simulation of a chiller under various <span class="hlt">fault</span> conditions is conducted using a standard chiller simulator from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE). We validated our FDI scheme using experimental data obtained from different types of chiller <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100014076','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100014076"><span>Real-Time <span class="hlt">Diagnosis</span> of <span class="hlt">Faults</span> Using a Bank of Kalman Filters</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kobayashi, Takahisa; Simon, Donald L.</p> <p>2006-01-01</p> <p>A new robust method of automated real-time <span class="hlt">diagnosis</span> of <span class="hlt">faults</span> in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A <span class="hlt">fault</span>-detection-and-isolation (FDI) system, developed based on this method, is able to isolate <span class="hlt">faults</span> in sensors and actuators while detecting component <span class="hlt">faults</span> (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor <span class="hlt">faults</span> before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time <span class="hlt">fault</span> diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient <span class="hlt">faults</span> while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator <span class="hlt">fault</span>. When a sensor</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...70...36G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...70...36G"><span>SVD and Hankel matrix based de-noising approach for ball bearing <span class="hlt">fault</span> detection and its assessment using artificial <span class="hlt">faults</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Golafshan, Reza; Yuce Sanliturk, Kenan</p> <p>2016-03-01</p> <p>Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable <span class="hlt">faults</span>. This incapability in identifying the <span class="hlt">faults</span> makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and <span class="hlt">fault</span> detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain <span class="hlt">vibration</span> signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the <span class="hlt">fault</span> detection process. The test cases conducted using experimental as well as the simulated <span class="hlt">vibration</span> signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing <span class="hlt">fault</span> detection.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JSV...385..372C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JSV...385..372C"><span>Double-dictionary matching pursuit for <span class="hlt">fault</span> extent evaluation of rolling bearing based on the Lempel-Ziv complexity</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cui, Lingli; Gong, Xiangyang; Zhang, Jianyu; Wang, Huaqing</p> <p>2016-12-01</p> <p>The quantitative <span class="hlt">diagnosis</span> of rolling bearing <span class="hlt">fault</span> severity is particularly crucial to realize a proper maintenance decision. Aiming at the <span class="hlt">fault</span> feature of rolling bearing, a novel double-dictionary matching pursuit (DDMP) for <span class="hlt">fault</span> extent evaluation of rolling bearing based on the Lempel-Ziv complexity (LZC) index is proposed in this paper. In order to match the features of rolling bearing <span class="hlt">fault</span>, the impulse time-frequency dictionary and modulation dictionary are constructed to form the double-dictionary by using the method of parameterized function model. Then a novel matching pursuit method is proposed based on the new double-dictionary. For rolling bearing <span class="hlt">vibration</span> signals with different <span class="hlt">fault</span> sizes, the signals are decomposed and reconstructed by the DDMP. After the noise reduced and signals reconstructed, the LZC index is introduced to realize the <span class="hlt">fault</span> extent evaluation. The applications of this method to the <span class="hlt">fault</span> experimental signals of bearing outer race and inner race with different degree of injury have shown that the proposed method can effectively realize the <span class="hlt">fault</span> extent evaluation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..324a2072X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..324a2072X"><span>LCD denoise and the vector mutual information method in the application of the gear <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under different working conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xiangfeng, Zhang; Hong, Jiang</p> <p>2018-03-01</p> <p>In this paper, the full vector LCD method is proposed to solve the misjudgment problem caused by the change of the working condition. First, the signal from different working condition is decomposed by LCD, to obtain the Intrinsic Scale Component (ISC)whose instantaneous frequency with physical significance. Then, calculate of the cross correlation coefficient between ISC and the original signal, signal denoising based on the principle of mutual information minimum. At last, calculate the sum of absolute Vector mutual information of the sample under different working condition and the denoised ISC as the characteristics to classify by use of Support vector machine (SVM). The wind turbines <span class="hlt">vibration</span> platform gear box experiment proves that this method can identify <span class="hlt">fault</span> characteristics under different working conditions. The advantage of this method is that it reduce dependence of man’s subjective experience, identify <span class="hlt">fault</span> directly from the original data of <span class="hlt">vibration</span> signal. It will has high engineering value.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..103...60W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..103...60W"><span>Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin</p> <p>2018-03-01</p> <p>A time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Local mean decomposition (LMD), as an adaptive non-stationary and nonlinear signal processing method, provides the capability to decompose multicomponent modulation signal into a series of demodulated mono-components. However, the occurring mode mixing is a serious drawback. To alleviate this, ELMD based on noise-assisted method was developed. Still, the existing environmental noise in the raw signal remains in corresponding PF with the component of interest. FK has good performance in impulse detection while strong environmental noise exists. But it is susceptible to non-Gaussian noise. The proposed method combines the merits of ELMD and FK to detect the <span class="hlt">fault</span> for rotating machinery. Primarily, by applying ELMD the raw signal is decomposed into a set of product functions (PFs). Then, the PF which mostly characterizes <span class="hlt">fault</span> information is selected according to kurtosis index. Finally, the selected PF signal is further filtered by an optimal band-pass filter based on FK to extract impulse signal. <span class="hlt">Fault</span> identification can be deduced by the appearance of <span class="hlt">fault</span> characteristic frequencies in the squared envelope spectrum of the filtered signal. The advantages of ELMD over LMD and EEMD are illustrated in the simulation analyses. Furthermore, the efficiency of the proposed method in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for rotating machinery is demonstrated on gearbox case and rolling bearing case analyses.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4851063','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4851063"><span><span class="hlt">Fault</span> Detection and <span class="hlt">Diagnosis</span> of Railway Point Machines by Sound Analysis</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan</p> <p>2016-01-01</p> <p>Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose <span class="hlt">faults</span> in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and <span class="hlt">diagnosis</span> of <span class="hlt">faults</span> using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods. PMID:27092509</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940011075','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940011075"><span><span class="hlt">Fault</span> management for data systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Boyd, Mark A.; Iverson, David L.; Patterson-Hine, F. Ann</p> <p>1993-01-01</p> <p>Issues related to automating the process of <span class="hlt">fault</span> management (<span class="hlt">fault</span> <span class="hlt">diagnosis</span> and response) for data management systems are considered. Substantial benefits are to be gained by successful automation of this process, particularly for large, complex systems. The use of graph-based models to develop a computer assisted <span class="hlt">fault</span> management system is advocated. The general problem is described and the motivation behind choosing graph-based models over other approaches for developing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> computer programs is outlined. Some existing work in the area of graph-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is reviewed, and a new <span class="hlt">fault</span> management method which was developed from existing methods is offered. Our method is applied to an automatic telescope system intended as a prototype for future lunar telescope programs. Finally, an application of our method to general data management systems is described.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1351548','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1351548"><span>Model-Based Sensor Placement for Component Condition Monitoring and <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Fossil Energy Systems</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Mobed, Parham; Pednekar, Pratik; Bhattacharyya, Debangsu</p> <p></p> <p>Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desiredmore » for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate <span class="hlt">fault</span> severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SPIE10200E..1EG','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SPIE10200E..1EG"><span>Development of a variable structure-based <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> strategy applied to an electromechanical system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gadsden, S. Andrew; Kirubarajan, T.</p> <p>2017-05-01</p> <p>Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span>, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and <span class="hlt">diagnosis</span> <span class="hlt">faults</span> in an electromechanical system. The results are compared with the KF method, and future work is discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19890005382&hterms=aircraft+diagnosis+expert+system&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Daircraft%2Bdiagnosis%2Bexpert%2Bsystem','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19890005382&hterms=aircraft+diagnosis+expert+system&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Daircraft%2Bdiagnosis%2Bexpert%2Bsystem"><span>A flight expert system (FLES) for on-board <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ali, Moonis; Scharnhorst, D. A.; Ai, C. S.; Feber, H. J.</p> <p>1987-01-01</p> <p>The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher workloads for the pilot and tend to induce a higher probability of errors. Therefore, an architecture for a flight expert system (FLES) is developed to assist pilots in monitoring, diagnosing and recovering from in-flight <span class="hlt">faults</span>. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with airplane status information during the diagnostic process. The simulator is based on the Lockheed Advanced Concept System (ACS), a future generation airplane, and on the Boeing 737. A distinction between two types of <span class="hlt">faults</span>, maladjustments and malfunctions, has led to two approaches to <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to access abnormal situations during airplane flight. Malfunctions and maladjustments are handled separately, diagnosed using domain knowledge.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AcAau..65..710P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AcAau..65..710P"><span><span class="hlt">Fault</span> detection and <span class="hlt">diagnosis</span> in a spacecraft attitude determination system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pirmoradi, F. N.; Sassani, F.; de Silva, C. W.</p> <p>2009-09-01</p> <p>This paper presents a new scheme for <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) in spacecraft attitude determination (AD) sensors. An integrated attitude determination system, which includes measurements of rate and angular position using rate gyros and vector sensors, is developed. Measurement data from all sensors are fused by a linearized Kalman filter, which is designed based on the system kinematics, to provide attitude estimation and the values of the gyro bias. Using this information the erroneous sensor measurements are corrected, and unbounded sensor measurement errors are avoided. The resulting bias-free data are used in the FDD scheme. The FDD algorithm uses model-based state estimation, combining the information from the rotational dynamics and kinematics of a spacecraft with the sensor measurements to predict the future sensor outputs. <span class="hlt">Fault</span> isolation is performed through extended Kalman filters (EKFs). The innovation sequences of EKFs are monitored by several statistical tests to detect the presence of a failure and to localize the failures in all AD sensors. The isolation procedure is developed in two phases. In the first phase, two EKFs are designed, which use subsets of measurements to provide state estimates and form residuals, which are used to verify the source of the <span class="hlt">fault</span>. In the second phase of isolation, testing of multiple hypotheses is performed. The generalized likelihood ratio test is utilized to identify the faulty components. In the scheme developed in this paper a relatively small number of hypotheses is used, which results in faster isolation and highly distinguishable <span class="hlt">fault</span> signatures. An important feature of the developed FDD scheme is that it can provide attitude estimations even if only one type of sensors is functioning properly.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MS%26E..231a2015L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MS%26E..231a2015L"><span>Research of converter transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on improved PSO-BP algorithm</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Long, Qi; Guo, Shuyong; Li, Qing; Sun, Yong; Li, Yi; Fan, Youping</p> <p>2017-09-01</p> <p>To overcome those disadvantages that BP (Back Propagation) neural network and conventional Particle Swarm Optimization (PSO) converge at the global best particle repeatedly in early stage and is easy trapped in local optima and with low <span class="hlt">diagnosis</span> accuracy when being applied in converter transformer <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, we come up with the improved PSO-BP neural network to improve the accuracy rate. This algorithm improves the inertia weight Equation by using the attenuation strategy based on concave function to avoid the premature convergence of PSO algorithm and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. At last the simulation results prove that the proposed approach has a better ability in optimizing BP neural network in terms of network output error, global searching performance and <span class="hlt">diagnosis</span> accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20020014794&hterms=service+processes&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dservice%2Bprocesses','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20020014794&hterms=service+processes&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dservice%2Bprocesses"><span><span class="hlt">Fault</span> Tree Based <span class="hlt">Diagnosis</span> with Optimal Test Sequencing for Field Service Engineers</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Iverson, David L.; George, Laurence L.; Patterson-Hine, F. A.; Lum, Henry, Jr. (Technical Monitor)</p> <p>1994-01-01</p> <p>When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to <span class="hlt">diagnosis</span>. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the <span class="hlt">Fault</span> Tree <span class="hlt">Diagnosis</span> and Optimal Test Sequence (FTDOTS) software system that performs automated <span class="hlt">diagnosis</span> and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a <span class="hlt">fault</span> tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a <span class="hlt">fault</span> tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28524090','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28524090"><span>Wind Turbine <span class="hlt">Diagnosis</span> under Variable Speed Conditions Using a Single Sensor Based on the Synchrosqueezing Transform Method.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Guo, Yanjie; Chen, Xuefeng; Wang, Shibin; Sun, Ruobin; Zhao, Zhibin</p> <p>2017-05-18</p> <p>The gearbox is one of the key components in wind turbines. Gearbox <span class="hlt">fault</span> signals are usually nonstationary and highly contaminated with noise. The presence of amplitude-modulated and frequency-modulated (AM-FM) characteristics compound the difficulty of precise <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of wind turbines, therefore, it is crucial to develop an effective <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for such equipment. This paper presents an improved <span class="hlt">diagnosis</span> method for wind turbines via the combination of synchrosqueezing transform and local mean decomposition. Compared to the conventional time-frequency analysis techniques, the improved method which is performed in non-real-time can effectively reduce the noise pollution of the signals and preserve the signal characteristics, and hence is suitable for the analysis of nonstationary signals with high noise. This method is further validated by simulated signals and practical <span class="hlt">vibration</span> data measured from a 1.5 MW wind turbine. The results confirm that the proposed method can simultaneously control the noise and increase the accuracy of time-frequency representation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5470895','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5470895"><span>Wind Turbine <span class="hlt">Diagnosis</span> under Variable Speed Conditions Using a Single Sensor Based on the Synchrosqueezing Transform Method</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Guo, Yanjie; Chen, Xuefeng; Wang, Shibin; Sun, Ruobin; Zhao, Zhibin</p> <p>2017-01-01</p> <p>The gearbox is one of the key components in wind turbines. Gearbox <span class="hlt">fault</span> signals are usually nonstationary and highly contaminated with noise. The presence of amplitude-modulated and frequency-modulated (AM-FM) characteristics compound the difficulty of precise <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of wind turbines, therefore, it is crucial to develop an effective <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for such equipment. This paper presents an improved <span class="hlt">diagnosis</span> method for wind turbines via the combination of synchrosqueezing transform and local mean decomposition. Compared to the conventional time-frequency analysis techniques, the improved method which is performed in non-real-time can effectively reduce the noise pollution of the signals and preserve the signal characteristics, and hence is suitable for the analysis of nonstationary signals with high noise. This method is further validated by simulated signals and practical <span class="hlt">vibration</span> data measured from a 1.5 MW wind turbine. The results confirm that the proposed method can simultaneously control the noise and increase the accuracy of time-frequency representation. PMID:28524090</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ammi.conf..311Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ammi.conf..311Z"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> System of Wind Turbine Generator Based on Petri Net</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Han</p> <p></p> <p>Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine <span class="hlt">fault</span> <span class="hlt">diagnosis</span> have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27390200','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27390200"><span>A Negative Selection Immune System Inspired Methodology for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Wind Turbines.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Alizadeh, Esmaeil; Meskin, Nader; Khorasani, Khashayar</p> <p>2017-11-01</p> <p>High operational and maintenance costs represent as major economic constraints in the wind turbine (WT) industry. These concerns have made investigation into <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of WT systems an extremely important and active area of research. In this paper, an immune system (IS) inspired methodology for performing <span class="hlt">fault</span> detection and isolation (FDI) of a WT system is proposed and developed. The proposed scheme is based on a self nonself discrimination paradigm of a biological IS. Specifically, the negative selection mechanism [negative selection algorithm (NSA)] of the human body is utilized. In this paper, a hierarchical bank of NSAs are designed to detect and isolate both individual as well as simultaneously occurring <span class="hlt">faults</span> common to the WTs. A smoothing moving window filter is then utilized to further improve the reliability and performance of the FDI scheme. Moreover, the performance of our proposed scheme is compared with another state-of-the-art data-driven technique, namely the support vector machines (SVMs) to demonstrate and illustrate the superiority and advantages of our proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate our proposed methodology with that of the SVM under various <span class="hlt">fault</span> severities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/9556260','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/9556260"><span>Is intrasound <span class="hlt">vibration</span> useful in the <span class="hlt">diagnosis</span> of occult scaphoid fractures?</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Roolker, L; Tiel-van Buul, M M; Broekhuizen, T H</p> <p>1998-03-01</p> <p>This study was designed to confirm the results of Finkenberg et al. (J Hand Surg 1993;18A: 4-7), who found a high sensitivity (100%) and specificity (95%) of the intrasound <span class="hlt">vibration</span> method in diagnosing occult scaphoid fractures. These occult scaphoid fractures are not visible on x-ray films, but clinically the patients are suspected of having a scaphoid fracture. A vibratory apparatus is placed over the anatomical snuff-box and a <span class="hlt">vibration</span> of 100 mW is emitted; a painful sensation is produced if the scaphoid is fractured. Thirty-seven consecutive patients with a clinically suspected scaphoid fracture were evaluated. In 6 patients, a scaphoid fracture was radiographically identified; in the remaining 31 patients, a 3-phase bone scan was obtained. Eleven wrists showed increased uptake over the scaphoid and were considered to have an occult scaphoid fracture. In this group, bone scintigraphy was used as the reference standard. The <span class="hlt">vibration</span> test was painful in 1 of 6 patients with a proven scaphoid fracture and in 3 of the 11 patients with a positive bone scan. In contrast to the results of Finkenberg et al, the intrasound <span class="hlt">vibration</span> method shows a sensitivity of 24%, a specificity of 85%, a positive predictive value of 40%, and a negative predictive value of 65%. We conclude that the accuracy of intrasound <span class="hlt">vibration</span> is low and that it is not useful in the <span class="hlt">diagnosis</span> of scaphoid fractures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1002400','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1002400"><span>Characterization of <span class="hlt">Fault</span> Size in Bearings</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-12-23</p> <p>suggests to use the ratio between the horizontal and the vertical RMS as an indicator of the <span class="hlt">fault</span> location is not applicable for small <span class="hlt">faults</span>. Since...<span class="hlt">Vibration</span> Monitoring of rolling element bearing by the high- frequency resonance technique - a review, Tribology international, Vol. 17, pp 3-10. M</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JSV...421..205Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JSV...421..205Y"><span>Sliding window denoising K-Singular Value Decomposition and its application on rolling bearing impact <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, Honggang; Lin, Huibin; Ding, Kang</p> <p>2018-05-01</p> <p>The performance of sparse features extraction by commonly used K-Singular Value Decomposition (K-SVD) method depends largely on the signal segment selected in rolling bearing <span class="hlt">diagnosis</span>, furthermore, the calculating speed is relatively slow and the dictionary becomes so redundant when the <span class="hlt">fault</span> signal is relatively long. A new sliding window denoising K-SVD (SWD-KSVD) method is proposed, which uses only one small segment of time domain signal containing impacts to perform sliding window dictionary learning and select an optimal pattern with oscillating information of the rolling bearing <span class="hlt">fault</span> according to a maximum variance principle. An inner product operation between the optimal pattern and the whole <span class="hlt">fault</span> signal is performed to enhance the characteristic of the impacts' occurrence moments. Lastly, the signal is reconstructed at peak points of the inner product to realize the extraction of the rolling bearing <span class="hlt">fault</span> features. Both simulation and experiments verify that the method could extract the <span class="hlt">fault</span> features effectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP...98..852L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP...98..852L"><span>Dynamic modeling of gearbox <span class="hlt">faults</span>: A review</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liang, Xihui; Zuo, Ming J.; Feng, Zhipeng</p> <p>2018-01-01</p> <p>Gearbox is widely used in industrial and military applications. Due to high service load, harsh operating conditions or inevitable fatigue, <span class="hlt">faults</span> may develop in gears. If the gear <span class="hlt">faults</span> cannot be detected early, the health will continue to degrade, perhaps causing heavy economic loss or even catastrophe. Early <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> allows properly scheduled shutdowns to prevent catastrophic failure and consequently result in a safer operation and higher cost reduction. Recently, many studies have been done to develop gearbox dynamic models with <span class="hlt">faults</span> aiming to understand gear <span class="hlt">fault</span> generation mechanism and then develop effective <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> methods. This paper focuses on dynamics based gearbox <span class="hlt">fault</span> modeling, detection and <span class="hlt">diagnosis</span>. State-of-art and challenges are reviewed and discussed. This detailed literature review limits research results to the following fundamental yet key aspects: gear mesh stiffness evaluation, gearbox damage modeling and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> techniques, gearbox transmission path modeling and method validation. In the end, a summary and some research prospects are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19780015853','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19780015853"><span>Critical <span class="hlt">fault</span> patterns determination in <span class="hlt">fault</span>-tolerant computer systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Mccluskey, E. J.; Losq, J.</p> <p>1978-01-01</p> <p>The method proposed tries to enumerate all the critical <span class="hlt">fault</span>-patterns (successive occurrences of failures) without analyzing every single possible <span class="hlt">fault</span>. The conditions for the system to be operating in a given mode can be expressed in terms of the static states. Thus, one can find all the system states that correspond to a given critical mode of operation. The next step consists in analyzing the <span class="hlt">fault</span>-detection mechanisms, the <span class="hlt">diagnosis</span> algorithm and the process of switch control. From them, one can find all the possible system configurations that can result from a failure occurrence. Thus, one can list all the characteristics, with respect to detection, <span class="hlt">diagnosis</span>, and switch control, that failures must have to constitute critical <span class="hlt">fault</span>-patterns. Such an enumeration of the critical <span class="hlt">fault</span>-patterns can be directly used to evaluate the overall system tolerance to failures. Present research is focused on how to efficiently make use of these system-level characteristics to enumerate all the failures that verify these characteristics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23145702','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23145702"><span>Real time automatic detection of bearing <span class="hlt">fault</span> in induction machine using kurtogram analysis.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tafinine, Farid; Mokrani, Karim</p> <p>2012-11-01</p> <p>A proposed signal processing technique for incipient real time bearing <span class="hlt">fault</span> detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary <span class="hlt">vibration</span> signal and for real time <span class="hlt">diagnosis</span>. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing <span class="hlt">fault</span> automatic detection method and gives a good basis for an integrated induction machine condition monitor.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..101..435L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..101..435L"><span>Train axle bearing <span class="hlt">fault</span> detection using a feature selection scheme based multi-scale morphological filter</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin</p> <p>2018-02-01</p> <p>This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing <span class="hlt">fault</span> detection. In this scheme, more than 30 feature indicators of <span class="hlt">vibration</span> signals are calculated for axle bearings with different conditions and the features which can reflect <span class="hlt">fault</span> characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on <span class="hlt">diagnosis</span> of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting <span class="hlt">fault</span> features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5713071','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5713071"><span><span class="hlt">Fault</span> Detection of Bearing Systems through EEMD and Optimization Algorithm</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan</p> <p>2017-01-01</p> <p>This study proposes a <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate <span class="hlt">vibration</span> signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing <span class="hlt">vibration</span> signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014IJBC...2450151H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014IJBC...2450151H"><span>From <span class="hlt">Fault-Diagnosis</span> and Performance Recovery of a Controlled System to Chaotic Secure Communication</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hsu, Wen-Teng; Tsai, Jason Sheng-Hong; Guo, Fang-Cheng; Guo, Shu-Mei; Shieh, Leang-San</p> <p></p> <p>Chaotic systems are often applied to encryption on secure communication, but they may not provide high-degree security. In order to improve the security of communication, chaotic systems may need to add other secure signals, but this may cause the system to diverge. In this paper, we redesign a communication scheme that could create secure communication with additional secure signals, and the proposed scheme could keep system convergence. First, we introduce the universal state-space adaptive observer-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span>/estimator and the high-performance tracker for the sampled-data linear time-varying system with unanticipated decay factors in actuators/system states. Besides, robustness, convergence in the mean, and tracking ability are given in this paper. A residual generation scheme and a mechanism for auto-tuning switched gain is also presented, so that the introduced methodology is applicable for the <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) for actuator and state <span class="hlt">faults</span> to yield a high tracking performance recovery. The evolutionary programming-based adaptive observer is then applied to the problem of secure communication. Whenever the tracker induces a large control input which might not conform to the input constraint of some physical systems, the proposed modified linear quadratic optimal tracker (LQT) can effectively restrict the control input within the specified constraint interval, under the acceptable tracking performance. The effectiveness of the proposed design methodology is illustrated through tracking control simulation examples.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5876526','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5876526"><span>Research of Planetary Gear <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Permutation Entropy of CEEMDAN and ANFIS</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kuai, Moshen; Cheng, Gang; Li, Yong</p> <p>2018-01-01</p> <p>For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing <span class="hlt">faults</span> in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear <span class="hlt">faults</span>, time complexity of IMFs are reflected by permutation entropies to quantify the <span class="hlt">fault</span> features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different <span class="hlt">fault</span> gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear <span class="hlt">fault</span> <span class="hlt">diagnosis</span> effectively. PMID:29510569</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29510569','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29510569"><span>Research of Planetary Gear <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Based on Permutation Entropy of CEEMDAN and ANFIS.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kuai, Moshen; Cheng, Gang; Pang, Yusong; Li, Yong</p> <p>2018-03-05</p> <p>For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing <span class="hlt">faults</span> in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear <span class="hlt">faults</span>, time complexity of IMFs are reflected by permutation entropies to quantify the <span class="hlt">fault</span> features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different <span class="hlt">fault</span> gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear <span class="hlt">fault</span> <span class="hlt">diagnosis</span> effectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...83..356L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...83..356L"><span>Sinusoidal synthesis based adaptive tracking for rotating machinery <span class="hlt">fault</span> detection</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Gang; McDonald, Geoff L.; Zhao, Qing</p> <p>2017-01-01</p> <p>This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for <span class="hlt">vibration</span>-based rotating machinery <span class="hlt">fault</span> detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of <span class="hlt">vibration</span> signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the <span class="hlt">vibration</span> signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed <span class="hlt">vibration</span> synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of <span class="hlt">vibration</span> signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-<span class="hlt">fault</span> condition which makes the algorithm ideal for on-line <span class="hlt">fault</span> detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the <span class="hlt">fault</span> detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JSV...423..340Q','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JSV...423..340Q"><span>Investigation on the subsynchronous pseudo-<span class="hlt">vibration</span> of rotating machinery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Qu, Lei; Liao, Yuhe; Lin, Jing; Zhao, Ming</p> <p>2018-06-01</p> <p>Subsynchronous pseudo-<span class="hlt">vibration</span> (SPV) of rotating machinery is one of the primary reasons for <span class="hlt">fault</span> misdiagnosis. SPV has similar signal signatures to those of a real <span class="hlt">fault</span>, which usually leads to excessive maintenance or even unscheduled shutdown. For this reason, it is essential to investigate the root causes of SPV so as to reduce unnecessary downtime and maintenance cost. Aiming at this issue, a novel signal model for rotor non-contact <span class="hlt">vibration</span> is built to describe the generating mechanism of one kind of SPV by considering the combined effects of rotor axial motion and detection surface runout on <span class="hlt">vibration</span> signal. To obtain more discriminative <span class="hlt">fault</span> features from the <span class="hlt">vibration</span> signals, the two-dimension holospectrum (2DH) is employed to integrate the phase information from two perpendicularly installed sensors. The characteristics of precession orbit used to describe the lateral motion of a rotor could be fully extracted by 2DH. It is shown that the precession orbit at the <span class="hlt">fault</span> feature frequency will degenerate into a straight line when SPV occurs, and thus the eccentricity of precession orbit could be considered as a key feature for discriminating SPV from real subsynchronous <span class="hlt">vibration</span> (RSV). The effectiveness of proposed method was validated on a rotor test rig. By using this method, the SPV problem of a real gearbox in a blast furnace blower set in a steel mill was successfully diagnosed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3545588','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3545588"><span>Spectral Regression Based <span class="hlt">Fault</span> Feature Extraction for Bearing Accelerometer Sensor Signals</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu</p> <p>2012-01-01</p> <p>Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing <span class="hlt">fault</span> prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, <span class="hlt">fault</span> feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and prognosis. This paper proposes a spectral regression (SR)-based approach for <span class="hlt">fault</span> feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired <span class="hlt">vibration</span> signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing <span class="hlt">faults</span> and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19920015067','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19920015067"><span>Real-time antenna <span class="hlt">fault</span> <span class="hlt">diagnosis</span> experiments at DSS 13</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Mellstrom, J.; Pierson, C.; Smyth, P.</p> <p>1992-01-01</p> <p>Experimental results obtained when a previously described <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system was run online in real time at the 34-m beam waveguide antenna at Deep Space Station (DSS) 13 are described. Experimental conditions and the quality of results are described. A neural network model and a maximum-likelihood Gaussian classifier are compared with and without a Markov component to model temporal context. At the rate of a state update every 6.4 seconds, over a period of roughly 1 hour, the neural-Markov system had zero errors (incorrect state estimates) while monitoring both faulty and normal operations. The overall results indicate that the neural-Markov combination is the most accurate model and has significant practical potential.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890003781','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890003781"><span><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> based on continuous simulation models</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Feyock, Stefan</p> <p>1987-01-01</p> <p>The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the <span class="hlt">diagnosis</span> of system <span class="hlt">faults</span>. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...81..202S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...81..202S"><span>Pseudo-<span class="hlt">fault</span> signal assisted EMD for <span class="hlt">fault</span> detection and isolation in rotating machines</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Singh, Dheeraj Sharan; Zhao, Qing</p> <p>2016-12-01</p> <p>This paper presents a novel data driven technique for the detection and isolation of <span class="hlt">faults</span>, which generate impacts in a rotating equipment. The technique is built upon the principles of empirical mode decomposition (EMD), envelope analysis and pseudo-<span class="hlt">fault</span> signal for <span class="hlt">fault</span> separation. Firstly, the most dominant intrinsic mode function (IMF) is identified using EMD of a raw signal, which contains all the necessary information about the <span class="hlt">faults</span>. The envelope of this IMF is often modulated with multiple <span class="hlt">vibration</span> sources and noise. A second level decomposition is performed by applying pseudo-<span class="hlt">fault</span> signal (PFS) assisted EMD on the envelope. A pseudo-<span class="hlt">fault</span> signal is constructed based on the known <span class="hlt">fault</span> characteristic frequency of the particular machine. The objective of using external (pseudo-<span class="hlt">fault</span>) signal is to isolate different <span class="hlt">fault</span> frequencies, present in the envelope . The pseudo-<span class="hlt">fault</span> signal serves dual purposes: (i) it solves the mode mixing problem inherent in EMD, (ii) it isolates and quantifies a particular <span class="hlt">fault</span> frequency component. The proposed technique is suitable for real-time implementation, which has also been validated on simulated <span class="hlt">fault</span> and experimental data corresponding to a bearing and a gear-box set-up, respectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5795768','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5795768"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Burriel-Valencia, Jordi; Martinez-Roman, Javier; Sapena-Bano, Angel</p> <p>2018-01-01</p> <p>The aim of this paper is to introduce a new methodology for the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the <span class="hlt">fault</span> components in the current’s spectrogram with a significant reduction of the required computational resources. PMID:29316650</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29316650','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29316650"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Burriel-Valencia, Jordi; Puche-Panadero, Ruben; Martinez-Roman, Javier; Sapena-Bano, Angel; Pineda-Sanchez, Manuel</p> <p>2018-01-06</p> <p>The aim of this paper is to introduce a new methodology for the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the <span class="hlt">fault</span> components in the current's spectrogram with a significant reduction of the required computational resources.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...97..112A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...97..112A"><span>Feedback on the Surveillance 8 challenge: <span class="hlt">Vibration</span>-based <span class="hlt">diagnosis</span> of a Safran aircraft engine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Antoni, Jérôme; Griffaton, Julien; André, Hugo; Avendaño-Valencia, Luis David; Bonnardot, Frédéric; Cardona-Morales, Oscar; Castellanos-Dominguez, German; Daga, Alessandro Paolo; Leclère, Quentin; Vicuña, Cristián Molina; Acuña, David Quezada; Ompusunggu, Agusmian Partogi; Sierra-Alonso, Edgar F.</p> <p>2017-12-01</p> <p>This paper presents the content and outcomes of the Safran contest organized during the International Conference Surveillance 8, October 20-21, 2015, at the Roanne Institute of Technology, France. The contest dealt with the <span class="hlt">diagnosis</span> of a civil aircraft engine based on <span class="hlt">vibration</span> data measured in a transient operating mode and provided by Safran. Based on two independent exercises, the contest offered the possibility to benchmark current diagnostic methods on real data supplemented with several challenges. Outcomes of seven competing teams are reported and discussed. The object of the paper is twofold. It first aims at giving a picture of the current state-of-the-art in <span class="hlt">vibration</span>-based <span class="hlt">diagnosis</span> of rolling-element bearings in nonstationary operating conditions. Second, it aims at providing the scientific community with a benchmark and some baseline solutions. In this respect, the data used in the contest are made available as supplementary material.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://eric.ed.gov/?q=robot&pg=6&id=EJ1144883','ERIC'); return false;" href="https://eric.ed.gov/?q=robot&pg=6&id=EJ1144883"><span>Mobile Robot Lab Project to Introduce Engineering Students to <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Mechatronic Systems</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Gómez-de-Gabriel, Jesús Manuel; Mandow, Anthony; Fernández-Lozano, Jesús; García-Cerezo, Alfonso</p> <p>2015-01-01</p> <p>This paper proposes lab work for learning <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) in mechatronic systems. These skills are important for engineering education because FDD is a key capability of competitive processes and products. The intended outcome of the lab work is that students become aware of the importance of faulty conditions and learn to…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4541939','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4541939"><span>AF-DHNN: Fuzzy Clustering and Inference-Based Node <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Method for Fire Detection</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying</p> <p>2015-01-01</p> <p>Wireless Sensor Networks (WSNs) have been utilized for node <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new <span class="hlt">diagnosis</span> mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the <span class="hlt">fault</span> determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node <span class="hlt">faults</span> promptly and effectively, which improves the WSN reliability. PMID:26193280</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010048000','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010048000"><span>Comparison of <span class="hlt">Fault</span> Detection Algorithms for Real-time <span class="hlt">Diagnosis</span> in Large-Scale System. Appendix E</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kirubarajan, Thiagalingam; Malepati, Venkat; Deb, Somnath; Ying, Jie</p> <p>2001-01-01</p> <p>In this paper, we present a review of different real-time capable algorithms to detect and isolate component failures in large-scale systems in the presence of inaccurate test results. A sequence of imperfect test results (as a row vector of I's and O's) are available to the algorithms. In this case, the problem is to recover the uncorrupted test result vector and match it to one of the rows in the test dictionary, which in turn will isolate the <span class="hlt">faults</span>. In order to recover the uncorrupted test result vector, one needs the accuracy of each test. That is, its detection and false alarm probabilities are required. In this problem, their true values are not known and, therefore, have to be estimated online. Other major aspects in this problem are the large-scale nature and the real-time capability requirement. Test dictionaries of sizes up to 1000 x 1000 are to be handled. That is, results from 1000 tests measuring the state of 1000 components are available. However, at any time, only 10-20% of the test results are available. Then, the objective becomes the real-time <span class="hlt">fault</span> <span class="hlt">diagnosis</span> using incomplete and inaccurate test results with online estimation of test accuracies. It should also be noted that the test accuracies can vary with time --- one needs a mechanism to update them after processing each test result vector. Using Qualtech's TEAMS-RT (system simulation and real-time <span class="hlt">diagnosis</span> tool), we test the performances of 1) TEAMSAT's built-in <span class="hlt">diagnosis</span> algorithm, 2) Hamming distance based <span class="hlt">diagnosis</span>, 3) Maximum Likelihood based <span class="hlt">diagnosis</span>, and 4) HidderMarkov Model based <span class="hlt">diagnosis</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/1179093-fault-diagnosis-multi-state-alarms-nuclear-power-control-simulation','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1179093-fault-diagnosis-multi-state-alarms-nuclear-power-control-simulation"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> with Multi-State Alarms in a Nuclear Power Control Simulation</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Stuart A. Ragsdale; Roger Lew; Ronald L. Boring</p> <p>2014-09-01</p> <p>This research addresses how alarm systems can increase operator performance within nuclear power plant operations. The experiment examined the effects of two types of alarm systems (two-state and three-state alarms) on alarm compliance and <span class="hlt">diagnosis</span> for two types of <span class="hlt">faults</span> differing in complexity. We hypothesized the use of three-state alarms would improve performance in alarm recognition and <span class="hlt">fault</span> diagnoses over that of two-state alarms. Sensitivity and criterion based on the Signal Detection Theory were used to measure performance. We further hypothesized that operator trust would be highest when using three-state alarms. The findings from this research showed participants performed bettermore » and had more trust in three-state alarms compared to two-state alarms. Furthermore, these findings have significant theoretical implications and practical applications as they apply to improving the efficiency and effectiveness of nuclear power plant operations.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MS%26E..283a2009R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MS%26E..283a2009R"><span>Research on rolling element bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on genetic algorithm matching pursuit</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rong, R. W.; Ming, T. F.</p> <p>2017-12-01</p> <p>In order to solve the problem of slow computation speed, matching pursuit algorithm is applied to rolling bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, and the improvement are conducted from two aspects that are the construction of dictionary and the way to search for atoms. To be specific, Gabor function which can reflect time-frequency localization characteristic well is used to construct the dictionary, and the genetic algorithm to improve the searching speed. A time-frequency analysis method based on genetic algorithm matching pursuit (GAMP) algorithm is proposed. The way to set property parameters for the improvement of the decomposition results is studied. Simulation and experimental results illustrate that the weak <span class="hlt">fault</span> feature of rolling bearing can be extracted effectively by this proposed method, at the same time, the computation speed increases obviously.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19910007729','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19910007729"><span>Flight elements: <span class="hlt">Fault</span> detection and <span class="hlt">fault</span> management</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lum, H.; Patterson-Hine, A.; Edge, J. T.; Lawler, D.</p> <p>1990-01-01</p> <p><span class="hlt">Fault</span> management for an intelligent computational system must be developed using a top down integrated engineering approach. An approach proposed includes integrating the overall environment involving sensors and their associated data; design knowledge capture; operations; <span class="hlt">fault</span> detection, identification, and reconfiguration; testability; causal models including digraph matrix analysis; and overall performance impacts on the hardware and software architecture. Implementation of the concept to achieve a real time intelligent <span class="hlt">fault</span> detection and management system will be accomplished via the implementation of several objectives, which are: Development of <span class="hlt">fault</span> tolerant/FDIR requirement and specification from a systems level which will carry through from conceptual design through implementation and mission operations; Implementation of monitoring, <span class="hlt">diagnosis</span>, and reconfiguration at all system levels providing <span class="hlt">fault</span> isolation and system integration; Optimize system operations to manage degraded system performance through system integration; and Lower development and operations costs through the implementation of an intelligent real time <span class="hlt">fault</span> detection and <span class="hlt">fault</span> management system and an information management system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900051008&hterms=knowledge+power&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dknowledge%2Bpower','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900051008&hterms=knowledge+power&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dknowledge%2Bpower"><span>An architecture for automated <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. [Space Station Module/Power Management And Distribution</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ashworth, Barry R.</p> <p>1989-01-01</p> <p>A description is given of the SSM/PMAD power system automation testbed, which was developed using a systems engineering approach. The architecture includes a knowledge-based system and has been successfully used in power system management and <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. Architectural issues which effect overall system activities and performance are examined. The knowledge-based system is discussed along with its associated automation implications, and interfaces throughout the system are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24296116','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24296116"><span>An approach for automated <span class="hlt">fault</span> <span class="hlt">diagnosis</span> based on a fuzzy decision tree and boundary analysis of a reconstructed phase space.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Aydin, Ilhan; Karakose, Mehmet; Akin, Erhan</p> <p>2014-03-01</p> <p>Although reconstructed phase space is one of the most powerful methods for analyzing a time series, it can fail in <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of an induction motor when the appropriate pre-processing is not performed. Therefore, boundary analysis based a new feature extraction method in phase space is proposed for <span class="hlt">diagnosis</span> of induction motor <span class="hlt">faults</span>. The proposed approach requires the measurement of one phase current signal to construct the phase space representation. Each phase space is converted into an image, and the boundary of each image is extracted by a boundary detection algorithm. A fuzzy decision tree has been designed to detect broken rotor bars and broken connector <span class="hlt">faults</span>. The results indicate that the proposed approach has a higher recognition rate than other methods on the same dataset. © 2013 ISA Published by ISA All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...81..126G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...81..126G"><span>A data-driven method to enhance <span class="hlt">vibration</span> signal decomposition for rolling bearing <span class="hlt">fault</span> analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grasso, M.; Chatterton, S.; Pennacchi, P.; Colosimo, B. M.</p> <p>2016-12-01</p> <p>Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible <span class="hlt">fault</span> features. Time-frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a <span class="hlt">fault</span>. The separation of components belonging to different time-frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze <span class="hlt">vibration</span> signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing <span class="hlt">fault</span> analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5038782','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5038782"><span>Sensor Data Fusion with Z-Numbers and Its Application in <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jiang, Wen; Xie, Chunhe; Zhuang, Miaoyan; Shou, Yehang; Tang, Yongchuan</p> <p>2016-01-01</p> <p>Sensor data fusion technology is widely employed in <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-number, Z = (A, B), can represent the fuzziness and the reliability of information simultaneously, where the first component A represents a fuzzy restriction on the values of uncertain variables and the second component B is a measure of the reliability of A. In order to model and process the uncertainties in a sensor data fusion system reasonably, in this paper, a novel method combining the Z-number and Dempster–Shafer (D-S) evidence theory is proposed, where the Z-number is used to model the fuzziness and reliability of the sensor data and the D-S evidence theory is used to fuse the uncertain information of Z-numbers. The main advantages of the proposed method are that it provides a more robust measure of reliability to the sensor data, and the complementary information of multi-sensors reduces the uncertainty of the <span class="hlt">fault</span> recognition, thus enhancing the reliability of <span class="hlt">fault</span> detection. PMID:27649193</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890006195','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890006195"><span>Graph-based real-time <span class="hlt">fault</span> diagnostics</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Padalkar, S.; Karsai, G.; Sztipanovits, J.</p> <p>1988-01-01</p> <p>A real-time <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based <span class="hlt">fault</span> diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy <span class="hlt">diagnosis</span>. In this paper we present a graph-based technique which is well suited for real-time <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, structured knowledge representation and acquisition and testing and validation. A Hierarchical <span class="hlt">Fault</span> Model of the system to be diagnosed is developed. At each level of hierarchy, there exist <span class="hlt">fault</span> propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with <span class="hlt">fault</span> propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006MSSP...20.1444G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006MSSP...20.1444G"><span>An investigation of the effects of measurement noise in the use of instantaneous angular speed for machine <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gu, Fengshou; Yesilyurt, Isa; Li, Yuhua; Harris, Georgina; Ball, Andrew</p> <p>2006-08-01</p> <p>In order to discriminate small changes for early <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machines, condition monitoring demands that the measurement of instantaneous angular speed (IAS) of the machines be as accurate as possible. This paper develops the theoretical basis and practical implementation of IAS data acquisition and IAS estimation when noise influence is included. IAS data is modelled as a frequency modulated signal of which the signal-to-noise ratio can be improved by using a high-resolution encoder. From this signal model and analysis, optimal configurations for IAS data collection are addressed for high accuracy IAS measurement. Simultaneously, a method based on analytic signal concept and fast Fourier transform is also developed for efficient and accurate estimation of IAS. Finally, a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is carried out on an electric induction motor driving system using IAS measurement. The <span class="hlt">diagnosis</span> results show that using a high-resolution encoder and a long data stream can achieve noise reduction by more than 10 dB in the frequency range of interest, validating the model and algorithm developed. Moreover, the results demonstrate that IAS measurement outperforms conventional <span class="hlt">vibration</span> in <span class="hlt">diagnosis</span> of incipient <span class="hlt">faults</span> of motor rotor bar defects and shaft misalignment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA376843','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA376843"><span>Non-Invasive Detection of CH-46 AFT Gearbox <span class="hlt">Faults</span> Using Digital Pattern Recognition and Classification Techniques</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1999-05-05</p> <p>processing and artificial neural network (ANN) technology. The detector will classify incipient <span class="hlt">faults</span> based on real-tine <span class="hlt">vibration</span> data taken from the...provided the <span class="hlt">vibration</span> data necessary to develop and test the feasibility of en artificial neural network for <span class="hlt">fault</span> classification. This research</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018FrME...13..264C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018FrME...13..264C"><span>Basic research on machinery <span class="hlt">fault</span> diagnostics: Past, present, and future trends</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Xuefeng; Wang, Shibin; Qiao, Baijie; Chen, Qiang</p> <p>2018-06-01</p> <p>Machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span> has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> to guarantee their designed functions and performance throughout their lifetime. Research on machinery <span class="hlt">Fault</span> diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in terms of four main aspects: <span class="hlt">Fault</span> mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery <span class="hlt">fault</span> diagnostics. On the basis of the review of basic theory of machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4327071','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4327071"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lv, Yong; Zhu, Qinglin; Yuan, Rui</p> <p>2015-01-01</p> <p>The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the <span class="hlt">fault</span> of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures. PMID:25585105</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920073616&hterms=translation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dtranslation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920073616&hterms=translation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dtranslation"><span>Automatic translation of digraph to <span class="hlt">fault</span>-tree models</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Iverson, David L.</p> <p>1992-01-01</p> <p>The author presents a technique for converting digraph models, including those models containing cycles, to a <span class="hlt">fault</span>-tree format. A computer program which automatically performs this translation using an object-oriented representation of the models has been developed. The <span class="hlt">fault</span>-trees resulting from translations can be used for <span class="hlt">fault</span>-tree analysis and <span class="hlt">diagnosis</span>. Programs to calculate <span class="hlt">fault</span>-tree and digraph cut sets and perform <span class="hlt">diagnosis</span> with <span class="hlt">fault</span>-tree models have also been developed. The digraph to <span class="hlt">fault</span>-tree translation system has been successfully tested on several digraphs of varying size and complexity. Details of some representative translation problems are presented. Most of the computation performed by the program is dedicated to finding minimal cut sets for digraph nodes in order to break cycles in the digraph. <span class="hlt">Fault</span>-trees produced by the translator have been successfully used with NASA's <span class="hlt">Fault</span>-Tree <span class="hlt">Diagnosis</span> System (FTDS) to produce automated diagnostic systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25258726','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25258726"><span><span class="hlt">Fault</span> detection and <span class="hlt">diagnosis</span> for gas turbines based on a kernelized information entropy model.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei</p> <p>2014-01-01</p> <p>Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their <span class="hlt">faults</span>. In this work, we introduce a remote system for online condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from <span class="hlt">fault</span> samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PhSen...4..354X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhSen...4..354X"><span>Distributed intrusion monitoring system with fiber link backup and on-line <span class="hlt">fault</span> <span class="hlt">diagnosis</span> functions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xu, Jiwei; Wu, Huijuan; Xiao, Shunkun</p> <p>2014-12-01</p> <p>A novel multi-channel distributed optical fiber intrusion monitoring system with smart fiber link backup and on-line <span class="hlt">fault</span> <span class="hlt">diagnosis</span> functions was proposed. A 1× N optical switch was intelligently controlled by a peripheral interface controller (PIC) to expand the fiber link from one channel to several ones to lower the cost of the long or ultra-long distance intrusion monitoring system and also to strengthen the intelligent monitoring link backup function. At the same time, a sliding window auto-correlation method was presented to identify and locate the broken or <span class="hlt">fault</span> point of the cable. The experimental results showed that the proposed multi-channel system performed well especially whenever any a broken cable was detected. It could locate the broken or <span class="hlt">fault</span> point by itself accurately and switch to its backup sensing link immediately to ensure the security system to operate stably without a minute idling. And it was successfully applied in a field test for security monitoring of the 220-km-length national borderline in China.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4167449','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4167449"><span><span class="hlt">Fault</span> Detection and <span class="hlt">Diagnosis</span> for Gas Turbines Based on a Kernelized Information Entropy Model</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei</p> <p>2014-01-01</p> <p>Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their <span class="hlt">faults</span>. In this work, we introduce a remote system for online condition monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from <span class="hlt">fault</span> samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...93..267L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...93..267L"><span>Multi-<span class="hlt">faults</span> decoupling on turbo-expander using differential-based ensemble empirical mode decomposition</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Hongguang; Li, Ming; Li, Cheng; Li, Fucai; Meng, Guang</p> <p>2017-09-01</p> <p>This paper dedicates on the multi-<span class="hlt">faults</span> decoupling of turbo-expander rotor system using Differential-based Ensemble Empirical Mode Decomposition (DEEMD). DEEMD is an improved version of DEMD to resolve the imperfection of mode mixing. The nonlinear behaviors of the turbo-expander considering temperature gradient with crack, rub-impact and pedestal looseness <span class="hlt">faults</span> are investigated respectively, so that the baseline for the multi-<span class="hlt">faults</span> decoupling can be established. DEEMD is then utilized on the <span class="hlt">vibration</span> signals of the rotor system with coupling <span class="hlt">faults</span> acquired by numerical simulation, and the results indicate that DEEMD can successfully decouple the coupling <span class="hlt">faults</span>, which is more efficient than EEMD. DEEMD is also applied on the <span class="hlt">vibration</span> signal of the misalignment coupling with rub-impact <span class="hlt">fault</span> obtained during the adjustment of the experimental system. The conclusion shows that DEEMD can decompose the practical multi-<span class="hlt">faults</span> signal and the industrial prospect of DEEMD is verified as well.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED192736.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED192736.pdf"><span>An Annotated Selective Bibliography on Human Performance in <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Tasks. Technical Report 435. Final Report.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Johnson, William B.; And Others</p> <p></p> <p>This annotated bibliography developed in connection with an ongoing investigation of the use of computer simulations for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> training cites 61 published works taken predominantly from the disciplines of engineering, psychology, and education. A review of the existing literature included computer searches of the past ten years of…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19990008859','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19990008859"><span>Model-Based <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> for Turboshaft Engines</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.</p> <p>1998-01-01</p> <p>Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between <span class="hlt">faults</span> in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify <span class="hlt">faults</span>. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of <span class="hlt">faults</span> which might not otherwise be detected during pre-flight checkout.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JPS...280..320P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JPS...280..320P"><span>Model-based development of a <span class="hlt">fault</span> signature matrix to improve solid oxide fuel cell systems on-site <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Polverino, Pierpaolo; Pianese, Cesare; Sorrentino, Marco; Marra, Dario</p> <p>2015-04-01</p> <p>The paper focuses on the design of a procedure for the development of an on-field diagnostic algorithm for solid oxide fuel cell (SOFC) systems. The <span class="hlt">diagnosis</span> design phase relies on an in-deep analysis of the mutual interactions among all system components by exploiting the physical knowledge of the SOFC system as a whole. This phase consists of the <span class="hlt">Fault</span> Tree Analysis (FTA), which identifies the correlations among possible <span class="hlt">faults</span> and their corresponding symptoms at system components level. The main outcome of the FTA is an inferential isolation tool (<span class="hlt">Fault</span> Signature Matrix - FSM), which univocally links the <span class="hlt">faults</span> to the symptoms detected during the system monitoring. In this work the FTA is considered as a starting point to develop an improved FSM. Making use of a model-based investigation, a <span class="hlt">fault</span>-to-symptoms dependency study is performed. To this purpose a dynamic model, previously developed by the authors, is exploited to simulate the system under faulty conditions. Five <span class="hlt">faults</span> are simulated, one for the stack and four occurring at BOP level. Moreover, the robustness of the FSM design is increased by exploiting symptom thresholds defined for the investigation of the quantitative effects of the simulated <span class="hlt">faults</span> on the affected variables.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19780048764&hterms=problem+solving+strategies&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dproblem%2Bsolving%2Bstrategies','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19780048764&hterms=problem+solving+strategies&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dproblem%2Bsolving%2Bstrategies"><span>Human problem solving performance in a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> task</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rouse, W. B.</p> <p>1978-01-01</p> <p>It is proposed that humans in automated systems will be asked to assume the role of troubleshooter or problem solver and that the problems which they will be asked to solve in such systems will not be amenable to rote solution. The design of visual displays for problem solving in such situations is considered, and the results of two experimental investigations of human problem solving performance in the <span class="hlt">diagnosis</span> of <span class="hlt">faults</span> in graphically displayed network problems are discussed. The effects of problem size, forced-pacing, computer aiding, and training are considered. Results indicate that human performance deviates from optimality as problem size increases. Forced-pacing appears to cause the human to adopt fairly brute force strategies, as compared to those adopted in self-paced situations. Computer aiding substantially lessens the number of mistaken diagnoses by performing the bookkeeping portions of the task.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28773035','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28773035"><span>Incipient <span class="hlt">Fault</span> Detection for Rolling Element Bearings under Varying Speed Conditions.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo</p> <p>2017-06-20</p> <p>Varying speed conditions bring a huge challenge to incipient <span class="hlt">fault</span> detection of rolling element bearings because both the change of speed and <span class="hlt">faults</span> could lead to the amplitude fluctuation of <span class="hlt">vibration</span> signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient <span class="hlt">fault</span> detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient <span class="hlt">fault</span>. The proposed method is demonstrated using <span class="hlt">vibration</span> signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient <span class="hlt">fault</span> detection of rolling element bearings under varying speed conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AIPC.1839b0077Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AIPC.1839b0077Z"><span>The engine fuel system <span class="hlt">fault</span> analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Yong; Song, Hanqiang; Yang, Changsheng; Zhao, Wei</p> <p>2017-05-01</p> <p>For improving the reliability of the engine fuel system, the typical <span class="hlt">fault</span> factor of the engine fuel system was analyzed from the point view of structure and functional. The <span class="hlt">fault</span> character was gotten by building the fuel system <span class="hlt">fault</span> tree. According the utilizing of <span class="hlt">fault</span> mode effect analysis method (FMEA), several factors of key component fuel regulator was obtained, which include the <span class="hlt">fault</span> mode, the <span class="hlt">fault</span> cause, and the <span class="hlt">fault</span> influences. All of this made foundation for next development of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA583061','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA583061"><span>Robust <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Electric Drives Using Machine Learning</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2004-09-08</p> <p>detection of <span class="hlt">fault</span> conditions of the inverter. A machine learning framework is developed to systematically select torque-speed domain operation points...were used to generate various <span class="hlt">fault</span> condition data for machine learning . The technique is viable for accurate, reliable and fast <span class="hlt">fault</span> detection in electric drives.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...91..295L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...91..295L"><span>A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Yongbo; Yang, Yuantao; Li, Guoyan; Xu, Minqiang; Huang, Wenhu</p> <p>2017-07-01</p> <p>Health condition identification of planetary gearboxes is crucial to reduce the downtime and maximize productivity. This paper aims to develop a novel <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method based on modified multi-scale symbolic dynamic entropy (MMSDE) and minimum redundancy maximum relevance (mRMR) to identify the different health conditions of planetary gearbox. MMSDE is proposed to quantify the regularity of time series, which can assess the dynamical characteristics over a range of scales. MMSDE has obvious advantages in the detection of dynamical changes and computation efficiency. Then, the mRMR approach is introduced to refine the <span class="hlt">fault</span> features. Lastly, the obtained new features are fed into the least square support vector machine (LSSVM) to complete the <span class="hlt">fault</span> pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different <span class="hlt">fault</span> types of planetary gearboxes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014MSSP...44...47G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014MSSP...44...47G"><span>Shaft instantaneous angular speed for blade <span class="hlt">vibration</span> in rotating machine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gubran, Ahmed A.; Sinha, Jyoti K.</p> <p>2014-02-01</p> <p>Reliable blade health monitoring (BHM) in rotating machines like steam turbines and gas turbines, is a topic of research since decades to reduce machine down time, maintenance costs and to maintain the overall safety. Transverse blade <span class="hlt">vibration</span> is often transmitted to the shaft as torsional <span class="hlt">vibration</span>. The shaft instantaneous angular speed (IAS) is nothing but the representing the shaft torsional <span class="hlt">vibration</span>. Hence the shaft IAS has been extracted from the measured encoder data during machine run-up to understand the blade <span class="hlt">vibration</span> and to explore the possibility of reliable assessment of blade health. A number of experiments on an experimental rig with a bladed disk were conducted with healthy but mistuned blades and with different <span class="hlt">faults</span> simulation in the blades. The measured shaft torsional <span class="hlt">vibration</span> shows a distinct difference between the healthy and the faulty blade conditions. Hence, the observations are useful for the BHM in future. The paper presents the experimental setup, simulation of blade <span class="hlt">faults</span>, experiments conducted, observations and results.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA199350','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA199350"><span><span class="hlt">Fault</span> Model Development for <span class="hlt">Fault</span> Tolerant VLSI Design</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1988-05-01</p> <p>0 % .%. . BEIDGING <span class="hlt">FAULTS</span> A bridging <span class="hlt">fault</span> in a digital circuit connects two or more conducting paths of the circuit. The resistance...Melvin Breuer and Arthur Friedman, "<span class="hlt">Diagnosis</span> and Reliable Design of Digital Systems", Computer Science Press, Inc., 1976. 4. [Chandramouli,1983] R...2138 AEDC LIBARY (TECH REPORTS FILE) MS-O0 ARNOLD AFS TN 37389-9998 USAG1 Attn: ASH-PCA-CRT Ft Huachuca AZ 85613-6000 DOT LIBRARY/iQA SECTION - ATTN</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28452925','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28452925"><span>Distributed <span class="hlt">Fault</span> Detection Based on Credibility and Cooperation for WSNs in Smart Grids.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong</p> <p>2017-04-28</p> <p>Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely <span class="hlt">fault</span> detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed <span class="hlt">fault</span> detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch <span class="hlt">fault</span> <span class="hlt">diagnosis</span> requests. Secondly, the sending time of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> request is discussed to avoid the transmission overhead brought about by unnecessary <span class="hlt">diagnosis</span> requests and improve the efficiency of <span class="hlt">fault</span> detection based on neighbor cooperation. The <span class="hlt">diagnosis</span> reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of <span class="hlt">fault</span> detection, the <span class="hlt">diagnosis</span> results of neighbors are divided into several classifications to judge the <span class="hlt">fault</span> status of the sensors which launch the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> requests. Simulation results show that this novel mechanism can achieve high <span class="hlt">fault</span> detection ratio with a small number of <span class="hlt">fault</span> diagnoses and low data congestion probability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5469336','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5469336"><span>Distributed <span class="hlt">Fault</span> Detection Based on Credibility and Cooperation for WSNs in Smart Grids</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong</p> <p>2017-01-01</p> <p>Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely <span class="hlt">fault</span> detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed <span class="hlt">fault</span> detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch <span class="hlt">fault</span> <span class="hlt">diagnosis</span> requests. Secondly, the sending time of <span class="hlt">fault</span> <span class="hlt">diagnosis</span> request is discussed to avoid the transmission overhead brought about by unnecessary <span class="hlt">diagnosis</span> requests and improve the efficiency of <span class="hlt">fault</span> detection based on neighbor cooperation. The <span class="hlt">diagnosis</span> reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of <span class="hlt">fault</span> detection, the <span class="hlt">diagnosis</span> results of neighbors are divided into several classifications to judge the <span class="hlt">fault</span> status of the sensors which launch the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> requests. Simulation results show that this novel mechanism can achieve high <span class="hlt">fault</span> detection ratio with a small number of <span class="hlt">fault</span> diagnoses and low data congestion probability. PMID:28452925</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...70..141M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...70..141M"><span><span class="hlt">Fault</span> detection in heavy duty wheels by advanced <span class="hlt">vibration</span> processing techniques and lumped parameter modeling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Malago`, M.; Mucchi, E.; Dalpiaz, G.</p> <p>2016-03-01</p> <p>Heavy duty wheels are used in applications such as automatic vehicles and are mainly composed of a polyurethane tread glued to a cast iron hub. In the manufacturing process, the adhesive application between tread and hub is a critical assembly phase, since it is completely made by an operator and a contamination of the bond area may happen. Furthermore, the presence of rust on the hub surface can contribute to worsen the adherence interface, reducing the operating life. In this scenario, a quality control procedure for <span class="hlt">fault</span> detection to be used at the end of the manufacturing process has been developed. This procedure is based on <span class="hlt">vibration</span> processing techniques and takes advantages of the results of a lumped parameter model. Indicators based on cyclostationarity can be considered as key parameters to be adopted in a monitoring test station at the end of the production line due to their not deterministic characteristics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3472847','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3472847"><span>Optimal Design of the Absolute Positioning Sensor for a High-Speed Maglev Train and Research on Its <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zhang, Dapeng; Long, Zhiqiang; Xue, Song; Zhang, Junge</p> <p>2012-01-01</p> <p>This paper studies an absolute positioning sensor for a high-speed maglev train and its <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the <span class="hlt">fault</span> characters, and the signal flow method is used to locate the faulty parts. The <span class="hlt">diagnosis</span> information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line <span class="hlt">diagnosis</span> for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project. PMID:23112619</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23112619','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23112619"><span>Optimal design of the absolute positioning sensor for a high-speed maglev train and research on its <span class="hlt">fault</span> <span class="hlt">diagnosis</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zhang, Dapeng; Long, Zhiqiang; Xue, Song; Zhang, Junge</p> <p>2012-01-01</p> <p>This paper studies an absolute positioning sensor for a high-speed maglev train and its <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the <span class="hlt">fault</span> characters, and the signal flow method is used to locate the faulty parts. The <span class="hlt">diagnosis</span> information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line <span class="hlt">diagnosis</span> for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150018870','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150018870"><span>Detection and Modeling of High-Dimensional Thresholds for <span class="hlt">Fault</span> Detection and <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>He, Yuning</p> <p>2015-01-01</p> <p>Many <span class="hlt">Fault</span> Detection and <span class="hlt">Diagnosis</span> (FDD) systems use discrete models for detection and reasoning. To obtain categorical values like oil pressure too high, analog sensor values need to be discretized using a suitablethreshold. Time series of analog and discrete sensor readings are processed and discretized as they come in. This task isusually performed by the wrapper code'' of the FDD system, together with signal preprocessing and filtering. In practice,selecting the right threshold is very difficult, because it heavily influences the quality of <span class="hlt">diagnosis</span>. If a threshold causesthe alarm trigger even in nominal situations, false alarms will be the consequence. On the other hand, if threshold settingdoes not trigger in case of an off-nominal condition, important alarms might be missed, potentially causing hazardoussituations. In this paper, we will in detail describe the underlying statistical modeling techniques and algorithm as well as the Bayesian method for selecting the most likely shape and its parameters. Our approach will be illustrated by several examples from the Aerospace domain.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1334649','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1334649"><span><span class="hlt">Fault</span> detection and <span class="hlt">diagnosis</span> for refrigerator from compressor sensor</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Keres, Stephen L.; Gomes, Alberto Regio; Litch, Andrew D.</p> <p></p> <p>A refrigerator, a sealed refrigerant system, and method are provided where the refrigerator includes at least a refrigerated compartment and a sealed refrigerant system including an evaporator, a compressor, a condenser, a controller, an evaporator fan, and a condenser fan. The method includes monitoring a frequency of the compressor, and identifying a <span class="hlt">fault</span> condition in the at least one component of the refrigerant sealed system in response to the compressor frequency. The method may further comprise calculating a compressor frequency rate based upon the rate of change of the compressor frequency, wherein a <span class="hlt">fault</span> in the condenser fan is identifiedmore » if the compressor frequency rate is positive and exceeds a condenser fan <span class="hlt">fault</span> threshold rate, and wherein a <span class="hlt">fault</span> in the evaporator fan is identified if the compressor frequency rate is negative and exceeds an evaporator fan <span class="hlt">fault</span> threshold rate.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003MSSP...17..317S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003MSSP...17..317S"><span>Artificial Neural Network Based <span class="hlt">Fault</span> Diagnostics of Rolling Element Bearings Using Time-Domain Features</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Samanta, B.; Al-Balushi, K. R.</p> <p>2003-03-01</p> <p>A procedure is presented for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain <span class="hlt">vibration</span> signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden and output layers. The features are obtained from direct processing of the signal segments using very simple preprocessing. The input layer consists of five nodes, one each for root mean square, variance, skewness, kurtosis and normalised sixth central moment of the time-domain <span class="hlt">vibration</span> signals. The inputs are normalised in the range of 0.0 and 1.0 except for the skewness which is normalised between -1.0 and 1.0. The output layer consists of two binary nodes indicating the status of the machine—normal or defective bearings. Two hidden layers with different number of neurons have been used. The ANN is trained using backpropagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The effects of some preprocessing techniques like high-pass, band-pass filtration, envelope detection (demodulation) and wavelet transform of the <span class="hlt">vibration</span> signals, prior to feature extraction, are also studied. The results show the effectiveness of the ANN in <span class="hlt">diagnosis</span> of the machine condition. The proposed procedure requires only a few features extracted from the measured <span class="hlt">vibration</span> data either directly or with simple preprocessing. The reduced number of inputs leads to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110003020','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110003020"><span>Onboard Nonlinear Engine Sensor and Component <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> and Isolation Scheme</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong</p> <p>2011-01-01</p> <p>A method detects and isolates in-flight sensor, actuator, and component <span class="hlt">faults</span> for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor <span class="hlt">fault</span> or component <span class="hlt">fault</span>, but not both, this method considers sensor <span class="hlt">fault</span>, actuator <span class="hlt">fault</span>, and component <span class="hlt">fault</span> under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive <span class="hlt">fault</span> diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear <span class="hlt">faults</span> without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-<span class="hlt">fault</span> modes, including a subset of sensor/actuator/components <span class="hlt">faults</span>, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated <span class="hlt">faults</span> as long as the <span class="hlt">fault</span> magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110008515','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110008515"><span>Combining Model-Based and Feature-Driven <span class="hlt">Diagnosis</span> Approaches - A Case Study on Electromechanical Actuators</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Narasimhan, Sriram; Roychoudhury, Indranil; Balaban, Edward; Saxena, Abhinav</p> <p>2010-01-01</p> <p>Model-based <span class="hlt">diagnosis</span> typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for <span class="hlt">vibration</span> data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven <span class="hlt">diagnosis</span> approaches. The analytic approach is used to reduce the set of possible <span class="hlt">faults</span> and then features are chosen to best distinguish among the remaining <span class="hlt">faults</span>. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020073472','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020073472"><span>Classification of Aircraft Maneuvers for <span class="hlt">Fault</span> Detection</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Clancy, Daniel (Technical Monitor)</p> <p>2002-01-01</p> <p>Automated <span class="hlt">fault</span> detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of <span class="hlt">fault</span> detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data is a reasonable match to known examples of proper operation. In our domain of <span class="hlt">fault</span> detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online <span class="hlt">fault</span> detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of <span class="hlt">vibration</span> data and other available data. We explain where this subsystem fits into our envisioned <span class="hlt">fault</span> detection system as well its experiments showing the promise of this classification subsystem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JSV...397..241A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JSV...397..241A"><span>A new time-frequency method for identification and classification of ball bearing <span class="hlt">faults</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Attoui, Issam; Fergani, Nadir; Boutasseta, Nadir; Oudjani, Brahim; Deliou, Adel</p> <p>2017-06-01</p> <p>In order to <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of ball bearing that is one of the most critical components of rotating machinery, this paper presents a time-frequency procedure incorporating a new feature extraction step that combines the classical wavelet packet decomposition energy distribution technique and a new feature extraction technique based on the selection of the most impulsive frequency bands. In the proposed procedure, firstly, as a pre-processing step, the most impulsive frequency bands are selected at different bearing conditions using a combination between Fast-Fourier-Transform FFT and Short-Frequency Energy SFE algorithms. Secondly, once the most impulsive frequency bands are selected, the measured machinery <span class="hlt">vibration</span> signals are decomposed into different frequency sub-bands by using discrete Wavelet Packet Decomposition WPD technique to maximize the detection of their frequency contents and subsequently the most useful sub-bands are represented in the time-frequency domain by using Short Time Fourier transform STFT algorithm for knowing exactly what the frequency components presented in those frequency sub-bands are. Once the proposed feature vector is obtained, three feature dimensionality reduction techniques are employed using Linear Discriminant Analysis LDA, a feedback wrapper method and Locality Sensitive Discriminant Analysis LSDA. Lastly, the Adaptive Neuro-Fuzzy Inference System ANFIS algorithm is used for instantaneous identification and classification of bearing <span class="hlt">faults</span>. In order to evaluate the performances of the proposed method, different testing data set to the trained ANFIS model by using different conditions of healthy and faulty bearings under various load levels, <span class="hlt">fault</span> severities and rotating speed. The conclusion resulting from this paper is highlighted by experimental results which prove that the proposed method can serve as an intelligent bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5554056','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5554056"><span>Incipient <span class="hlt">Fault</span> Detection for Rolling Element Bearings under Varying Speed Conditions</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo</p> <p>2017-01-01</p> <p>Varying speed conditions bring a huge challenge to incipient <span class="hlt">fault</span> detection of rolling element bearings because both the change of speed and <span class="hlt">faults</span> could lead to the amplitude fluctuation of <span class="hlt">vibration</span> signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient <span class="hlt">fault</span> detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient <span class="hlt">fault</span>. The proposed method is demonstrated using <span class="hlt">vibration</span> signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient <span class="hlt">fault</span> detection of rolling element bearings under varying speed conditions. PMID:28773035</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1206356-dynamic-modeling-injection-induced-fault-reactivation-ground-motion-impact-surface-structures-human-perception','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1206356-dynamic-modeling-injection-induced-fault-reactivation-ground-motion-impact-surface-structures-human-perception"><span>Dynamic modeling of injection-induced <span class="hlt">fault</span> reactivation and ground motion and impact on surface structures and human perception</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Rutqvist, Jonny; Cappa, Frederic; Rinaldi, Antonio P.; ...</p> <p>2014-12-31</p> <p>We summarize recent modeling studies of injection-induced <span class="hlt">fault</span> reactivation, seismicity, and its potential impact on surface structures and nuisance to the local human population. We used coupled multiphase fluid flow and geomechanical numerical modeling, dynamic wave propagation modeling, seismology theories, and empirical <span class="hlt">vibration</span> criteria from mining and construction industries. We first simulated injection-induced <span class="hlt">fault</span> reactivation, including dynamic <span class="hlt">fault</span> slip, seismic source, wave propagation, and ground <span class="hlt">vibrations</span>. From co-seismic average shear displacement and rupture area, we determined the moment magnitude to about M w = 3 for an injection-induced <span class="hlt">fault</span> reactivation at a depth of about 1000 m. We then analyzedmore » the ground <span class="hlt">vibration</span> results in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and frequency content, with comparison to the U.S. Bureau of Mines’ <span class="hlt">vibration</span> criteria for cosmetic damage to buildings, as well as human-perception <span class="hlt">vibration</span> limits. For the considered synthetic M w = 3 event, our analysis showed that the short duration, high frequency ground motion may not cause any significant damage to surface structures, and would not cause, in this particular case, upward CO 2 leakage, but would certainly be felt by the local population.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JPS...364..163W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPS...364..163W"><span>Optimal <span class="hlt">fault</span>-tolerant control strategy of a solid oxide fuel cell system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Xiaojuan; Gao, Danhui</p> <p>2017-10-01</p> <p>For solid oxide fuel cell (SOFC) development, load tracking, heat management, air excess ratio constraint, high efficiency, low cost and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> are six key issues. However, no literature studies the control techniques combining optimization and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for the SOFC system. An optimal <span class="hlt">fault</span>-tolerant control strategy is presented in this paper, which involves four parts: a <span class="hlt">fault</span> <span class="hlt">diagnosis</span> module, a switching module, two backup optimizers and a controller loop. The <span class="hlt">fault</span> <span class="hlt">diagnosis</span> part is presented to identify the SOFC current <span class="hlt">fault</span> type, and the switching module is used to select the appropriate backup optimizer based on the <span class="hlt">diagnosis</span> result. NSGA-II and TOPSIS are employed to design the two backup optimizers under normal and air compressor <span class="hlt">fault</span> states. PID algorithm is proposed to design the control loop, which includes a power tracking controller, an anode inlet temperature controller, a cathode inlet temperature controller and an air excess ratio controller. The simulation results show the proposed optimal <span class="hlt">fault</span>-tolerant control method can track the power, temperature and air excess ratio at the desired values, simultaneously achieving the maximum efficiency and the minimum unit cost in the case of SOFC normal and even in the air compressor <span class="hlt">fault</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/22391663-knowledge-based-fault-diagnosis-system-refuse-collection-vehicle','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/22391663-knowledge-based-fault-diagnosis-system-refuse-collection-vehicle"><span>Knowledge-based <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system for refuse collection vehicle</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Tan, CheeFai; Juffrizal, K.; Khalil, S. N.</p> <p></p> <p>The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledgemore » that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system framework for the refuse collection vehicle.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030064882','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030064882"><span>Maneuver Classification for Aircraft <span class="hlt">Fault</span> Detection</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.</p> <p>2003-01-01</p> <p>Automated <span class="hlt">fault</span> detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of <span class="hlt">fault</span> detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of <span class="hlt">fault</span> detection in aircraft, identifying all possible faulty and proper operating modes is clearly impossible. We envision a system for online <span class="hlt">fault</span> detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of <span class="hlt">vibration</span> data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned <span class="hlt">fault</span> detection system as well as experiments showing the promise of this classification subsystem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26819590','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26819590"><span>Real-Time Monitoring and <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of a Low Power Hub Motor Using Feedforward Neural Network.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Şimşir, Mehmet; Bayır, Raif; Uyaroğlu, Yılmaz</p> <p>2016-01-01</p> <p>Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined <span class="hlt">faults</span> under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the <span class="hlt">faults</span> are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the <span class="hlt">faults</span> of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system prototype for hub motor was designed and manufactured.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4706884','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4706884"><span>Real-Time Monitoring and <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> of a Low Power Hub Motor Using Feedforward Neural Network</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Şimşir, Mehmet; Bayır, Raif; Uyaroğlu, Yılmaz</p> <p>2016-01-01</p> <p>Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined <span class="hlt">faults</span> under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the <span class="hlt">faults</span> are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the <span class="hlt">faults</span> of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system prototype for hub motor was designed and manufactured. PMID:26819590</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19900003338','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19900003338"><span>The <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> knowledge-based system for space power systems: AMPERES, phase 1</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lee, S. C.</p> <p>1989-01-01</p> <p>The objective is to develop a real time <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span> knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective <span class="hlt">fault</span> monitoring and <span class="hlt">diagnosis</span>, and its supporting knowledge representation scheme.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...72..206M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...72..206M"><span>Rolling element bearing defect <span class="hlt">diagnosis</span> under variable speed operation through angle synchronous averaging of wavelet de-noised estimate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mishra, C.; Samantaray, A. K.; Chakraborty, G.</p> <p>2016-05-01</p> <p>Rolling element bearings are widely used in rotating machines and their <span class="hlt">faults</span> can lead to excessive <span class="hlt">vibration</span> levels and/or complete seizure of the machine. Under special operating conditions such as non-uniform or low speed shaft rotation, the available <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods cannot be applied for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> with full confidence. <span class="hlt">Fault</span> symptoms in such operating conditions cannot be easily extracted through usual measurement and signal processing techniques. A typical example is a bearing in heavy rolling mill with variable load and disturbance from other sources. In extremely slow speed operation, variation in speed due to speed controller transients or external disturbances (e.g., varying load) can be relatively high. To account for speed variation, instantaneous angular position instead of time is used as the base variable of signals for signal processing purposes. Even with time synchronous averaging (TSA) and well-established methods like envelope order analysis, rolling element <span class="hlt">faults</span> in rolling element bearings cannot be easily identified during such operating conditions. In this article we propose to use order tracking on the envelope of the wavelet de-noised estimate of the short-duration angle synchronous averaged signal to diagnose <span class="hlt">faults</span> in rolling element bearing operating under the stated special conditions. The proposed four-stage sequential signal processing method eliminates uncorrelated content, avoids signal smearing and exposes only the <span class="hlt">fault</span> frequencies and its harmonics in the spectrum. We use experimental data1</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20090021633','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20090021633"><span>A Mode-Shape-Based <span class="hlt">Fault</span> Detection Methodology for Cantilever Beams</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tejada, Arturo</p> <p>2009-01-01</p> <p>An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for <span class="hlt">fault</span> detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known <span class="hlt">vibrational</span> techniques to detect <span class="hlt">faults</span> due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the <span class="hlt">vibrational</span> techniques and a clear understanding of the particular <span class="hlt">fault</span> detection methodology. This report presents a succinct review of the physical principles behind the modeling of <span class="hlt">vibrating</span> structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of <span class="hlt">fault</span> detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3472873','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3472873"><span><span class="hlt">Fault</span> Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lu, Feng; Huang, Jinquan; Xing, Yaodong</p> <p>2012-01-01</p> <p>Combining a simplified on-board turbo-shaft model with sensor <span class="hlt">fault</span> diagnostic logic, a model-based sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is proposed. The existing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor <span class="hlt">fault</span> detection, <span class="hlt">diagnosis</span> (FDD) logic is designed, and two types of sensor failures, such as the step <span class="hlt">faults</span> and the drift <span class="hlt">faults</span>, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> logic determines the cause of the difference. Through this approach, the sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system achieves the objectives of anomaly detection, sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of <span class="hlt">faults</span> under different channel combinations are presented. The experimental results show that the proposed method for sensor <span class="hlt">fault</span> diagnostics is efficient. PMID:23112645</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23112645','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23112645"><span><span class="hlt">Fault</span> diagnostics for turbo-shaft engine sensors based on a simplified on-board model.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lu, Feng; Huang, Jinquan; Xing, Yaodong</p> <p>2012-01-01</p> <p>Combining a simplified on-board turbo-shaft model with sensor <span class="hlt">fault</span> diagnostic logic, a model-based sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method is proposed. The existing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor <span class="hlt">fault</span> detection, <span class="hlt">diagnosis</span> (FDD) logic is designed, and two types of sensor failures, such as the step <span class="hlt">faults</span> and the drift <span class="hlt">faults</span>, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> logic determines the cause of the difference. Through this approach, the sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> system achieves the objectives of anomaly detection, sensor <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of <span class="hlt">faults</span> under different channel combinations are presented. The experimental results show that the proposed method for sensor <span class="hlt">fault</span> diagnostics is efficient.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25132845','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25132845"><span>Neural networks and <span class="hlt">fault</span> probability evaluation for <span class="hlt">diagnosis</span> issues.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kourd, Yahia; Lefebvre, Dimitri; Guersi, Noureddine</p> <p>2014-01-01</p> <p>This paper presents a new FDI technique for <span class="hlt">fault</span> detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the <span class="hlt">fault</span>-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely <span class="hlt">fault</span> among a set of candidate <span class="hlt">faults</span>. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 <span class="hlt">fault</span> candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JSV...418...55Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JSV...418...55Z"><span>An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Xin; Liu, Zhiwen; Miao, Qiang; Wang, Lei</p> <p>2018-03-01</p> <p>A time varying filtering based empirical mode decomposition (EMD) (TVF-EMD) method was proposed recently to solve the mode mixing problem of EMD method. Compared with the classical EMD, TVF-EMD was proven to improve the frequency separation performance and be robust to noise interference. However, the decomposition parameters (i.e., bandwidth threshold and B-spline order) significantly affect the decomposition results of this method. In original TVF-EMD method, the parameter values are assigned in advance, which makes it difficult to achieve satisfactory analysis results. To solve this problem, this paper develops an optimized TVF-EMD method based on grey wolf optimizer (GWO) algorithm for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotating machinery. Firstly, a measurement index termed weighted kurtosis index is constructed by using kurtosis index and correlation coefficient. Subsequently, the optimal TVF-EMD parameters that match with the input signal can be obtained by GWO algorithm using the maximum weighted kurtosis index as objective function. Finally, <span class="hlt">fault</span> features can be extracted by analyzing the sensitive intrinsic mode function (IMF) owning the maximum weighted kurtosis index. Simulations and comparisons highlight the performance of TVF-EMD method for signal decomposition, and meanwhile verify the fact that bandwidth threshold and B-spline order are critical to the decomposition results. Two case studies on rotating machinery <span class="hlt">fault</span> <span class="hlt">diagnosis</span> demonstrate the effectiveness and advantages of the proposed method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MSSP...80..445L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MSSP...80..445L"><span>A windowing and mapping strategy for gear tooth <span class="hlt">fault</span> detection of a planetary gearbox</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liang, Xihui; Zuo, Ming J.; Liu, Libin</p> <p>2016-12-01</p> <p>When there is a single cracked tooth in a planet gear, the cracked tooth is enmeshed for very short time duration in comparison to the total time of a full revolution of the planet gear. The <span class="hlt">fault</span> symptom generated by the single cracked tooth may be very weak. This study aims to develop a windowing and mapping strategy to interpret the <span class="hlt">vibration</span> signal of a planetary gear at the tooth level. The <span class="hlt">fault</span> symptoms generated by a single cracked tooth of the planet gear of interest can be extracted. The health condition of the planet gear can be assessed by comparing the differences among the signals of all teeth of the planet gear. The proposed windowing and mapping strategy is tested with both simulated <span class="hlt">vibration</span> signals and experimental <span class="hlt">vibration</span> signals. The tooth signals can be successfully decomposed and a single tooth <span class="hlt">fault</span> on a planet gear can be effectively detected.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1994agma.workR..24C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1994agma.workR..24C"><span>Analytical and experimental <span class="hlt">vibration</span> analysis of a faulty gear system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Choy, F. K.; Braun, M. J.; Polyshchuk, V.; Zakrajsek, J. J.; Townsend, D. P.; Handschuh, R. F.</p> <p>1994-10-01</p> <p>A comprehensive analytical procedure was developed for predicting <span class="hlt">faults</span> in gear transmission systems under normal operating conditions. A gear tooth <span class="hlt">fault</span> model is developed to simulate the effects of pitting and wear on the <span class="hlt">vibration</span> signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth <span class="hlt">faults</span>. The overall dynamics of the gear transmission system is evaluated by coupling the dynamics of each individual gear-rotor system through gear mesh forces generated between each gear-rotor system and the bearing forces generated between the rotor and the gearbox structures. The predicted results were compared with experimental results obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. The Wigner-Ville Distribution (WVD) was used to give a comprehensive comparison of the predicted and experimental results. The WVD method applied to the experimental results were also compared to other <span class="hlt">fault</span> detection techniques to verify the WVD's ability to detect the pitting damage, and to determine its relative performance. Overall results show good correlation between the experimental <span class="hlt">vibration</span> data of the damaged test gear and the predicted <span class="hlt">vibration</span> from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1994agma.workQ..24C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1994agma.workQ..24C"><span>Analytical and experimental <span class="hlt">vibration</span> analysis of a faulty gear system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Choy, F. K.; Braun, M. J.; Polyshchuk, V.; Zakrajsek, J. J.; Townsend, D. P.; Handschuh, R. F.</p> <p>1994-10-01</p> <p>A comprehensive analytical procedure was developed for predicting <span class="hlt">faults</span> in gear transmission systems under normal operating conditions. A gear tooth <span class="hlt">fault</span> model is developed to simulate the effects of pitting and wear on the <span class="hlt">vibration</span> signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth <span class="hlt">faults</span>. The overall dynamics of the gear transmission system is evaluated by coupling the dynamics of each individual gear-rotor system through gear mesh forces generated between each gear-rotor system and the bearing forces generated between the rotor and the gearbox structure. The predicted results were compared with experimental results obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. The Wigner-Ville distribution (WVD) was used to give a comprehensive comparison of the predicted and experimental results. The WVD method applied to the experimental results were also compared to other <span class="hlt">fault</span> detection techniques to verify the WVD's ability to detect the pitting damage, and to determine its relative performance. Overall results show good correlation between the experimental <span class="hlt">vibration</span> data of the damaged test gear and the predicted <span class="hlt">vibration</span> from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950005964','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950005964"><span>Analytical and Experimental <span class="hlt">Vibration</span> Analysis of a Faulty Gear System</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Choy, F. K.; Braun, M. J.; Polyshchuk, V.; Zakrajsek, J. J.; Townsend, D. P.; Handschuh, R. F.</p> <p>1994-01-01</p> <p>A comprehensive analytical procedure was developed for predicting <span class="hlt">faults</span> in gear transmission systems under normal operating conditions. A gear tooth <span class="hlt">fault</span> model is developed to simulate the effects of pitting and wear on the <span class="hlt">vibration</span> signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth <span class="hlt">faults</span>. The overall dynamics of the gear transmission system is evaluated by coupling the dynamics of each individual gear-rotor system through gear mesh forces generated between each gear-rotor system and the bearing forces generated between the rotor and the gearbox structure. The predicted results were compared with experimental results obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. The Wigner-Ville distribution (WVD) was used to give a comprehensive comparison of the predicted and experimental results. The WVD method applied to the experimental results were also compared to other <span class="hlt">fault</span> detection techniques to verify the WVD's ability to detect the pitting damage, and to determine its relative performance. Overall results show good correlation between the experimental <span class="hlt">vibration</span> data of the damaged test gear and the predicted <span class="hlt">vibration</span> from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4208195','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4208195"><span>Sensor-Based <span class="hlt">Vibration</span> Signal Feature Extraction Using an Improved Composite Dictionary Matching Pursuit Algorithm</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui</p> <p>2014-01-01</p> <p>This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to <span class="hlt">vibration</span> sensor signal feature extraction and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear <span class="hlt">fault</span> signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation <span class="hlt">vibration</span> signals with noise. The simulation sensor-based <span class="hlt">vibration</span> signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based <span class="hlt">vibration</span> signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25207870','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25207870"><span>Sensor-based <span class="hlt">vibration</span> signal feature extraction using an improved composite dictionary matching pursuit algorithm.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui</p> <p>2014-09-09</p> <p>This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to <span class="hlt">vibration</span> sensor signal feature extraction and <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear <span class="hlt">fault</span> signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation <span class="hlt">vibration</span> signals with noise. The simulation sensor-based <span class="hlt">vibration</span> signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based <span class="hlt">vibration</span> signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880020406','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880020406"><span>Intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and failure management of flight control actuation systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bonnice, William F.; Baker, Walter</p> <p>1988-01-01</p> <p>The real-time <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and failure management (FDFM) of current operational and experimental dual tandem aircraft flight control system actuators was investigated. Dual tandem actuators were studied because of the active FDFM capability required to manage the redundancy of these actuators. The FDFM methods used on current dual tandem actuators were determined by examining six specific actuators. The FDFM capability on these six actuators was also evaluated. One approach for improving the FDFM capability on dual tandem actuators may be through the application of artificial intelligence (AI) technology. Existing AI approaches and applications of FDFM were examined and evaluated. Based on the general survey of AI FDFM approaches, the potential role of AI technology for real-time actuator FDFM was determined. Finally, FDFM and maintainability improvements for dual tandem actuators were recommended.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030014137','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030014137"><span>Classification of Aircraft Maneuvers for <span class="hlt">Fault</span> Detection</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oza, Nikunj; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Koga, Dennis (Technical Monitor)</p> <p>2002-01-01</p> <p>Automated <span class="hlt">fault</span> detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of <span class="hlt">fault</span> detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of <span class="hlt">fault</span> detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online <span class="hlt">fault</span> detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of <span class="hlt">vibration</span> data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned <span class="hlt">fault</span> detection system as well as experiments showing the promise of this classification subsystem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006SPIE.6357E..4UX','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006SPIE.6357E..4UX"><span>Intelligent classifier for dynamic <span class="hlt">fault</span> patterns based on hidden Markov model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xu, Bo; Feng, Yuguang; Yu, Jinsong</p> <p>2006-11-01</p> <p>It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> method, an integrated <span class="hlt">fault</span>-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the <span class="hlt">fault</span> feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is realized by <span class="hlt">fault</span> patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the <span class="hlt">diagnosis</span> model is easy to extend, and the <span class="hlt">fault</span> pattern classifier is efficient and is convenient to the detecting and diagnosing of new <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29757251','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29757251"><span>Integral Sensor <span class="hlt">Fault</span> Detection and Isolation for Railway Traction Drive.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Garramiola, Fernando; Del Olmo, Jon; Poza, Javier; Madina, Patxi; Almandoz, Gaizka</p> <p>2018-05-13</p> <p>Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of <span class="hlt">faults</span> has become an important key for Railway traction drive manufacturers. Sensor <span class="hlt">faults</span> are important sources of failures. Among the different <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approaches, in this article an integral <span class="hlt">diagnosis</span> strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the <span class="hlt">fault</span> detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the <span class="hlt">diagnosis</span> is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral <span class="hlt">fault</span> <span class="hlt">diagnosis</span> solution is provided, to detect and isolate <span class="hlt">faults</span> in all the sensors installed in the traction drive.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5982243','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5982243"><span>Integral Sensor <span class="hlt">Fault</span> Detection and Isolation for Railway Traction Drive</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>del Olmo, Jon; Poza, Javier; Madina, Patxi; Almandoz, Gaizka</p> <p>2018-01-01</p> <p>Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of <span class="hlt">faults</span> has become an important key for Railway traction drive manufacturers. Sensor <span class="hlt">faults</span> are important sources of failures. Among the different <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approaches, in this article an integral <span class="hlt">diagnosis</span> strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the <span class="hlt">fault</span> detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the <span class="hlt">diagnosis</span> is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral <span class="hlt">fault</span> <span class="hlt">diagnosis</span> solution is provided, to detect and isolate <span class="hlt">faults</span> in all the sensors installed in the traction drive. PMID:29757251</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1002748','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1002748"><span><span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> and Prognosis Based on Lebesgue Sampling</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-10-02</p> <p>required for many safety critical systems such as unmanned air/ground/sea vehicles, aircraft , power generation, nuclear power plants, and various industrial...prediction horizon in the <span class="hlt">fault</span> dimen- sion axis and described by the number fo <span class="hlt">fault</span> states. This provides a straightforward means to conduct prognosis that...shown in Figure 2.(b), only 5 Lebesgue states are visited during the 550 cycles in R1 and 4 states during the 100 cycles in R2, which means that the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29621131','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29621131"><span>A Weighted Deep Representation Learning Model for Imbalanced <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Cyber-Physical Systems.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wu, Zhenyu; Guo, Yang; Lin, Wenfang; Yu, Shuyang; Ji, Yang</p> <p>2018-04-05</p> <p>Predictive maintenance plays an important role in modern Cyber-Physical Systems (CPSs) and data-driven methods have been a worthwhile direction for Prognostics Health Management (PHM). However, two main challenges have significant influences on the traditional <span class="hlt">fault</span> diagnostic models: one is that extracting hand-crafted features from multi-dimensional sensors with internal dependencies depends too much on expertise knowledge; the other is that imbalance pervasively exists among faulty and normal samples. As deep learning models have proved to be good methods for automatic feature extraction, the objective of this paper is to study an optimized deep learning model for imbalanced <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for CPSs. Thus, this paper proposes a weighted Long Recurrent Convolutional LSTM model with sampling policy (wLRCL-D) to deal with these challenges. The model consists of 2-layer CNNs, 2-layer inner LSTMs and 2-Layer outer LSTMs, with under-sampling policy and weighted cost-sensitive loss function. Experiments are conducted on PHM 2015 challenge datasets, and the results show that wLRCL-D outperforms other baseline methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948747','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948747"><span>A Weighted Deep Representation Learning Model for Imbalanced <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Cyber-Physical Systems</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Guo, Yang; Lin, Wenfang; Yu, Shuyang; Ji, Yang</p> <p>2018-01-01</p> <p>Predictive maintenance plays an important role in modern Cyber-Physical Systems (CPSs) and data-driven methods have been a worthwhile direction for Prognostics Health Management (PHM). However, two main challenges have significant influences on the traditional <span class="hlt">fault</span> diagnostic models: one is that extracting hand-crafted features from multi-dimensional sensors with internal dependencies depends too much on expertise knowledge; the other is that imbalance pervasively exists among faulty and normal samples. As deep learning models have proved to be good methods for automatic feature extraction, the objective of this paper is to study an optimized deep learning model for imbalanced <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for CPSs. Thus, this paper proposes a weighted Long Recurrent Convolutional LSTM model with sampling policy (wLRCL-D) to deal with these challenges. The model consists of 2-layer CNNs, 2-layer inner LSTMs and 2-Layer outer LSTMs, with under-sampling policy and weighted cost-sensitive loss function. Experiments are conducted on PHM 2015 challenge datasets, and the results show that wLRCL-D outperforms other baseline methods. PMID:29621131</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140004901','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140004901"><span>Improving Multiple <span class="hlt">Fault</span> Diagnosability using Possible Conflicts</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Daigle, Matthew J.; Bregon, Anibal; Biswas, Gautam; Koutsoukos, Xenofon; Pulido, Belarmino</p> <p>2012-01-01</p> <p>Multiple <span class="hlt">fault</span> <span class="hlt">diagnosis</span> is a difficult problem for dynamic systems. Due to <span class="hlt">fault</span> masking, compensation, and relative time of <span class="hlt">fault</span> occurrence, multiple <span class="hlt">faults</span> can manifest in many different ways as observable <span class="hlt">fault</span> signature sequences. This decreases diagnosability of multiple <span class="hlt">faults</span>, and therefore leads to a loss in effectiveness of the <span class="hlt">fault</span> isolation step. We develop a qualitative, event-based, multiple <span class="hlt">fault</span> isolation framework, and derive several notions of multiple <span class="hlt">fault</span> diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples <span class="hlt">faults</span> from residuals, we can significantly improve the diagnosability of multiple <span class="hlt">faults</span> compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19900017964','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19900017964"><span>Dynamic test input generation for multiple-<span class="hlt">fault</span> isolation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schaefer, Phil</p> <p>1990-01-01</p> <p>Recent work is Causal Reasoning has provided practical techniques for multiple <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. These techniques provide a hypothesis/measurement <span class="hlt">diagnosis</span> cycle. Using probabilistic methods, they choose the best measurements to make, then update <span class="hlt">fault</span> hypotheses in response. For many applications such as computers and spacecraft, few measurement points may be accessible, or values may change quickly as the system under <span class="hlt">diagnosis</span> operates. In these cases, a hypothesis/measurement cycle is insufficient. A technique is presented for a hypothesis/test-input/measurement <span class="hlt">diagnosis</span> cycle. In contrast to generating tests a priori for determining device functionality, it dynamically generates tests in response to current knowledge about <span class="hlt">fault</span> probabilities. It is shown how the mathematics previously used for measurement specification can be applied to the test input generation process. An example from an efficient implementation called Multi-Purpose Causal (MPC) is presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880001756','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880001756"><span>Incipient <span class="hlt">fault</span> detection study for advanced spacecraft systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Milner, G. Martin; Black, Michael C.; Hovenga, J. Mike; Mcclure, Paul F.</p> <p>1986-01-01</p> <p>A feasibility study to investigate the application of <span class="hlt">vibration</span> monitoring to the rotating machinery of planned NASA advanced spacecraft components is described. Factors investigated include: (1) special problems associated with small, high RPM machines; (2) application across multiple component types; (3) microgravity; (4) multiple <span class="hlt">fault</span> types; (5) eight different analysis techniques including signature analysis, high frequency demodulation, cepstrum, clustering, amplitude analysis, and pattern recognition are compared; and (6) small sample statistical analysis is used to compare performance by computation of probability of detection and false alarm for an ensemble of repeated baseline and <span class="hlt">faulted</span> tests. Both detection and classification performance are quantified. <span class="hlt">Vibration</span> monitoring is shown to be an effective means of detecting the most important problem types for small, high RPM fans and pumps typical of those planned for the advanced spacecraft. A preliminary monitoring system design and implementation plan is presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008EOSTr..89..349V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008EOSTr..89..349V"><span>Drill Bit Noise Illuminates the San Andreas <span class="hlt">Fault</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vasconcelos, Ivan; Snieder, Roel; Sava, Paul; Taylor, Tom; Malin, Peter; Chavarria, Andres</p> <p>2008-09-01</p> <p>Extracting the <span class="hlt">vibration</span> response of the subsurface from noise is a rapidly growing field of research [Curtis et al., 2006; Larose et al., 2006]. We carried out broadside imaging of the San Andreas <span class="hlt">fault</span> zone (SAFZ) using drill bit noise created in the main hole of the San Andreas <span class="hlt">Fault</span> Observatory at Depth (SAFOD), near Parkfield, Calif. Imaging with drill bit noise is not new, but it traditionally requires the measurement of the <span class="hlt">vibrations</span> of the drill stem [Rector and Marion, 1991]; such measurements provide the waves radiated by the drill bit. At SAFOD, these measurements were not available due to the absence of an accelerometer mounted on the drill stem. For this reason, the new technique of deconvolution interferometry was used [Vasconcelos and Snieder, 2008]. This technique extracts the waves propagating between seismometers from recordings of incoherent noise.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_23 --> <div id="page_24" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="461"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950007756','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950007756"><span><span class="hlt">Fault</span> detection and <span class="hlt">diagnosis</span> using neural network approaches</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kramer, Mark A.</p> <p>1992-01-01</p> <p>Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process <span class="hlt">faults</span>, radial basis function networks can effectively identify failures. This approach is often limited by the lack of <span class="hlt">fault</span> data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify <span class="hlt">faults</span>. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..100..439Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..100..439Z"><span>A deep convolutional neural network with new training methods for bearing <span class="hlt">fault</span> <span class="hlt">diagnosis</span> under noisy environment and different working load</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Wei; Li, Chuanhao; Peng, Gaoliang; Chen, Yuanhang; Zhang, Zhujun</p> <p>2018-02-01</p> <p>In recent years, intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithms using machine learning technique have achieved much success. However, due to the fact that in real world industrial applications, the working load is changing all the time and noise from the working environment is inevitable, degradation of the performance of intelligent <span class="hlt">fault</span> <span class="hlt">diagnosis</span> methods is very serious. In this paper, a new model based on deep learning is proposed to address the problem. Our contributions of include: First, we proposed an end-to-end method that takes raw temporal signals as inputs and thus doesn't need any time consuming denoising preprocessing. The model can achieve pretty high accuracy under noisy environment. Second, the model does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working load is changed. To understand the proposed model, we will visualize the learned features, and try to analyze the reasons behind the high performance of the model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JSV...349..163C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JSV...349..163C"><span><span class="hlt">Vibration</span> modelling and verifications for whole aero-engine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, G.</p> <p>2015-08-01</p> <p>In this study, a new rotor-ball-bearing-casing coupling dynamic model for a practical aero-engine is established. In the coupling system, the rotor and casing systems are modelled using the finite element method, support systems are modelled as lumped parameter models, nonlinear factors of ball bearings and <span class="hlt">faults</span> are included, and four types of supports and connection models are defined to model the complex rotor-support-casing coupling system of the aero-engine. A new numerical integral method that combines the Newmark-β method and the improved Newmark-β method (Zhai method) is used to obtain the system responses. Finally, the new model is verified in three ways: (1) modal experiment based on rotor-ball bearing rig, (2) modal experiment based on rotor-ball-bearing-casing rig, and (3) <span class="hlt">fault</span> simulations for a certain type of missile turbofan aero-engine <span class="hlt">vibration</span>. The results show that the proposed model can not only simulate the natural <span class="hlt">vibration</span> characteristics of the whole aero-engine but also effectively perform nonlinear dynamic simulations of a whole aero-engine with <span class="hlt">faults</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19870052876&hterms=Problem+solving&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DProblem%2Bsolving','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870052876&hterms=Problem+solving&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DProblem%2Bsolving"><span>Modeling <span class="hlt">fault</span> <span class="hlt">diagnosis</span> as the activation and use of a frame system. [for pilot problem-solving rating</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Smith, Philip J.; Giffin, Walter C.; Rockwell, Thomas H.; Thomas, Mark</p> <p>1986-01-01</p> <p>Twenty pilots with instrument flight ratings were asked to perform a <span class="hlt">fault-diagnosis</span> task for which they had relevant domain knowledge. The pilots were asked to think out loud as they requested and interpreted information. Performances were then modeled as the activation and use of a frame system. Cognitive biases, memory distortions and losses, and failures to correctly diagnose the problem were studied in the context of this frame system model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20090033812','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20090033812"><span>Modeling, Detection, and Disambiguation of Sensor <span class="hlt">Faults</span> for Aerospace Applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Balaban, Edward; Saxena, Abhinav; Bansal, Prasun; Goebel, Kai F.; Curran, Simon</p> <p>2009-01-01</p> <p>Sensor <span class="hlt">faults</span> continue to be a major hurdle for systems health management to reach its full potential. At the same time, few recorded instances of sensor <span class="hlt">faults</span> exist. It is equally difficult to seed particular sensor <span class="hlt">faults</span>. Therefore, research is underway to better understand the different <span class="hlt">fault</span> modes seen in sensors and to model the <span class="hlt">faults</span>. The <span class="hlt">fault</span> models can then be used in simulated sensor <span class="hlt">fault</span> scenarios to ensure that algorithms can distinguish between sensor <span class="hlt">faults</span> and system <span class="hlt">faults</span>. The paper illustrates the work with data collected from an electro-mechanical actuator in an aerospace setting, equipped with temperature, <span class="hlt">vibration</span>, current, and position sensors. The most common sensor <span class="hlt">faults</span>, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network based classifier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and disambiguation efficacy to severity of <span class="hlt">fault</span> conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JBO....17f6017G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JBO....17f6017G"><span>Differential <span class="hlt">diagnosis</span> of lung carcinoma with three-dimensional quantitative molecular <span class="hlt">vibrational</span> imaging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gao, Liang; Hammoudi, Ahmad A.; Li, Fuhai; Thrall, Michael J.; Cagle, Philip T.; Chen, Yuanxin; Yang, Jian; Xia, Xiaofeng; Fan, Yubo; Massoud, Yehia; Wang, Zhiyong; Wong, Stephen T. C.</p> <p>2012-06-01</p> <p>The advent of molecularly targeted therapies requires effective identification of the various cell types of non-small cell lung carcinomas (NSCLC). Currently, cell type <span class="hlt">diagnosis</span> is performed using small biopsies or cytology specimens that are often insufficient for molecular testing after morphologic analysis. Thus, the ability to rapidly recognize different cancer cell types, with minimal tissue consumption, would accelerate <span class="hlt">diagnosis</span> and preserve tissue samples for subsequent molecular testing in targeted therapy. We report a label-free molecular <span class="hlt">vibrational</span> imaging framework enabling three-dimensional (3-D) image acquisition and quantitative analysis of cellular structures for identification of NSCLC cell types. This diagnostic imaging system employs superpixel-based 3-D nuclear segmentation for extracting such disease-related features as nuclear shape, volume, and cell-cell distance. These features are used to characterize cancer cell types using machine learning. Using fresh unstained tissue samples derived from cell lines grown in a mouse model, the platform showed greater than 97% accuracy for <span class="hlt">diagnosis</span> of NSCLC cell types within a few minutes. As an adjunct to subsequent histology tests, our novel system would allow fast delineation of cancer cell types with minimum tissue consumption, potentially facilitating on-the-spot <span class="hlt">diagnosis</span>, while preserving specimens for additional tests. Furthermore, 3-D measurements of cellular structure permit evaluation closer to the native state of cells, creating an alternative to traditional 2-D histology specimen evaluation, potentially increasing accuracy in diagnosing cell type of lung carcinomas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...85..354P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...85..354P"><span>A PLL-based resampling technique for <span class="hlt">vibration</span> analysis in variable-speed wind turbines with PMSG: A bearing <span class="hlt">fault</span> case</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pezzani, Carlos M.; Bossio, José M.; Castellino, Ariel M.; Bossio, Guillermo R.; De Angelo, Cristian H.</p> <p>2017-02-01</p> <p>Condition monitoring in permanent magnet synchronous machines has gained interest due to the increasing use in applications such as electric traction and power generation. Particularly in wind power generation, non-invasive condition monitoring techniques are of great importance. Usually, in such applications the access to the generator is complex and costly, while unexpected breakdowns results in high repair costs. This paper presents a technique which allows using <span class="hlt">vibration</span> analysis for bearing <span class="hlt">fault</span> detection in permanent magnet synchronous generators used in wind turbines. Given that in wind power applications the generator rotational speed may vary during normal operation, it is necessary to use special sampling techniques to apply spectral analysis of mechanical <span class="hlt">vibrations</span>. In this work, a resampling technique based on order tracking without measuring the rotor position is proposed. To synchronize sampling with rotor position, an estimation of the rotor position obtained from the angle of the voltage vector is proposed. This angle is obtained from a phase-locked loop synchronized with the generator voltages. The proposed strategy is validated by laboratory experimental results obtained from a permanent magnet synchronous generator. Results with single point defects in the outer race of a bearing under variable speed and load conditions are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29693577','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29693577"><span>Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Jinjun; Leng, Yonggang; Lai, Zhihui; Fan, Shengbo</p> <p>2018-04-25</p> <p>Mechanical <span class="hlt">fault</span> <span class="hlt">diagnosis</span> usually requires not only identification of the <span class="hlt">fault</span> characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency <span class="hlt">fault</span> signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the <span class="hlt">fault</span> signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5981854','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5981854"><span>Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Leng, Yonggang; Fan, Shengbo</p> <p>2018-01-01</p> <p>Mechanical <span class="hlt">fault</span> <span class="hlt">diagnosis</span> usually requires not only identification of the <span class="hlt">fault</span> characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency <span class="hlt">fault</span> signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the <span class="hlt">fault</span> signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method. PMID:29693577</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP..100..242W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP..100..242W"><span>Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Shibin; Chen, Xuefeng; Selesnick, Ivan W.; Guo, Yanjie; Tong, Chaowei; Zhang, Xingwu</p> <p>2018-02-01</p> <p>Synchrosqueezing transform (SST) can effectively improve the readability of the time-frequency (TF) representation (TFR) of nonstationary signals composed of multiple components with slow varying instantaneous frequency (IF). However, for signals composed of multiple components with fast varying IF, SST still suffers from TF blurs. In this paper, we introduce a time-frequency analysis (TFA) method called matching synchrosqueezing transform (MSST) that achieves a highly concentrated TF representation comparable to the standard TF reassignment methods (STFRM), even for signals with fast varying IF, and furthermore, MSST retains the reconstruction benefit of SST. MSST captures the philosophy of STFRM to simultaneously consider time and frequency variables, and incorporates three estimators (i.e., the IF estimator, the group delay estimator, and a chirp-rate estimator) into a comprehensive and accurate IF estimator. In this paper, we first introduce the motivation of MSST with three heuristic examples. Then we introduce a precise mathematical definition of a class of chirp-like intrinsic-mode-type functions that locally can be viewed as a sum of a reasonably small number of approximate chirp signals, and we prove that MSST does indeed succeed in estimating chirp-rate and IF of arbitrary functions in this class and succeed in decomposing these functions. Furthermore, we describe an efficient numerical algorithm for the practical implementation of the MSST, and we provide an adaptive IF extraction method for MSST reconstruction. Finally, we verify the effectiveness of the MSST in practical applications for machine <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, including gearbox <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for a wind turbine in variable speed conditions and rotor rub-impact <span class="hlt">fault</span> <span class="hlt">diagnosis</span> for a dual-rotor turbofan engine.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010MSSP...24..289C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010MSSP...24..289C"><span>Automated <span class="hlt">diagnosis</span> of rolling bearings using MRA and neural networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Castejón, C.; Lara, O.; García-Prada, J. C.</p> <p>2010-01-01</p> <p>Any industry needs an efficient predictive plan in order to optimize the management of resources and improve the economy of the plant by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in productive processes are caused by bearings. They begin to deteriorate from early stages of their functional life, also called the incipient level. This manuscript develops an automated <span class="hlt">diagnosis</span> of rolling bearings based on the analysis and classification of signature <span class="hlt">vibrations</span>. The novelty of this work is the application of the methodology proposed for data collected from a quasi-real industrial machine, where rolling bearings support the radial and axial loads the bearings are designed for. Multiresolution analysis (MRA) is used in a first stage in order to extract the most interesting features from signals. Features will be used in a second stage as inputs of a supervised neural network (NN) for classification purposes. Experimental results carried out in a real system show the soundness of the method which detects four bearing conditions (normal, inner race <span class="hlt">fault</span>, outer race <span class="hlt">fault</span> and ball <span class="hlt">fault</span>) in a very incipient stage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009cip3.conf..125H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009cip3.conf..125H"><span>An Ontology for Identifying Cyber Intrusion Induced <span class="hlt">Faults</span> in Process Control Systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hieb, Jeffrey; Graham, James; Guan, Jian</p> <p></p> <p>This paper presents an ontological framework that permits formal representations of process control systems, including elements of the process being controlled and the control system itself. A <span class="hlt">fault</span> <span class="hlt">diagnosis</span> algorithm based on the ontological model is also presented. The algorithm can identify traditional process elements as well as control system elements (e.g., IP network and SCADA protocol) as <span class="hlt">fault</span> sources. When these elements are identified as a likely <span class="hlt">fault</span> source, the possibility exists that the process <span class="hlt">fault</span> is induced by a cyber intrusion. A laboratory-scale distillation column is used to illustrate the model and the algorithm. Coupled with a well-defined statistical process model, this <span class="hlt">fault</span> <span class="hlt">diagnosis</span> approach provides cyber security enhanced <span class="hlt">fault</span> <span class="hlt">diagnosis</span> information to plant operators and can help identify that a cyber attack is underway before a major process failure is experienced.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19980096374','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19980096374"><span>Multiple <span class="hlt">Fault</span> Isolation in Redundant Systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Pattipati, Krishna R.; Patterson-Hine, Ann; Iverson, David</p> <p>1997-01-01</p> <p><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> in large-scale systems that are products of modern technology present formidable challenges to manufacturers and users. This is due to large number of failure sources in such systems and the need to quickly isolate and rectify failures with minimal down time. In addition, for <span class="hlt">fault</span>-tolerant systems and systems with infrequent opportunity for maintenance (e.g., Hubble telescope, space station), the assumption of at most a single <span class="hlt">fault</span> in the system is unrealistic. In this project, we have developed novel block and sequential diagnostic strategies to isolate multiple <span class="hlt">faults</span> in the shortest possible time without making the unrealistic single <span class="hlt">fault</span> assumption.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19990004612','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19990004612"><span>Multiple <span class="hlt">Fault</span> Isolation in Redundant Systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Pattipati, Krishna R.</p> <p>1997-01-01</p> <p><span class="hlt">Fault</span> <span class="hlt">diagnosis</span> in large-scale systems that are products of modem technology present formidable challenges to manufacturers and users. This is due to large number of failure sources in such systems and the need to quickly isolate and rectify failures with minimal down time. In addition, for <span class="hlt">fault</span>-tolerant systems and systems with infrequent opportunity for maintenance (e.g., Hubble telescope, space station), the assumption of at most a single <span class="hlt">fault</span> in the system is unrealistic. In this project, we have developed novel block and sequential diagnostic strategies to isolate multiple <span class="hlt">faults</span> in the shortest possible time without making the unrealistic single <span class="hlt">fault</span> assumption.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948601','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5948601"><span>Diagnosing a Strong-<span class="hlt">Fault</span> Model by Conflict and Consistency</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zhou, Gan; Feng, Wenquan</p> <p>2018-01-01</p> <p>The <span class="hlt">diagnosis</span> method for a weak-<span class="hlt">fault</span> model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-<span class="hlt">fault</span> models, the <span class="hlt">fault</span> modes of which have specific behaviors as well. It is difficult to diagnose a strong-<span class="hlt">fault</span> model due to its non-monotonicity. Currently, <span class="hlt">diagnosis</span> methods usually employ conflicts to isolate possible <span class="hlt">fault</span> and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-<span class="hlt">fault</span> model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-<span class="hlt">fault</span> model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists <span class="hlt">fault</span>. The search approaches offer the best candidate efficiency based on the reasoning result until the <span class="hlt">diagnosis</span> results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods. PMID:29596302</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3435993','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3435993"><span>An Intelligent Sensor Array Distributed System for <span class="hlt">Vibration</span> Analysis and Acoustic Noise Characterization of a Linear Switched Reluctance Actuator</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Salvado, José; Espírito-Santo, António; Calado, Maria</p> <p>2012-01-01</p> <p>This paper proposes a distributed system for analysis and monitoring (DSAM) of <span class="hlt">vibrations</span> and acoustic noise, which consists of an array of intelligent modules, sensor modules, communication bus and a host PC acting as data center. The main advantages of the DSAM are its modularity, scalability, and flexibility for use of different type of sensors/transducers, with analog or digital outputs, and for signals of different nature. Its final cost is also significantly lower than other available commercial solutions. The system is reconfigurable, can operate either with synchronous or asynchronous modes, with programmable sampling frequencies, 8-bit or 12-bit resolution and a memory buffer of 15 kbyte. It allows real-time data-acquisition for signals of different nature, in applications that require a large number of sensors, thus it is suited for monitoring of <span class="hlt">vibrations</span> in Linear Switched Reluctance Actuators (LSRAs). The acquired data allows the full characterization of the LSRA in terms of its response to <span class="hlt">vibrations</span> of structural origins, and the <span class="hlt">vibrations</span> and acoustic noise emitted under normal operation. The DSAM can also be used for electrical machine condition monitoring, machine <span class="hlt">fault</span> <span class="hlt">diagnosis</span>, structural characterization and monitoring, among other applications. PMID:22969364</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3942393','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3942393"><span>Empirical Mode Decomposition and Neural Networks on FPGA for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> in Induction Motors</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus</p> <p>2014-01-01</p> <p>Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple <span class="hlt">faults</span> in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic <span class="hlt">diagnosis</span> during the startup transient of motor <span class="hlt">faults</span> such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications. PMID:24678281</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24678281','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24678281"><span>Empirical mode decomposition and neural networks on FPGA for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> in induction motors.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Camarena-Martinez, David; Valtierra-Rodriguez, Martin; Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus</p> <p>2014-01-01</p> <p>Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple <span class="hlt">faults</span> in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic <span class="hlt">diagnosis</span> during the startup transient of motor <span class="hlt">faults</span> such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003SPIE.5107...44P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003SPIE.5107...44P"><span>A fuzzy Petri-net-based mode identification algorithm for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of complex systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Propes, Nicholas C.; Vachtsevanos, George</p> <p>2003-08-01</p> <p>Complex dynamical systems such as aircraft, manufacturing systems, chillers, motor vehicles, submarines, etc. exhibit continuous and event-driven dynamics. These systems undergo several discrete operating modes from startup to shutdown. For example, a certain shipboard system may be operating at half load or full load or may be at start-up or shutdown. Of particular interest are extreme or "shock" operating conditions, which tend to severely impact <span class="hlt">fault</span> <span class="hlt">diagnosis</span> or the progression of a <span class="hlt">fault</span> leading to a failure. <span class="hlt">Fault</span> conditions are strongly dependent on the operating mode. Therefore, it is essential that in any diagnostic/prognostic architecture, the operating mode be identified as accurately as possible so that such functions as feature extraction, diagnostics, prognostics, etc. can be correlated with the predominant operating conditions. This paper introduces a mode identification methodology that incorporates both time- and event-driven information about the process. A fuzzy Petri net is used to represent the possible successive mode transitions and to detect events from processed sensor signals signifying a mode change. The operating mode is initialized and verified by analysis of the time-driven dynamics through a fuzzy logic classifier. An evidence combiner module is used to combine the results from both the fuzzy Petri net and the fuzzy logic classifier to determine the mode. Unlike most event-driven mode identifiers, this architecture will provide automatic mode initialization through the fuzzy logic classifier and robustness through the combining of evidence of the two algorithms. The mode identification methodology is applied to an AC Plant typically found as a component of a shipboard system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MSSP...99..169C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MSSP...99..169C"><span>A review on data-driven <span class="hlt">fault</span> severity assessment in rolling bearings</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cerrada, Mariela; Sánchez, René-Vinicio; Li, Chuan; Pacheco, Fannia; Cabrera, Diego; Valente de Oliveira, José; Vásquez, Rafael E.</p> <p>2018-01-01</p> <p>Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in industrial processes. In particular, bearings are mechanical components used in most rotating devices and they represent the main source of <span class="hlt">faults</span> in such equipments; reason for which research activities on detecting and diagnosing their <span class="hlt">faults</span> have increased. <span class="hlt">Fault</span> detection aims at identifying whether the device is or not in a <span class="hlt">fault</span> condition, and <span class="hlt">diagnosis</span> is commonly oriented towards identifying the <span class="hlt">fault</span> mode of the device, after detection. An important step after <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> is the analysis of the magnitude or the degradation level of the <span class="hlt">fault</span>, because this represents a support to the decision-making process in condition based-maintenance. However, no extensive works are devoted to analyse this problem, or some works tackle it from the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> point of view. In a rough manner, <span class="hlt">fault</span> severity is associated with the magnitude of the <span class="hlt">fault</span>. In bearings, <span class="hlt">fault</span> severity can be related to the physical size of <span class="hlt">fault</span> or a general degradation of the component. Due to literature regarding the severity assessment of bearing damages is limited, this paper aims at discussing the recent methods and techniques used to achieve the <span class="hlt">fault</span> severity evaluation in the main components of the rolling bearings, such as inner race, outer race, and ball. The review is mainly focused on data-driven approaches such as signal processing for extracting the proper <span class="hlt">fault</span> signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contributions in this field.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_24 --> <div id="page_25" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="481"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19900005786','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19900005786"><span>Display interface concepts for automated <span class="hlt">fault</span> <span class="hlt">diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Palmer, Michael T.</p> <p>1989-01-01</p> <p>An effort which investigated concepts for displaying dynamic system status and <span class="hlt">fault</span> history (propagation) information to the flight crew is described. This investigation was performed by developing several candidate display formats and then conducting comprehension tests to determine those characteristics that made one format preferable to another for presenting this type of information. Twelve subjects participated. Flash tests, or limited time exposure tests, were used to determine the subjects' comprehension of the information presented in the display formats. It was concluded from the results of the comprehension tests that pictographs were more comprehensible than both block diagrams and text for presenting dynamic system status and <span class="hlt">fault</span> history information, and that pictographs were preferred over both block diagrams and text. It was also concluded that the addition of this type of information in the cockpit would help the crew remain aware of the status of their aircraft.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110008499','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110008499"><span>Qualitative Event-Based <span class="hlt">Diagnosis</span>: Case Study on the Second International Diagnostic Competition</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Daigle, Matthew; Roychoudhury, Indranil</p> <p>2010-01-01</p> <p>We describe a <span class="hlt">diagnosis</span> algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a <span class="hlt">diagnosis</span> algorithm must detect, isolate, and identify <span class="hlt">faults</span> in an electrical power distribution testbed and provide corresponding recovery recommendations. The <span class="hlt">diagnosis</span> algorithm embodies a model-based approach, centered around qualitative event-based <span class="hlt">fault</span> isolation. <span class="hlt">Faults</span> produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate <span class="hlt">faults</span>. We augment this approach with model-based <span class="hlt">fault</span> identification, which determines <span class="hlt">fault</span> parameters and helps to further isolate <span class="hlt">faults</span>. We describe the <span class="hlt">diagnosis</span> approach, provide <span class="hlt">diagnosis</span> results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19740021422','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19740021422"><span>On-line <span class="hlt">diagnosis</span> of sequential systems, 2</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sundstrom, R. J.</p> <p>1974-01-01</p> <p>The theory and techniques applicable to the on-line <span class="hlt">diagnosis</span> of sequential systems, were investigated. A complete model for the study of on-line <span class="hlt">diagnosis</span> is developed. First an appropriate class of system models is formulated which can serve as a basis for a theoretical study of on-line <span class="hlt">diagnosis</span>. Then notions of realization, <span class="hlt">fault</span>, <span class="hlt">fault</span>-tolerance and diagnosability are formalized which have meaningful interpretations in the the context of on-line <span class="hlt">diagnosis</span>. The <span class="hlt">diagnosis</span> of systems which are structurally decomposed and are represented as a network of smaller systems is studied. The <span class="hlt">fault</span> set considered is the set of <span class="hlt">faults</span> which only affect one component system is the network. A characterization of those networks which can be diagnosed using a purely combinational detector is achieved. A technique is given which can be used to realize any network by a network which is diagnosable in the above sense. Limits are found on the amount of redundancy involved in any such technique.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28606709','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28606709"><span>A review and comparison of <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> methods for squirrel-cage induction motors: State of the art.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Liu, Yiqi; Bazzi, Ali M</p> <p>2017-09-01</p> <p>Preventing induction motors (IMs) from failure and shutdown is important to maintain functionality of many critical loads in industry and commerce. This paper provides a comprehensive review of <span class="hlt">fault</span> detection and <span class="hlt">diagnosis</span> (FDD) methods targeting all the four major types of <span class="hlt">faults</span> in IMs. Popular FDD methods published up to 2010 are briefly introduced, while the focus of the review is laid on the state-of-the-art FDD techniques after 2010, i.e. in 2011-2015 and some in 2016. Different FDD methods are introduced and classified into four categories depending on their application domains, instead of on <span class="hlt">fault</span> types like in many other reviews, to better reveal hidden connections and similarities of different FDD methods. Detailed comparisons of the reviewed papers after 2010 are given in tables for fast referring. Finally, a dedicated discussion session is provided, which presents recent developments, trends and remaining difficulties regarding to FDD of IMs, to inspire novel research ideas and new research possibilities. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140005560','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140005560"><span><span class="hlt">Vibration</span> Based Sun Gear Damage Detection</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hood, Adrian; LaBerge, Kelsen; Lewicki, David; Pines, Darryll</p> <p>2013-01-01</p> <p>Seeded <span class="hlt">fault</span> experiments were conducted on the planetary stage of an OH-58C helicopter transmission. Two <span class="hlt">vibration</span> based methods are discussed that isolate the dynamics of the sun gear from that of the planet gears, bearings, input spiral bevel stage, and other components in and around the gearbox. Three damaged sun gears: two spalled and one cracked, serve as the focus of this current work. A non-sequential <span class="hlt">vibration</span> separation algorithm was developed and the resulting signals analyzed. The second method uses only the time synchronously averaged data but takes advantage of the signal/source mapping required for <span class="hlt">vibration</span> separation. Both algorithms were successful in identifying the spall damage. Sun gear damage was confirmed by the presence of sun mesh groups. The sun tooth crack condition was inconclusive.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130001690','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130001690"><span>An Integrated Framework for Model-Based Distributed <span class="hlt">Diagnosis</span> and Prognosis</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bregon, Anibal; Daigle, Matthew J.; Roychoudhury, Indranil</p> <p>2012-01-01</p> <p><span class="hlt">Diagnosis</span> and prognosis are necessary tasks for system reconfiguration and <span class="hlt">fault</span>-adaptive control in complex systems. <span class="hlt">Diagnosis</span> consists of detection, isolation and identification of <span class="hlt">faults</span>, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed <span class="hlt">diagnosis</span> and prognosis, where system decomposition is used to enable the <span class="hlt">diagnosis</span> and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local <span class="hlt">diagnosis</span> and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different <span class="hlt">fault</span> scenarios. Our experiments show that our approach correctly performs distributed <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and prognosis in an efficient and robust manner.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110008264','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110008264"><span>Early Oscillation Detection for DC/DC Converter <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wang, Bright L.</p> <p>2011-01-01</p> <p>The electrical power system of a spacecraft plays a very critical role for space mission success. Such a modern power system may contain numerous hybrid DC/DC converters both inside the power system electronics (PSE) units and onboard most of the flight electronics modules. One of the faulty conditions for DC/DC converter that poses serious threats to mission safety is the random occurrence of oscillation related to inherent instability characteristics of the DC/DC converters and design deficiency of the power systems. To ensure the highest reliability of the power system, oscillations in any form shall be promptly detected during part level testing, system integration tests, flight health monitoring, and on-board <span class="hlt">fault</span> <span class="hlt">diagnosis</span>. The popular gain/phase margin analysis method is capable of predicting stability levels of DC/DC converters, but it is limited only to verification of designs and to part-level testing on some of the models. This method has to inject noise signals into the control loop circuitry as required, thus, interrupts the DC/DC converter's normal operation and increases risks of degrading and damaging the flight unit. A novel technique to detect oscillations at early stage for flight hybrid DC/DC converters was developed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JPhCS.364a2085C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JPhCS.364a2085C"><span>The technique of entropy optimization in motor current signature analysis and its application in the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of gear transmission</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Xiaoguang; Liang, Lin; Liu, Fei; Xu, Guanghua; Luo, Ailing; Zhang, Sicong</p> <p>2012-05-01</p> <p>Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In <span class="hlt">diagnosis</span> the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and the condition monitoring of machine tools.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030064150','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030064150"><span>Detailed <span class="hlt">Vibration</span> Analysis of Pinion Gear with Time-Frequency Methods</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Mosher, Marianne; Pryor, Anna H.; Lewicki, David G.</p> <p>2003-01-01</p> <p>In this paper, the authors show a detailed analysis of the <span class="hlt">vibration</span> signal from the destructive testing of a spiral bevel gear and pinion pair containing seeded <span class="hlt">faults</span>. The <span class="hlt">vibration</span> signal is analyzed in the time domain, frequency domain and with four time-frequency transforms: the Short Time Frequency Transform (STFT), the Wigner-Ville Distribution with the Choi-Williams kernel (WV-CW), the Continuous Wavelet' Transform (CWT) and the Discrete Wavelet Transform (DWT). <span class="hlt">Vibration</span> data of bevel gear tooth fatigue cracks, under a variety of operating load levels and damage conditions, are analyzed using these methods. A new metric for automatic anomaly detection is developed and can be produced from any systematic numerical representation of the <span class="hlt">vibration</span> signals. This new metric reveals indications of gear damage with all of the time-frequency transforms, as well as time and frequency representations, on this data set. Analysis with the CWT detects changes in the signal at low torque levels not found with the other transforms. The WV-CW and CWT use considerably more resources than the STFT and the DWT. More testing of the new metric is needed to determine its value for automatic anomaly detection and to develop <span class="hlt">fault</span> detection methods for the metric.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19930016777','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19930016777"><span>Machine learning techniques for <span class="hlt">fault</span> isolation and sensor placement</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carnes, James R.; Fisher, Douglas H.</p> <p>1993-01-01</p> <p><span class="hlt">Fault</span> isolation and sensor placement are vital for monitoring and <span class="hlt">diagnosis</span>. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid <span class="hlt">fault</span> isolation and sensor placement, with an eye towards minimality, <span class="hlt">fault</span> coverage, and noise tolerance.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MeScT..28d5011D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MeScT..28d5011D"><span>Self adaptive multi-scale morphology AVG-Hat filter and its application to <span class="hlt">fault</span> feature extraction for wheel bearing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Deng, Feiyue; Yang, Shaopu; Tang, Guiji; Hao, Rujiang; Zhang, Mingliang</p> <p>2017-04-01</p> <p>Wheel bearings are essential mechanical components of trains, and <span class="hlt">fault</span> detection of the wheel bearing is of great significant to avoid economic loss and casualty effectively. However, considering the operating conditions, detection and extraction of the <span class="hlt">fault</span> features hidden in the heavy noise of the <span class="hlt">vibration</span> signal have become a challenging task. Therefore, a novel method called adaptive multi-scale AVG-Hat morphology filter (MF) is proposed to solve it. The morphology AVG-Hat operator not only can suppress the interference of the strong background noise greatly, but also enhance the ability of extracting <span class="hlt">fault</span> features. The improved envelope spectrum sparsity (IESS), as a new evaluation index, is proposed to select the optimal filtering signal processed by the multi-scale AVG-Hat MF. It can present a comprehensive evaluation about the intensity of <span class="hlt">fault</span> impulse to the background noise. The weighted coefficients of the different scale structural elements (SEs) in the multi-scale MF are adaptively determined by the particle swarm optimization (PSO) algorithm. The effectiveness of the method is validated by analyzing the real wheel bearing <span class="hlt">fault</span> <span class="hlt">vibration</span> signal (e.g. outer race <span class="hlt">fault</span>, inner race <span class="hlt">fault</span> and rolling element <span class="hlt">fault</span>). The results show that the proposed method could improve the performance in the extraction of <span class="hlt">fault</span> features effectively compared with the multi-scale combined morphological filter (CMF) and multi-scale morphology gradient filter (MGF) methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JSV...333.3321J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JSV...333.3321J"><span><span class="hlt">Fault</span> identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiang, Fan; Zhu, Zhencai; Li, Wei; Zhou, Gongbo; Chen, Guoan</p> <p>2014-07-01</p> <p>Accurately identifying <span class="hlt">faults</span> in rotor-bearing systems by analyzing <span class="hlt">vibration</span> signals, which are nonlinear and nonstationary, is challenging. To address this issue, a new approach based on ensemble empirical mode decomposition (EEMD) and self-zero space projection analysis is proposed in this paper. This method seeks to identify <span class="hlt">faults</span> appearing in a rotor-bearing system using simple algebraic calculations and projection analyses. First, EEMD is applied to decompose the collected <span class="hlt">vibration</span> signals into a set of intrinsic mode functions (IMFs) for features. Second, these extracted features under various mechanical health conditions are used to design a self-zero space matrix according to space projection analysis. Finally, the so-called projection indicators are calculated to identify the rotor-bearing system's <span class="hlt">faults</span> with simple decision logic. Experiments are implemented to test the reliability and effectiveness of the proposed approach. The results show that this approach can accurately identify <span class="hlt">faults</span> in rotor-bearing systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70187040','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70187040"><span>San Andreas tremor cascades define deep <span class="hlt">fault</span> zone complexity</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Shelly, David R.</p> <p>2015-01-01</p> <p>Weak seismic <span class="hlt">vibrations</span> - tectonic tremor - can be used to delineate some plate boundary <span class="hlt">faults</span>. Tremor on the deep San Andreas <span class="hlt">Fault</span>, located at the boundary between the Pacific and North American plates, is thought to be a passive indicator of slow <span class="hlt">fault</span> slip. San Andreas <span class="hlt">Fault</span> tremor migrates at up to 30 m s-1, but the processes regulating tremor migration are unclear. Here I use a 12-year catalogue of more than 850,000 low-frequency earthquakes to systematically analyse the high-speed migration of tremor along the San Andreas <span class="hlt">Fault</span>. I find that tremor migrates most effectively through regions of greatest tremor production and does not propagate through regions with gaps in tremor production. I interpret the rapid tremor migration as a self-regulating cascade of seismic ruptures along the <span class="hlt">fault</span>, which implies that tremor may be an active, rather than passive participant in the slip propagation. I also identify an isolated group of tremor sources that are offset eastwards beneath the San Andreas <span class="hlt">Fault</span>, possibly indicative of the interface between the Monterey Microplate, a hypothesized remnant of the subducted Farallon Plate, and the North American Plate. These observations illustrate a possible link between the central San Andreas <span class="hlt">Fault</span> and tremor-producing subduction zones.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MeScT..29d5104Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MeScT..29d5104Y"><span>Multisensor signal denoising based on matching synchrosqueezing wavelet transform for mechanical <span class="hlt">fault</span> condition assessment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yi, Cancan; Lv, Yong; Xiao, Han; Huang, Tao; You, Guanghui</p> <p>2018-04-01</p> <p>Since it is difficult to obtain the accurate running status of mechanical equipment with only one sensor, multisensor measurement technology has attracted extensive attention. In the field of mechanical <span class="hlt">fault</span> <span class="hlt">diagnosis</span> and condition assessment based on <span class="hlt">vibration</span> signal analysis, multisensor signal denoising has emerged as an important tool to improve the reliability of the measurement result. A reassignment technique termed the synchrosqueezing wavelet transform (SWT) has obvious superiority in slow time-varying signal representation and denoising for <span class="hlt">fault</span> <span class="hlt">diagnosis</span> applications. The SWT uses the time-frequency reassignment scheme, which can provide signal properties in 2D domains (time and frequency). However, when the measured signal contains strong noise components and fast varying instantaneous frequency, the performance of SWT-based analysis still depends on the accuracy of instantaneous frequency estimation. In this paper, a matching synchrosqueezing wavelet transform (MSWT) is investigated as a potential candidate to replace the conventional synchrosqueezing transform for the applications of denoising and <span class="hlt">fault</span> feature extraction. The improved technology utilizes the comprehensive instantaneous frequency estimation by chirp rate estimation to achieve a highly concentrated time-frequency representation so that the signal resolution can be significantly improved. To exploit inter-channel dependencies, the multisensor denoising strategy is performed by using a modulated multivariate oscillation model to partition the time-frequency domain; then, the common characteristics of the multivariate data can be effectively identified. Furthermore, a modified universal threshold is utilized to remove noise components, while the signal components of interest can be retained. Thus, a novel MSWT-based multisensor signal denoising algorithm is proposed in this paper. The validity of this method is verified by numerical simulation, and experiments including a rolling</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880019996','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880019996"><span>ARGES: an Expert System for <span class="hlt">Fault</span> <span class="hlt">Diagnosis</span> Within Space-Based ECLS Systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Pachura, David W.; Suleiman, Salem A.; Mendler, Andrew P.</p> <p>1988-01-01</p> <p>ARGES (Atmospheric Revitalization Group Expert System) is a demonstration prototype expert system for <span class="hlt">fault</span> management for the Solid Amine, Water Desorbed (SAWD) CO2 removal assembly, associated with the Environmental Control and Life Support (ECLS) System. ARGES monitors and reduces data in real time from either the SAWD controller or a simulation of the SAWD assembly. It can detect gradual degradations or predict failures. This allows graceful shutdown and scheduled maintenance, which reduces crew maintenance overhead. Status and <span class="hlt">fault</span> information is presented in a user interface that simulates what would be seen by a crewperson. The user interface employs animated color graphics and an object oriented approach to provide detailed status information, <span class="hlt">fault</span> identification, and explanation of reasoning in a rapidly assimulated manner. In addition, ARGES recommends possible courses of action for predicted and actual <span class="hlt">faults</span>. ARGES is seen as a forerunner of AI-based <span class="hlt">fault</span> management systems for manned space systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JSV...421..220L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JSV...421..220L"><span>Development of a morphological convolution operator for bearing <span class="hlt">fault</span> detection</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan</p> <p>2018-05-01</p> <p>This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing <span class="hlt">fault</span> detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the <span class="hlt">fault</span> detection ability of the reported MUDWs. Experimental <span class="hlt">vibration</span> signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on <span class="hlt">fault</span> detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting <span class="hlt">fault</span> features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race <span class="hlt">faults</span> and inner race <span class="hlt">faults</span> of rolling element bearings.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/11954726','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/11954726"><span>The numerical modelling and process simulation for the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotary kiln incinerator.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Roh, S D; Kim, S W; Cho, W S</p> <p>2001-10-01</p> <p>The numerical modelling and process simulation for the <span class="hlt">fault</span> <span class="hlt">diagnosis</span> of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process <span class="hlt">diagnosis</span>, analysis and control.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25185834','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25185834"><span>Temperature-dependent stability of stacking <span class="hlt">faults</span> in Al, Cu and Ni: first-principles analysis.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bhogra, Meha; Ramamurty, U; Waghmare, Umesh V</p> <p>2014-09-24</p> <p>We present comparative analysis of microscopic mechanisms relevant to plastic deformation of the face-centered cubic (FCC) metals Al, Cu, and Ni, through determination of the temperature-dependent free energies of intrinsic and unstable stacking <span class="hlt">faults</span> along [1 1̄ 0] and [1 2̄ 1] on the (1 1 1) plane using first-principles density-functional-theory-based calculations. We show that <span class="hlt">vibrational</span> contribution results in significant decrease in the free energy of barriers and intrinsic stacking <span class="hlt">faults</span> (ISFs) of Al, Cu, and Ni with temperature, confirming an important role of thermal fluctuations in the stability of stacking <span class="hlt">faults</span> (SFs) and deformation at elevated temperatures. In contrast to Al and Ni, the <span class="hlt">vibrational</span> spectrum of the unstable stacking <span class="hlt">fault</span> (USF[1 2̄ 1]) in Cu reveals structural instabilities, indicating that the energy barrier (γusf) along the (1 1 1)[1 2̄ 1] slip system in Cu, determined by typical first-principles calculations, is an overestimate, and its commonly used interpretation as the energy release rate needed for dislocation nucleation, as proposed by Rice (1992 J. Mech. Phys. Solids 40 239), should be taken with caution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/12858981','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/12858981"><span>Creating an automated chiller <span class="hlt">fault</span> detection and diagnostics tool using a data <span class="hlt">fault</span> library.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bailey, Margaret B; Kreider, Jan F</p> <p>2003-07-01</p> <p>Reliable, automated detection and <span class="hlt">diagnosis</span> of abnormal behavior within vapor compression refrigeration cycle (VCRC) equipment is extremely desirable for equipment owners and operators. The specific type of VCRC equipment studied in this paper is a 70-ton helical rotary, air-cooled chiller. The <span class="hlt">fault</span> detection and diagnostic (FDD) tool developed as part of this research analyzes chiller operating data and detects <span class="hlt">faults</span> through recognizing trends or patterns existing within the data. The FDD method incorporates a neural network (NN) classifier to infer the current state given a vector of observables. Therefore the FDD method relies upon the availability of normal and <span class="hlt">fault</span> empirical data for training purposes and therefore a <span class="hlt">fault</span> library of empirical data is assembled. This paper presents procedures for conducting sophisticated <span class="hlt">fault</span> experiments on chillers that simulate air-cooled condenser, refrigerant, and oil related <span class="hlt">faults</span>. The experimental processes described here are not well documented in literature and therefore will provide the interested reader with a useful guide. In addition, the authors provide evidence, based on both thermodynamics and empirical data analysis, that chiller performance is significantly degraded during <span class="hlt">fault</span> operation. The chiller's performance degradation is successfully detected and classified by the NN FDD classifier as discussed in the paper's final section.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MSSP...85..296U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MSSP...85..296U"><span>Trends in non-stationary signal processing techniques applied to <span class="hlt">vibration</span> analysis of wind turbine drive train - A contemporary survey</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Uma Maheswari, R.; Umamaheswari, R.</p> <p>2017-02-01</p> <p>Condition Monitoring System (CMS) substantiates potential economic benefits and enables prognostic maintenance in wind turbine-generator failure prevention. <span class="hlt">Vibration</span> Monitoring and Analysis is a powerful tool in drive train CMS, which enables the early detection of impending failure/damage. In variable speed drives such as wind turbine-generator drive trains, the <span class="hlt">vibration</span> signal acquired is of non-stationary and non-linear. The traditional stationary signal processing techniques are inefficient to diagnose the machine <span class="hlt">faults</span> in time varying conditions. The current research trend in CMS for drive-train focuses on developing/improving non-linear, non-stationary feature extraction and <span class="hlt">fault</span> classification algorithms to improve <span class="hlt">fault</span> detection/prediction sensitivity and selectivity and thereby reducing the misdetection and false alarm rates. In literature, review of stationary signal processing algorithms employed in <span class="hlt">vibration</span> analysis is done at great extent. In this paper, an attempt is made to review the recent research advances in non-linear non-stationary signal processing algorithms particularly suited for variable speed wind turbines.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_25 --> <div class="footer-extlink text-muted" style="margin-bottom:1rem; text-align:center;">Some links on this page may take you to non-federal websites. 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