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Sample records for vibration fault diagnosis

  1. Distributed bearing fault diagnosis based on vibration analysis

    NASA Astrophysics Data System (ADS)

    Dolenc, Boštjan; Boškoski, Pavle; Juri?i?, ?ani

    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.

  2. Vibration signal models for fault diagnosis of planetary gearboxes

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Zuo, Ming J.

    2012-10-01

    A thorough understanding of the spectral structure of planetary gear system vibration signals is helpful to fault diagnosis of planetary gearboxes. Considering both the amplitude modulation and the frequency modulation effects due to gear damage and periodically time variant working condition, as well as the effect of vibration transfer path, signal models of gear damage for fault diagnosis of planetary gearboxes are given and the spectral characteristics are summarized in closed form. Meanwhile, explicit equations for calculating the characteristic frequency of local and distributed gear fault are deduced. The theoretical derivations are validated using both experimental and industrial signals. According to the theoretical basis derived, manually created local gear damage of different levels and naturally developed gear damage in a planetary gearbox can be detected and located.

  3. Fault diagnosis of planetary gearboxes via torsional vibration signal analysis

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Zuo, Ming J.

    2013-04-01

    Torsional vibration signals are theoretically free from the amplitude modulation effect caused by time variant vibration transfer paths due to the rotation of planet carrier and sun gear, and therefore their spectral structure are simpler than transverse vibration signals. Thus, it is potentially easy and effective to diagnose planetary gearbox faults via torsional vibration signal analysis. We give explicit equations to model torsional vibration signals, considering both distributed gear faults (like manufacturing or assembly errors) and local gear faults (like pitting, crack or breakage of one tooth), and derive the characteristics of both the traditional Fourier spectrum and the proposed demodulated spectra of amplitude envelope and instantaneous frequency. These derivations are not only effective to diagnose single gear fault of planetary gearboxes, but can also be generalized to detect and locate multiple gear faults. We validate experimentally the signal models, as well as the Fourier spectral analysis and demodulation analysis methods.

  4. Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis

    PubMed Central

    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

  5. Vibration sensor data denoising using a time-frequency manifold for machinery fault diagnosis.

    PubMed

    He, Qingbo; Wang, Xiangxiang; Zhou, Qiang

    2013-01-01

    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

  6. Diagnosis of Centrifugal Pump Faults Using Vibration Methods

    NASA Astrophysics Data System (ADS)

    Albraik, A.; Althobiani, F.; Gu, F.; Ball, A.

    2012-05-01

    Pumps are the largest single consumer of power in industry. This means that faulty pumps cause a high rate of energy loss with associated performance degradation, high vibration levels and significant noise radiation. This paper investigates the correlations between pump performance parameters including head, flow rate and energy consumption and surface vibration for the purpose of both pump condition monitoring and performance assessment. Using an in-house pump system, a number of experiments have been carried out on a centrifugal pump system using five impellers: one in good condition and four others with different defects, and at different flow rates for the comparison purposes. The results have shown that each defective impeller performance curve (showing flow, head, efficiency and NPSH (Net Positive Suction Head) is different from the benchmark curve showing the performance of the impeller in good condition. The exterior vibration responses were investigated to extract several key features to represent the healthy pump condition, pump operating condition and pump energy consumption. In combination, these parameter allow an optimal decision for pump overhaul to be made [1].

  7. Fault diagnosis

    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 examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

  8. Research of high-resolution vibration signal detection technique and application to mechanical fault diagnosis

    NASA Astrophysics Data System (ADS)

    Fan, Y. S.; Zheng, G. T.

    2007-02-01

    Bilinear time-frequency transformation can possess a simultaneous high resolution both in the time domain and the frequency domain. It can be utilised to detect weak transient vibration signals generated by early mechanical faults in complex background and thus is of great importance to early mechanical fault diagnoses. It has been found that the spectrogram has low resolution, and there is strong cross-terms in Wigner-Ville distribution and frequency aliasing and information loss in Choi-Williams distribution (CWD). Hence, they cannot achieve satisfied transient signal detection results. To enhance the capability of detecting transient vibration signals, based on the analysis of exponent distribution, this paper presents some novel alias-free time-frequency distributions. These distributions can avoid the information loss in CWD while suppressing the cross-terms. Moreover, they have high simultaneous resolutions in both the time and frequency domain. Digital simulation and gearbox fault diagnosis experiments prove that these new distributions can effectively detect transient components from complicated mechanical vibration signals.

  9. Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

    PubMed Central

    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

  10. 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.

  11. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

    PubMed Central

    Cerrada, Mariela; Sánchez, René Vinicio; Cabrera, Diego; Zurita, Grover; Li, Chuan

    2015-01-01

    There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%. PMID:26393603

  12. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal.

    PubMed

    Cerrada, Mariela; Vinicio Sánchez, René; Cabrera, Diego; Zurita, Grover; Li, Chuan

    2015-01-01

    There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%. PMID:26393603

  13. Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis.

    PubMed

    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. PMID:24975564

  14. Acoustic Emission, Cylinder Pressure and Vibration: A Multisensor Approach to Robust Fault Diagnosis \\Lambda

    E-print Network

    Sharkey, Amanda

    in the combustion process of an internal combus­ tion piston engine, malfunctions, this malfunction may be reflected, University of Sheffield, U.K. amanda@dcs.shef.ac.uk Abstract: When an engine component participating of a diesel engine and 4 fault conditions, Artificial Neural Nets based on data from any one of these three

  15. 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.

  16. Energy operator demodulating of optimal resonance components for the compound faults diagnosis of gearboxes

    NASA Astrophysics Data System (ADS)

    Zhang, Dingcheng; Yu, Dejie; Zhang, Wenyi

    2015-11-01

    Compound faults diagnosis is a challenge for rotating machinery fault diagnosis. The vibration signals measured from gearboxes are usually complex, non-stationary, and nonlinear. When compound faults occur in a gearbox, weak fault characteristic signals are always submerged by the strong ones. Therefore, it is difficult to detect a weak fault by using the demodulating analysis of vibration signals of gearboxes directly. The key to compound faults diagnosis of gearboxes is to separate different fault characteristic signals from the collected vibration signals. Aiming at that problem, a new method for the compound faults diagnosis of gearboxes is proposed based on the energy operator demodulating of optimal resonance components. In this method, the genetic algorithm is first used to obtain the optimal decomposition parameters. Then the compound faults vibration signals of a gearbox are subject to resonance-based signal sparse decomposition (RSSD) to separate the fault characteristic signals of the gear and the bearing by using the optimal decomposition parameters. Finally, the separated fault characteristic signals are analyzed by energy operator demodulating, and each one’s instantaneous amplitude can be calculated. According to the spectra of instantaneous amplitudes of fault characteristic signals, the faults of the gear and the bearing can be diagnosed, respectively. The performance of the proposed method is validated by using the simulation data and the experiment vibration signals from a gearbox with compound faults.

  17. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

    PubMed Central

    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

  18. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

    PubMed

    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

  19. Underground distribution cable incipient fault diagnosis system 

    E-print Network

    Jaafari Mousavi, Mir Rasoul

    2007-04-25

    This dissertation presents a methodology for an efficient, non-destructive, and online incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they become catastrophic. The system provides vital information...

  20. Rolling bearing fault diagnosis using an optimization deep belief network

    NASA Astrophysics Data System (ADS)

    Shao, Haidong; Jiang, Hongkai; Zhang, Xun; Niu, Maogui

    2015-11-01

    The vibration signals measured from a rolling bearing are usually affected by the variable operating conditions and background noise which lead to the diversity and complexity of the vibration signal characteristics, and it is a challenge to effectively identify the rolling bearing faults from such vibration signals with no further fault information. In this paper, a novel optimization deep belief network (DBN) is proposed for rolling bearing fault diagnosis. Stochastic gradient descent is used to efficiently fine-tune all the connection weights after the pre-training of restricted Boltzmann machines (RBMs) based on the energy functions, and the classification accuracy of the DBN is improved. Particle swarm is further used to decide the optimal structure of the trained DBN, and the optimization DBN is designed. The proposed method is applied to analyze the simulation signal and experimental signal of a rolling bearing. The results confirm that the proposed method is more accurate and robust than other intelligent methods.

  1. Sensor Fault Diagnosis Using Principal Component Analysis 

    E-print Network

    Sharifi, Mahmoudreza

    2010-07-14

    The purpose of this research is to address the problem of fault diagnosis of sensors which measure a set of direct redundant variables. This study proposes: 1. A method for linear senor fault diagnosis 2. An analysis of isolability and detectability...

  2. 3D fluid-structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS

    NASA Astrophysics Data System (ADS)

    Saeed, R. A.; Galybin, A. N.; Popov, V.

    2013-01-01

    This paper discusses condition monitoring and fault diagnosis 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 faults 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.

  3. Planetary gearbox fault diagnosis using an adaptive stochastic resonance method

    NASA Astrophysics Data System (ADS)

    Lei, Yaguo; Han, Dong; Lin, Jing; He, Zhengjia

    2013-07-01

    Planetary gearboxes are widely used in aerospace, automotive and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause gear tooth damage such as fatigue crack and teeth missed etc. The challenging issues in fault diagnosis of planetary gearboxes include selection of sensitive measurement locations, investigation of vibration transmission paths and weak feature extraction. One of them is how to effectively discover the weak characteristics from noisy signals of faulty components in planetary gearboxes. To address the issue in fault diagnosis of planetary gearboxes, an adaptive stochastic resonance (ASR) method is proposed in this paper. The ASR method utilizes the optimization ability of ant colony algorithms and adaptively realizes the optimal stochastic resonance system matching input signals. Using the ASR method, the noise may be weakened and weak characteristics highlighted, and therefore the faults can be diagnosed accurately. A planetary gearbox test rig is established and experiments with sun gear faults including a chipped tooth and a missing tooth are conducted. And the vibration signals are collected under the loaded condition and various motor speeds. The proposed method is used to process the collected signals and the results of feature extraction and fault diagnosis demonstrate its effectiveness.

  4. Detection of stator winding faults in induction machines using flux and vibration analysis

    NASA Astrophysics Data System (ADS)

    Lamim Filho, P. C. M.; Pederiva, R.; Brito, J. N.

    2014-01-01

    This work aims at presenting the detection and diagnosis of electrical faults in the stator winding of three-phase induction motors using magnetic flux and vibration analysis techniques. A relationship was established between the main electrical faults (inter-turn short circuits and unbalanced voltage supplies) and the signals of magnetic flux and vibration, in order to identify the characteristic frequencies of those faults. The experimental results showed the efficiency of the conjugation of these techniques for detection, diagnosis and monitoring tasks. The results were undoubtedly impressive and can be adapted and used in real predictive maintenance programs in industries.

  5. A survey of fault diagnosis technology

    NASA Technical Reports Server (NTRS)

    Riedesel, Joel

    1989-01-01

    Existing techniques and methodologies for fault diagnosis 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 fault diagnosis problem.

  6. Fault Diagnosis in HVAC Chillers

    NASA Technical Reports Server (NTRS)

    Choi, Kihoon; Namuru, Setu M.; Azam, Mohammad S.; Luo, Jianhui; Pattipati, Krishna R.; Patterson-Hine, Ann

    2005-01-01

    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 fault-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 fault 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 faults 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 fault 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 faults.

  7. Low-cost motor drive embedded fault diagnosis systems 

    E-print Network

    Akin, Bilal

    2009-05-15

    cost incipient fault detection of inverter-fed driven motors. Basically, low order inverter harmonics contributions to fault diagnosis, a motor drive embedded condition monitoring method, analysis of motor fault signatures in noisy line current, and a...

  8. Hierarchical Approach to Fault Diagnosis Master's Thesis

    E-print Network

    van Gemund, Arjan J.C.

    & Technology BV, for maintaining the development infrastructure and enduring my not always healthy updates. Some diagnostic techniques compromise on the completeness of the search, bypassing fault hypothesis for non-strict compilation of dictionaries, allowing faster diagnosis at run-time. Finally, empirical

  9. A dynamic integrated fault diagnosis method for power transformers.

    PubMed

    Gao, Wensheng; Bai, Cuifen; Liu, Tong

    2015-01-01

    In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis 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 diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis 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 diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified. PMID:25685841

  10. Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings

    NASA Astrophysics Data System (ADS)

    Dong, Guangming; Chen, Jin

    2012-11-01

    Rolling element bearings are frequently used in rotary machinery, but they are also fragile mechanical parts. Hence, exact condition monitoring and fault diagnosis for them plays an important role in ensuring machinery's reliable running. Timely diagnosis of early bearing faults is desirable, but the early fault signatures are easily submerged in noise. In this paper, Wigner-Ville spectrum based on cyclic spectral density (CSWVS for a brief notation) is studied, which is able to represent the cyclostationary signals while reducing the masking effect of additive stationary noise. Both simulations and experiments show that CSWVS is a noise resistant time frequency analysis technique for extracting bearing fault patterns, when bearing signals are under influences of random noise and gear vibrations. The 3-D feature of the CSWVS is proved useful in extracting bearing fault pattern from gearbox vibration signals, where bearing signals are affected by gear meshing vibration and noise. Besides, CSWVS utilizes the second order cyclostationary property of the vibration signals produced by bearing distributed fault, and clearly extracts its fault features, which cannot be extracted by envelope analysis. To quantitatively describe the extent of bearing fault, Renyi information encoded in the time frequency diagram of CSWVS is studied. It is shown to be a more sensitive index to reflect bearing performance degradation, compared with the spectral entropy (SE), squared envelope spectrum entropy (SESE) and Renyi informations for WVD, PWVD, especially when SNR is low.

  11. Fault Diagnosis Using Consensus of Markov Chains Dejan P. Jovanovi

    E-print Network

    Pollett, Phil

    Fault Diagnosis Using Consensus of Markov Chains Dejan P. Jovanovi Department of Mathematics--A fault diagnosis procedure is proposed based on consensus in a group of local agents/experts. Local to accommodate Hidden Markov models (HMMs). Index Terms--Fault diagnosis, consensus algorithm, mixtures of Markov

  12. Blind Source Separation and Dynamic Fuzzy Neural Network for Fault Diagnosis in Machines

    NASA Astrophysics Data System (ADS)

    Huang, Haifeng; Ouyang, Huajiang; Gao, Hongli

    2015-07-01

    Many assessment and detection methods are used to diagnose faults in machines. High accuracy in fault detection and diagnosis can be achieved by using numerical methods with noise-resistant properties. However, to some extent, noise always exists in measured data on real machines, which affects the identification results, especially in the diagnosis of early- stage faults. In view of this situation, a damage assessment method based on blind source separation and dynamic fuzzy neural network (DFNN) is presented to diagnose the early-stage machinery faults in this paper. In the processing of measurement signals, blind source separation is adopted to reduce noise. Then sensitive features of these faults are obtained by extracting low dimensional manifold characteristics from the signals. The model for fault diagnosis is established based on DFNN. Furthermore, on-line computation is accelerated by means of compressed sensing. Numerical vibration signals of ball screw fault modes are processed on the model for mechanical fault diagnosis and the results are in good agreement with the actual condition even at the early stage of fault development. This detection method is very useful in practice and feasible for early-stage fault diagnosis.

  13. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network

    PubMed Central

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis 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 fault diagnosis 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 fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault 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 faults and fault 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 fault diagnosis 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 fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information. PMID:25938760

  14. Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study

    PubMed Central

    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

  15. Advanced fault diagnosis methods in molecular networks.

    PubMed

    Habibi, Iman; Emamian, Effat S; Abdi, Ali

    2014-01-01

    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 fault diagnosis 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-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis 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-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault 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

  16. Statistical Fault Detection & Diagnosis Expert System

    Energy Science and Technology Software Center (ESTSC)

    1996-12-18

    STATMON is an expert system that performs real-time fault detection and diagnosis of redundant sensors in any industrial process requiring high reliability. After a training period performed during normal operation, the expert system monitors the statistical properties of the incoming signals using a pattern recognition test. If the test determines that statistical properties of the signals have changed, the expert system performs a sequence of logical steps to determine which sensor or machine component hasmore »degraded.« less

  17. Diverse neural net solutions to a fault diagnosis problem \\Lambda

    E-print Network

    Sharkey, Amanda

    Abstract The development of a neural net system for fault diagnosis in a mar­ ine diesel engine system solution to a problem of fault diagnosis in a four­stroke marine diesel engine; that of early to recognise faults in simulated data from a diesel engine; specifically to classify combustion condition

  18. Planetary Gearbox Fault Detection Using Vibration Separation Techniques

    NASA Technical Reports Server (NTRS)

    Lewicki, David G.; LaBerge, Kelsen E.; Ehinger, Ryan T.; Fetty, Jason

    2011-01-01

    Studies were performed to demonstrate the capability to detect planetary gear and bearing faults 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. Vibration data from the OH-58C planetary system were collected on a healthy transmission as well as with various seeded-fault components. Planetary fault detection algorithms were used with the collected data to evaluate fault detection effectiveness. Planet gear tooth cracks and spalls were detectable using the vibration separation techniques. Sun gear tooth cracks were not discernibly detectable from the vibration 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 fault detection.

  19. SSME fault monitoring and diagnosis expert system

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Norman, Arnold M.; Gupta, U. K.

    1989-01-01

    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 fault diagnosis 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 fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

  20. Two Similarity Measure Approaches to Whole Building Fault Diagnosis 

    E-print Network

    Lin, G.; Claridge, D.

    2012-01-01

    similarity are defined and the methodology for implementing the proposed whole building fault diagnosis approaches is presented. Cosine similarity and Euclidean distance similarity are applied to two field observed fault test cases, and both the cosine...

  1. Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Liang, Ming

    2014-09-01

    The vibration signals from complex structures such as wind turbine (WT) planetary gearboxes are intricate. Reliable analysis of such signals is the key to success in fault detection and diagnosis for complex structures. The recently proposed iterative atomic decomposition thresholding (IADT) method has shown to be effective in extracting true constituent components of complicated signals and in suppressing background noise interferences. In this study, such properties of the IADT are exploited to analyze and extract the target signal components from complex signals with a focus on WT planetary gearboxes under constant running conditions. Fault diagnosis for WT planetary gearboxes has been a very important yet challenging issue due to their harsh working conditions and complex structures. Planetary gearbox fault diagnosis relies on detecting the presence of gear characteristic frequencies or monitoring their magnitude changes. However, a planetary gearbox vibration signal is a mixture of multiple complex components due to the unique structure, complex kinetics and background noise. As such, the IADT is applied to enhance the gear characteristic frequencies of interest, and thereby diagnose gear faults. Considering the spectral properties of planetary gearbox vibration signals, we propose to use Fourier dictionary in the IADT so as to match the harmonic waves in frequency domain and pinpoint the gear fault characteristic frequency. To reduce computing time and better target at more relevant signal components, we also suggest a criterion to estimate the number of sparse components to be used by the IADT. The performance of the proposed approach in planetary gearbox fault diagnosis has been evaluated through analyzing the numerically simulated, lab experimental and on-site collected signals. The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.

  2. Rule-based fault diagnosis of hall sensors and fault-tolerant control of PMSM

    NASA Astrophysics Data System (ADS)

    Song, Ziyou; Li, Jianqiu; Ouyang, Minggao; Gu, Jing; Feng, Xuning; Lu, Dongbin

    2013-07-01

    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 faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-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 fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-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 fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

  3. Quantitative diagnosis of fault severity trend of rolling element bearings

    NASA Astrophysics Data System (ADS)

    Cui, Lingli; Ma, Chunqing; Zhang, Feibin; Wang, Huaqing

    2015-10-01

    The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condition and fault type but also the severity of the fault. This means fault severity quantitative analysis is one of most active and valid ways to realize proper maintenance decision. Aiming at the deficiency of the research in bearing single point pitting fault quantitative diagnosis, a new back-propagation neural network method based on wavelet packet decomposition coefficient entropy is proposed. The three levels of wavelet packet coefficient entropy(WPCE) is introduced as a characteristic input vector to the BPNN. Compared with the wavelet packet decomposition energy ratio input vector, WPCE shows more sensitive in distinguishing from the different fault severity degree of the measured signal. The engineering application results show that the quantitative trend fault diagnosis is realized in the different fault degree of the single point bearing pitting fault. The breakthrough attempt from quantitative to qualitative on the pattern recognition of rolling element bearings fault diagnosis is realized.

  4. ECE 586 Fault Detection in Digital Circuits Lecture 28 Diagnosis

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 28 Diagnosis Professor Jia Wang Department of Electrical and Computer Engineering Illinois Institute of Technology April 27, 2015 ECE 586 ­ Fault Detection review ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/25 #12;Final Exam Final exam: Mon. May

  5. Experimental Evaluation of a Structure-Based Connectionist Network for Fault Diagnosis of Helicopter Gearboxes

    NASA Technical Reports Server (NTRS)

    Jammu, V. B.; Danai, K.; Lewicki, D. G.

    1998-01-01

    This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) fault diagnostic system introduced in the preceding article. For this vibration 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 vibration signals as a substitute to training. To formulate this knowledge, approximate vibration transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component faults on vibration 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 vibration data from the two gearboxes are also used to evaluate the performance of SBCN in fault diagnosis. The diagnostic results indicate that the SBCN is effective in directing the presence of faults 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 vibration features.

  6. A Hybrid Model Based and Statistical Fault Diagnosis System for Industrial Process 

    E-print Network

    Lin, Chen-Han

    2014-11-21

    This thesis presents a hybrid model based and statistical fault diagnosis system, which applied on the nonlinear three-tank model. The purpose of fault diagnosis is generating and analyzing the residual to find out the fault occurrence. This fault...

  7. Intelligent fault diagnosis at the Australia Telescope

    NASA Astrophysics Data System (ADS)

    Landau, Robert

    1994-08-01

    The Fault Diagnosis Expert System for the Australia Telescope analyzes approximately 12000 items of monitor data every minute that report the health and stability of specific components and signal pathways in the array. These data are divided into signatures which are matched against signatures of known failure modes to diagnose problems with the array. Knowledge about many of the failures is acquired by generating them in earlier tests. The system keeps a six-hour history of the detailed behavior of all monitor data as well as the visibilities. It archives the data in half-hour intervals, characterizing the interval with a small set of robust statistical estimators. An interactive graphical user interface allows the simultaneous display of twenty-four histories of either the 6-hour data or one week of the characterized data, together with options for plotting one history against another and for calculating their robust regression.

  8. Multisensor fusion for induction motor aging analysis and fault diagnosis

    NASA Astrophysics Data System (ADS)

    Erbay, Ali Seyfettin

    Induction motors are the most commonly used electrical drives, ranging in power from fractional horsepower to several thousand horsepowers. Several studies have been conducted to identify the cause of failure of induction motors in industrial applications. Recent activities indicate a focus towards building intelligence into the motors, so that a continuous on-line fault diagnosis and prognosis may be performed. The purpose of this research and development was to perform aging studies of three-phase, squirrel-cage induction motors; establish a database of mechanical, electrical and thermal measurements from load testing of the motors; develop a sensor-fusion method for on-line motor diagnosis; and use the accelerated aging models to extrapolate to the normal aging regimes. A new laboratory was established at The University of Tennessee to meet the goals of the project. The accelerated aging and motor performance tests constitute a unique database, containing information about the trend characteristics of measured signatures as a function of motor faults. The various measurements facilitate enhanced fault diagnosis of motors and may be effectively utilized to increase the reliability of decision making and for the development of life prediction techniques. One of these signatures is the use of Multi-Resolution Analysis (MRA) using wavelets. Using MRA in trending different frequency bands has revealed that higher frequencies show a characteristic increase when the condition of a bearing is in question. This study effectively showed that the use of MRA in vibration signatures can identify a thermal degradation or degradation via electrical charge of the bearing, whereas other failure mechanisms, such as winding insulation failure, do not exhibit such characteristics. A motor diagnostic system, called the Intelligent Motor Monitoring System (IMMS) was developed in this research. The IMMS integrated the various mechanical, electrical and thermal signatures, and artificial neural networks and fuzzy logic algorithms. The IMMS was then used for motor fault detection and isolation and for estimating its remaining operable lifetime. The performance of the IMMS was evaluated using the motor aging data, and showed that several motor degradation modes could be effectively diagnosed and the prognosis of motor operation could be established.

  9. Understanding Vibration Spectra of Planetary Gear Systems for Fault Detection

    NASA Technical Reports Server (NTRS)

    Mosher, Marianne

    2003-01-01

    An understanding of the vibration spectra is very useful for any gear fault detection scheme based upon vibration measurements. The vibration measured from planetary gears is complicated. Sternfeld noted the presence of sidebands about the gear mesh harmonics spaced at the planet passage frequency in spectra measured near the ring gear of a CH-47 helicopter. McFadden proposes a simple model of the vibration transmission that predicts high spectral amplitudes at multiples of the planet passage frequency, for planetary gears with evenly spaced planets. This model correctly predicts no strong signal at the meshing frequency when the number of teeth on the ring gear is not an integer multiple of the number of planets. This paper will describe a model for planetary gear vibration spectra developed from the ideas started in reference. This model predicts vibration to occur only at frequencies that are multiples of the planet repetition passage frequency and clustered around gear mesh harmonics. Vibration measurements will be shown from tri-axial accelerometers mounted on three different planetary gear systems and compared with the model. The model correctly predicts the frequencies with large components around the first several gear mesh harmonics in measurements for systems with uniformly and nonuniformly spaced planet gears. Measurements do not confirm some of the more detailed features predicted by the model. Discrepancies of the ideal model to the measurements are believed due to simplifications in the model and will be discussed. Fault detection will be discussed applying the understanding will be discussed.

  10. Expert systems for real-time monitoring and fault diagnosis

    NASA Technical Reports Server (NTRS)

    Edwards, S. J.; Caglayan, A. K.

    1989-01-01

    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 fault diagnosis 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 fault diagnosis systems, and their applications to new real-time hardware fault diagnosis and monitoring systems for aircraft.

  11. Adaptive PCA based fault diagnosis scheme in imperial smelting process.

    PubMed

    Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin

    2014-09-01

    In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. PMID:24439836

  12. Vibration Signature Analysis of a Faulted Gear Transmission System

    NASA Technical Reports Server (NTRS)

    Choy, F. K.; Huang, S.; Zakrajsek, J. J.; Handschuh, R. F.; Townsend, D. P.

    1996-01-01

    A comprehensive procedure in predicting faults in gear transmission systems under normal operating conditions is presented. Experimental data were obtained from a spiral bevel gear fatigue test rig at NASA/Lewis. Time-synchronous-averaged vibration data were recorded throughout the test as the fault progressed from a small single pit to severe pitting over several teeth, and finally tooth fracture. A numerical procedure based on the Wigner-Ville distribution was used to examine the time-averaged vibration data. Results from the Wigner-Ville procedure are compared to results from a variety of signal analysis techniques that 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 faults.

  13. Vibration Signature Analysis of a Faulted Gear Transmission System

    NASA Technical Reports Server (NTRS)

    Choy, F. K.; Huang, S.; Zakrajsek, J. J.; Handschuh, R. F.; Townsend, D. P.

    1994-01-01

    A comprehensive procedure in predicting faults 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 vibration data was recorded throughout the test as the fault 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 vibration 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 faults.

  14. Solar Dynamic Power System Fault Diagnosis

    NASA Technical Reports Server (NTRS)

    Momoh, James A.; Dias, Lakshman G.

    1996-01-01

    The objective of this research is to conduct various fault simulation studies for diagnosing the type and location of faults in the power distribution system. Different types of faults are simulated at different locations within the distribution system and the faulted waveforms are monitored at measurable nodes such as at the output of the DDCU's. These fault signatures are processed using feature extractors such as FFT and wavelet transforms. The extracted features are fed to a clustering based neural network for training and subsequent testing using previously unseen data. Different load models consisting of constant impedance and constant power are used for the loads. Open circuit faults and short circuit faults are studied. It is concluded from present studies that using features extracted from wavelet transforms give better success rates during ANN testing. The trained ANN's are capable of diagnosing fault types and approximate locations in the solar dynamic power distribution system.

  15. A novel KFCM based fault diagnosis method for unknown faults in satellite reaction wheels.

    PubMed

    Hu, Di; Sarosh, Ali; Dong, Yun-Feng

    2012-03-01

    Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. 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 fault diagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault 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-fault' classes while the low similarity points are labeled as 'unknown-faults'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%. PMID:22035775

  16. Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Cui, Lingli; Wang, Jing; Lee, Seungchul

    2014-05-01

    The sparse decomposition based on matching pursuit is an adaptive sparse expression of the signals. An adaptive matching pursuit algorithm that uses an impulse dictionary is introduced in this article for rolling bearing vibration signal processing and fault diagnosis. First, a new dictionary model is established according to the characteristics and mechanism of rolling bearing faults. The new model incorporates the rotational speed of the bearing, the dimensions of the bearing and the bearing fault status, among other parameters. The model can simulate the impulse experienced by the bearing at different bearing fault levels. A simulation experiment suggests that a new impulse dictionary used in a matching pursuit algorithm combined with a genetic algorithm has a more accurate effect on bearing fault diagnosis than using a traditional impulse dictionary. However, those two methods have some weak points, namely, poor stability, rapidity and controllability. Each key parameter in the dictionary model and its influence on the analysis results are systematically studied, and the impulse location is determined as the primary model parameter. The adaptive impulse dictionary is established by changing characteristic parameters progressively. The dictionary built by this method has a lower redundancy and a higher relevance between each dictionary atom and the analyzed vibration signal. The matching pursuit algorithm of an adaptive impulse dictionary is adopted to analyze the simulated signals. The results indicate that the characteristic fault components could be accurately extracted from the noisy simulation fault signals by this algorithm, and the result exhibited a higher efficiency in addition to an improved stability, rapidity and controllability when compared with a matching pursuit approach that was based on a genetic algorithm. We experimentally analyze the early-stage fault signals and composite fault signals of the bearing. The results further demonstrate the effectiveness and superiority of the matching pursuit algorithm that uses the adaptive impulse dictionary. Finally, this algorithm is applied to the analysis of engineering data, and good results are achieved.

  17. CAPRI: A Common Architecture for Distributed Probabilistic Internet Fault Diagnosis

    E-print Network

    Lee, George J.

    2007-06-04

    This thesis presents a new approach to root cause localization and fault diagnosis in the Internet based on a Common Architecture for Probabilistic Reasoning in the Internet (CAPRI) in which distributed, heterogeneous ...

  18. CAPRI : a common architecture for distributed probabilistic Internet fault diagnosis

    E-print Network

    Lee, George J. (George Janbing), 1979-

    2007-01-01

    This thesis presents a new approach to root cause localization and fault diagnosis in the Internet based on a Common Architecture for Probabilistic Reasoning in the Internet (CAPRI) in which distributed, heterogeneous ...

  19. Robust model-based fault diagnosis for chemical process systems 

    E-print Network

    Rajaraman, Srinivasan

    2006-08-16

    Fault detection and diagnosis have gained central importance in the chemical process industries over the past decade. This is due to several reasons, one of them being that copious amount of data is available from a large number of sensors...

  20. Intelligent fault isolation and diagnosis for communication satellite systems

    NASA Technical Reports Server (NTRS)

    Tallo, Donald P.; Durkin, John; Petrik, Edward J.

    1992-01-01

    Discussed here is a prototype diagnosis expert system to provide the Advanced Communication Technology Satellite (ACTS) System with autonomous diagnosis capability. The system, the Fault Isolation and Diagnosis EXpert (FIDEX) system, is a frame-based system that uses hierarchical structures to represent such items as the satellite's subsystems, components, sensors, and fault 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 diagnosis studies.

  1. Real-time fault diagnosis for propulsion systems

    NASA Technical Reports Server (NTRS)

    Merrill, Walter C.; Guo, Ten-Huei; Delaat, John C.; Duyar, Ahmet

    1991-01-01

    Current research toward real time fault diagnosis for propulsion systems at NASA-Lewis is described. The research is being applied to both air breathing and rocket propulsion systems. Topics include fault detection methods including neural networks, system modeling, and real time implementations.

  2. Fault detection and diagnosis capabilities of test sequence selection

    E-print Network

    Thulsiraman, Krishnaiyan

    Review Fault detection and diagnosis capabilities of test sequence selection methods based on the FSM model T Ramalingam*, Anindya Dast and K ThuIasiraman* Different test sequence selection methods resolution in diagnosing the fault. The test sequence selection methods are then compared based on the length

  3. Fault Detection and Diagnosis Method for VAV Terminal Units 

    E-print Network

    Miyata, M.; Yoshida, H.; Asada, M.; Wang, F.; Hashiguchi, S.

    2004-01-01

    This paper proposes two fault detection and diagnosis methods for VAV units without a sensor of supply air volume, and the results of applying these methods to a real building are presented. One method detects faults by applying a statistical method...

  4. Diagnosis of single faults in quantum circuits

    E-print Network

    Debajyoti Bera; Subhamoy Maitra; Sparsa Roychowdhury; Susanta Chakraborty

    2015-12-16

    Detecting and isolating faults is crucial for synthesis of quantum circuits. Under the single fault assumption that is now routinely accepted in circuit fault analysis, we show that the behaviour of faulty quantum circuits can be fully characterized by the single faulty gate and the corresponding fault model. This allows us to efficiently determine test input states as well as measurement strategy that can be used to detect every single-gate fault using very few test cases and with minimal probability of error; in fact we demonstrate that most commonly used quantum gates can be isolated without any error under the single missing gate fault (SMGF) model. We crucially exploit the quantum nature of circuits to show vast improvement upon the existing works of automatic test pattern generation (ATPG) for quantum circuits.

  5. Modeling Fault Propagation in Telecommunications Networks for Diagnosis Purposes

    E-print Network

    Pencolé, Yannick

    Modeling Fault Propagation in Telecommunications Networks for Diagnosis Purposes A. Aghasaryan , C@lipn.univ-paris13.fr Abstract: This paper describes a formalism to model the behavior of telecommunications net. Keywords: network management, modeling, UML, distributed diagnosis. 1 Introduction Telecommunications

  6. Composite Bending Box Section Modal Vibration Fault Detection

    NASA Technical Reports Server (NTRS)

    Werlink, Rudy

    2002-01-01

    One of the primary concerns with Composite construction in critical structures such as wings and stabilizers is that hidden faults and cracks can develop operationally. In the real world, catastrophic sudden failure can result from these undetected faults in composite structures. Vibration 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 vibration 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 vibration fault detection sensitivity to band-width, location and axis will be investigated. Do the sensor accelerometers need to be near the fault 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 vibrational 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.

  7. SVD principle analysis and fault diagnosis for bearings based on the correlation coefficient

    NASA Astrophysics Data System (ADS)

    Qiao, Zijian; Pan, Zhengrong

    2015-08-01

    Aiming at solving the existing sharp problems by using singular value decomposition (SVD) in the fault diagnosis of rolling bearings, such as the determination of the delay step k for creating the Hankel matrix and selection of effective singular values, the present study proposes a novel adaptive SVD method for fault feature detection based on the correlation coefficient by analyzing the principles of the SVD method. This proposed method achieves not only the optimal determination of the delay step k by means of the absolute value {{r}k} of the autocorrelation function sequence of the collected vibration signal, but also the adaptive selection of effective singular values using the index ? corresponding to useful component signals including weak fault information to detect weak fault signals for rolling bearings, especially weak impulse signals. The effectiveness of this method has been verified by contrastive results between the proposed method and traditional SVD, even using the wavelet-based method through simulated experiments. Finally, the proposed method has been applied to fault diagnosis for a deep-groove ball bearing in which a single point fault located on either the inner or outer race of rolling bearings is obtained successfully. Therefore, it can be stated that the proposed method is of great practical value in engineering applications.

  8. Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification

    NASA Astrophysics Data System (ADS)

    Wang, Tianyang; Liang, Ming; Li, Jianyong; Cheng, Weidong; Li, Chuan

    2015-10-01

    The interfering vibration signals of a gearbox often represent a challenging issue in rolling bearing fault detection and diagnosis, 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) fault characteristic order (FCO) based fault detection, and (c) rotational-order-sideband (ROS) based fault 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 fault type diagnosis 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 fault 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.

  9. A theoretical model for fault diagnosis of localized bearing defects under non-weight-dominant conditions

    NASA Astrophysics Data System (ADS)

    Han, Q. K.; Chu, F. L.

    2015-07-01

    Fault diagnosis of localized bearing defects under non-weight-dominant conditions is studied in this paper. A theoretical model with eight degrees of freedom is established, considering two transverse vibrations of the rotor and bearing raceway and one high-frequency resonant degree of freedom. Both the Hertzian contact between rolling elements and raceways, bearing clearance, unbalance force and self-weight of rotor are taken into account in the model. The localized defects in both inner and outer raceways are modeled as half sinusoidal waves. Then, the theoretical model is solved numerically and the vibrational responses are obtained. Through envelope analysis, the fault characteristic frequencies of inner/outer raceway defects for various conditions, including the weight-dominant condition and non-weight-dominant condition, are presented and compared with each other.

  10. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

    NASA Astrophysics Data System (ADS)

    Wang, Yanxue; Xiang, Jiawei; Markert, Richard; Liang, Ming

    2016-01-01

    Condition-based maintenance via vibration signal processing plays an important role to reduce unscheduled machine downtime and avoid catastrophic accidents in industrial enterprises. Many machine faults, such as local defects in rotating machines, manifest themselves in the acquired vibration signals as a series of impulsive events. The spectral kurtosis (SK) technique extends the concept of kurtosis to that of a function of frequency that indicates how the impulsiveness of a signal. This work intends to review and summarize the recent research developments on the SK theories, for instance, short-time Fourier transform-based SK, kurtogram, adaptive SK and protrugram, as well as the corresponding applications in fault detection and diagnosis of the rotating machines. The potential prospects of prognostics using SK technique are also designated. Some examples have been presented to illustrate their performances. The expectation is that further research and applications of the SK technique will flourish in the future, especially in the fields of the prognostics.

  11. Fault Diagnosis of Demountable Disk-Drum Aero-Engine Rotor Using Customized Multiwavelet Method

    PubMed Central

    Chen, Jinglong; Wang, Yu; He, Zhengjia; Wang, Xiaodong

    2015-01-01

    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 fault has led to several unscheduled breakdowns and serious accidents. Thus, condition monitoring and fault diagnosis technique are required for identifying abnormal conditions. Customized ensemble multiwavelet method for aero-engine rotor condition identification, using measured vibration data, is developed in this paper. First, customized multiwavelet basis function with strong adaptivity is constructed via symmetric multiwavelet lifting scheme. Then vibration 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 fault detection of an experimental aero-engine rotor. Finally, the proposed approach is used in an engineering application, where it successfully identified the crack fault of a demountable disk-drum aero-engine rotor. The results show that the proposed method possesses excellent performance in fault 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

  12. Fault Diagnosis of Demountable Disk-Drum Aero-Engine Rotor Using Customized Multiwavelet Method.

    PubMed

    Chen, Jinglong; Wang, Yu; He, Zhengjia; Wang, Xiaodong

    2015-01-01

    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 fault has led to several unscheduled breakdowns and serious accidents. Thus, condition monitoring and fault diagnosis technique are required for identifying abnormal conditions. Customized ensemble multiwavelet method for aero-engine rotor condition identification, using measured vibration data, is developed in this paper. First, customized multiwavelet basis function with strong adaptivity is constructed via symmetric multiwavelet lifting scheme. Then vibration 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 fault detection of an experimental aero-engine rotor. Finally, the proposed approach is used in an engineering application, where it successfully identified the crack fault of a demountable disk-drum aero-engine rotor. The results show that the proposed method possesses excellent performance in fault 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

  13. Fault diagnosis viewed as a left invertibility problem.

    PubMed

    Martínez-Guerra, R; Mata-Machuca, J L; Rincón-Pasaye, J J

    2013-09-01

    This work deals with the fault diagnosis problem, some new properties are found using the left invertibility condition through the concept of differential output rank. Two schemes of nonlinear observers are used to estimate the fault signals for comparison purposes, one of these is a proportional reduced order observer and the other is a sliding mode observer. The methodology is tested in a real time implementation of a three-tank system. PMID:23838257

  14. Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery

    NASA Astrophysics Data System (ADS)

    Lu, Na; Xiao, Zhihuai; Malik, O. P.

    2015-02-01

    State identification to diagnose the condition of rotating machinery is often converted to a classification problem of values of non-dimensional symptom parameters (NSPs). To improve the sensitivity of the NSPs to the changes in machine condition, a novel feature extraction method based on adaptive multiwavelets and the synthetic detection index (SDI) is proposed in this paper. Based on the SDI maximization principle, optimal multiwavelets are searched by genetic algorithms (GAs) from an adaptive multiwavelets library and used for extracting fault features from vibration signals. By the optimal multiwavelets, more sensitive NSPs can be extracted. To examine the effectiveness of the optimal multiwavelets, conventional methods are used for comparison study. The obtained NSPs are fed into K-means classifier to diagnose rotor faults. The results show that the proposed method can effectively improve the sensitivity of the NSPs and achieve a higher discrimination rate for rotor fault diagnosis than the conventional methods.

  15. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.

    PubMed

    Sobhani-Tehrani, E; Talebi, H A; Khorasani, K

    2014-02-01

    This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements. PMID:24239987

  16. Display interface concepts for automated fault diagnosis

    NASA Technical Reports Server (NTRS)

    Palmer, Michael T.

    1989-01-01

    An effort which investigated concepts for displaying dynamic system status and fault 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 fault 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.

  17. Inverse Gaussian mixtures models of bearing vibrations under local faults

    NASA Astrophysics Data System (ADS)

    Boškoski, Pavle; Juri?i?, ?ani

    2016-01-01

    Repetitive impacts performed by damaged spot on a component of the rolling element bearing specific statistical properties, due to the constant angular distance between the roller elements. Under (almost) constant rotational speed the successive impacts are regarded as almost periodic with small random fluctuations due to slippage. Often these random components are modelled as normally distributed, which is unrealistic since physically impossible events, such as negative time between two consecutive impacts, become likely by the nature of the distribution. Motivated by this deficiency we propose a new model that describes the occurrence of repetitive vibrational patterns as realisation of a point process with the (mixture) inverse Gaussian distribution of the inter-event times. Such a model is applicable to both constant and variable rotational speeds. Additionally, the proposed model inherently describes the quasi-cyclostationarity of the impact times under almost constant rotational speed. The applicability of the model was evaluated using vibrational signals generated by bearings with localised surface fault.

  18. Hierarchical Diagnosis of Multiple Faults Sajjad Siddiqi and Jinbo Huang

    E-print Network

    Huang, Jinbo

    of the health variables that is consistent with the observation and system model. For instance, givenHierarchical Diagnosis of Multiple Faults Sajjad Siddiqi and Jinbo Huang National ICT Australia and Australian National University Canberra, ACT 0200 Australia {sajjad.siddiqi, jinbo

  19. Advanced fault diagnosis techniques and their role in preventing cascading blackouts 

    E-print Network

    Zhang, Nan

    2007-04-25

    This dissertation studied new transmission line fault diagnosis approaches using new technologies and proposed a scheme to apply those techniques in preventing and mitigating cascading blackouts. The new fault diagnosis approaches are based on two...

  20. Multiple Fault Diagnosis in Complex Physical Systems Matthew Daigle, Xenofon Koutsoukos, and Gautam Biswas

    E-print Network

    Koutsoukos, Xenofon D.

    Multiple Fault Diagnosis in Complex Physical Systems Matthew Daigle, Xenofon Koutsoukos, and Gautam.j.daigle,xenofon.koutsoukos,gautam.biswas}@vanderbilt.edu Abstract Multiple fault diagnosis is a challenging problem because the number of candidates grows exponen- tially in the number of faults. In addition, multiple faults in dynamic systems may be hard to detect

  1. Fault Diagnosis System for a Multilevel Inverter Using a Neural Network Surin Khomfoi Leon M. Tolbert

    E-print Network

    Tolbert, Leon M.

    Fault Diagnosis System for a Multilevel Inverter Using a Neural Network Surin Khomfoi Leon M, TN 37996-2100, USA surin@utk.edu tolbert@utk.edu Abstract ­ In this paper, a fault diagnosis system to the fault diagnosis of a MLID system. Five multilayer perceptron (MLP) networks are used to identify

  2. Fault Diagnosis System for a Multilevel Inverter Using a Principal Component Neural Network Surin Khomfoi

    E-print Network

    Tolbert, Leon M.

    Fault Diagnosis System for a Multilevel Inverter Using a Principal Component Neural Network Surin Engineering 414 Ferris Hall, Knoxville, TN 37996-2100, USA Email: tolbert@utk.edu Abstract-- A fault diagnosis classification is applied to the fault diagnosis of a MLID system. Multilayer perceptron (MLP) networks are used

  3. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery

    PubMed Central

    Wang, Huaqing; Chen, Peng

    2009-01-01

    This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to. PMID:22574021

  4. Fault Diagnosis of Power Systems Using Intelligent Systems

    NASA Technical Reports Server (NTRS)

    Momoh, James A.; Oliver, Walter E. , Jr.

    1996-01-01

    The power system operator's need for a reliable power delivery system calls for a real-time or near-real-time Al-based fault diagnosis 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 faulted branches and loads of the system. And after isolation, re-energizing those branches and loads that have been found not to have any faults in them. A proposed solution involves using the Fault Diagnosis Intelligent System (FDIS) to perform near-real time fault diagnosis of Alpha's EPS by downloading power transient telemetry at fault-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 fault 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 fault 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 from the rest of the system. The benefit of these studies provides NASA with the ability to quickly restore the operating status of a space station from a critical state to a safe degraded mode, thereby saving costs in experimentation rescheduling, fault diagnostics, and prevention of loss-of-life.

  5. Fault diagnosis in orbital refueling operations

    NASA Technical Reports Server (NTRS)

    Boy, Guy A.

    1988-01-01

    Usually, operation manuals are provided for helping astronauts during space operations. These manuals include normal and malfunction procedures. Transferring operation manual knowledge into a computerized form is not a trivial task. This knowledge is generally written by designers or operation engineers and is often quite different from the user logic. The latter is usually a compiled version of the former. Experiments are in progress to assess the user logic. HORSES (Human - Orbital Refueling System - Expert System) is an attempt to include both of these logics in the same tool. It is designed to assist astronauts during monitoring and diagnosis tasks. Basically, HORSES includes a situation recognition level coupled to an analytical diagnoser, and a meta-level working on both of the previous levels. HORSES is a good tool for modeling task models and is also more broadly useful for knowledge design. The presentation is represented by abstract and overhead visuals only.

  6. Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix

    NASA Astrophysics Data System (ADS)

    Zhao, Minghang; Tang, Baoping; Tan, Qian

    2015-08-01

    Time-frequency analysis is an effective tool to extract machinery health information contained in non-stationary vibration signals. Various time-frequency analysis methods have been proposed and successfully applied to machinery fault diagnosis. However, little research has been done on bearing fault diagnosis using texture features extracted from time-frequency representations (TFRs), although they may contain plenty of sensitive information highly related to fault pattern. Therefore, to make full use of the textural information contained in the TFRs, this paper proposes a novel fault diagnosis method based on S transform, gray level co-occurrence matrix (GLCM) and multi-class support vector machine (Multi-SVM). Firstly, S transform is chosen to generate the TFRs due to its advantages of providing frequency-dependent resolution while keeping a direct relationship with the Fourier spectrum. Secondly, the famous GLCM-based texture features are extracted for capturing fault pattern information. Finally, as a classifier which has good discrimination and generalization abilities, Multi-SVM is used for the classification. Experimental results indicate that the GLCM-based texture features extracted from TFRs can identify bearing fault patterns accurately, and provide higher accuracies than the traditional time-domain and frequency-domain features, wavelet packet node energy or two-direction 2D linear discriminant analysis based features of the same TFRs in most cases.

  7. Sequential Fuzzy Diagnosis Method for Motor Roller Bearing in Variable Operating Conditions Based on Vibration Analysis

    PubMed Central

    Li, Ke; Ping, Xueliang; Wang, Huaqing; Chen, Peng; Cao, Yi

    2013-01-01

    A novel intelligent fault diagnosis 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 vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration 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 diagnosis 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 diagnosis. Finally, a fuzzy diagnosis 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

  8. LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information

    PubMed Central

    Liu, Zhiwen; Chen, Xuefeng; He, Zhengjia; Shen, Zhongjie

    2013-01-01

    Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM. PMID:23881133

  9. Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Lu, Lei; Yan, Jihong; de Silva, Clarence W.

    2015-05-01

    This paper develops a novel dominant feature selection method using a genetic algorithm with a dynamic searching strategy. It is applied in the search for the most representative features in rotary mechanical fault diagnosis, and is shown to improve the classification performance with fewer features. First, empirical mode decomposition (EMD) is employed to decompose a vibration signal into intrinsic mode functions (IMFs) which represent the signal characteristic with sample oscillatory modes. Then, a modified genetic algorithm with variable-range encoding and dynamic searching strategy is used to establish relationships between optimized feature subsets and the classification performance. Next, a statistical model that uses receiver operating characteristic (ROC) is developed to select dominant features. Finally, support vector machine (SVM) is used to classify different fault patterns. Two real-world problems, rotor-unbalance vibration and bearing corrosion, are employed to evaluate the proposed feature selection scheme and fault diagnosis system. Statistical results obtained by analyzing the two problems, and comparative studies with five well-known feature selection techniques, demonstrate that the method developed in this paper can achieve improvements in identification accuracy with lower feature dimensionality. In addition, the results indicate that the proposed method is a promising tool to select dominant features in rotary machinery fault diagnosis.

  10. Development of an Automated Fault Detection and Diagnosis tool for AHU's 

    E-print Network

    Bruton, K.; Raftery, P.; Aughney, N.; Keane, M.; O'Sullivan, D.

    2012-01-01

    -commissioning HVAC systems to rectify faulty operation with savings of over 20 percent of total energy cost possible by continuously commissioning. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with automating the detection of faults...

  11. Hypothetical Scenario Generator for Fault-Tolerant Diagnosis

    NASA Technical Reports Server (NTRS)

    James, Mark

    2007-01-01

    The Hypothetical Scenario Generator for Fault-tolerant Diagnostics (HSG) is an algorithm being developed in conjunction with other components of artificial- intelligence systems for automated diagnosis and prognosis of faults 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.

  12. Gas Turbine Fault Diagnosis Using Probabilistic Neural Networks

    NASA Astrophysics Data System (ADS)

    Loboda, Igor; Olivares Robles, Miguel Angel

    2015-05-01

    Efficiency of gas turbine monitoring systems primarily depends on the accuracy of employed algorithms, in particular, pattern classification techniques for diagnosing gas path faults. In recent investigations many techniques have been applied to classify gas path faults, but recommendations for selecting the best technique for real monitoring systems are still insufficient and often contradictory. In our previous work, three classification techniques were compared under different conditions of gas turbine diagnosis. The comparative analysis has shown that all these techniques yield practically the same accuracy for each comparison case. The present contribution considers a new classification technique, Probabilistic Neural Network (PNN), and we compare it with the techniques previously examined. The results for all comparison cases show that the PNN is not inferior to the other techniques. We recommend choosing the PNN for real monitoring systems because it has an important advantage of providing confidence estimation for every diagnostic decision made.

  13. A fault diagnosis approach for diesel engine valve train based on improved ITD and SDAG-RVM

    NASA Astrophysics Data System (ADS)

    Yu, Liu; Junhong, Zhang; Fengrong, Bi; Jiewei, Lin; Wenpeng, Ma

    2015-02-01

    Targeting the non-stationary characteristics of the vibration signals of a diesel engine valve train, and the limitation of the autoregressive (AR) model, a novel approach based on the improved intrinsic time-scale decomposition (ITD) and relevance vector machine (RVM) is proposed in this paper for the identification of diesel engine valve train faults. The approach mainly consists of three stages: First, prior to the feature extraction, non-uniform B-spline interpolation is introduced to the ITD method for the fitting of baseline signal, then the improved ITD is used to decompose the non-stationary signals into a set of stationary proper rotation components (PRCs). Second, the AR model is established for each PRC, and the first several AR coefficients together with the remnant variance of all PRCs are regarded as the fault feature vectors. Finally, a new separability based directed acyclic graph (SDAG) method is proposed to determine the structure of multi-class RVM, and the fault feature vectors are classified using the SDAG-RVM classifier to recognize the fault of the diesel engine valve train. The experimental results demonstrate that the proposed fault diagnosis approach can effectively extract the fault features and accurately identify the fault patterns.

  14. A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy

    NASA Astrophysics Data System (ADS)

    Li, Yongbo; Xu, Minqiang; Wang, Rixin; Huang, Wenhu

    2016-01-01

    This paper presents a new rolling bearing fault diagnosis 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 fault occurs in rolling bearings, the measured vibration 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 vibration 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 fault features by sorting the scale factors. Subsequently, the obtained features are fed into the multi-fault classifier ISVM-BT to automatically fulfill the fault 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.

  15. Joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Zuo, Ming J.; Qu, Jian; Tian, Tao; Liu, Zhiliang

    2013-10-01

    The vibration signals of faulty planetary gearboxes have complicated spectral structures due to the amplitude modulation and frequency modulation (AMFM) nature of gear damage induced vibration and the additional multiplicative amplitude modulation (AM) effect caused by the time-varying vibration transfer paths (for local gear damage case) and the passing planets (for distributed gear damage case). The spectral complexity leads to the difficulty in fault diagnosis of planetary gearboxes. Observing that both the amplitude envelope and the instantaneous frequency of planetary gearbox vibration signals are associated with the characteristic frequency of the faulty gear, a joint amplitude and frequency demodulation method is proposed for fault diagnosis of planetary gearboxes. In order to satisfy the mono-component requirement by instantaneous frequency estimation, a signal is firstly decomposed into product functions (PF) using the local mean decomposition (LMD) method. Then, the earliest extracted PF that has an instantaneous frequency fluctuating around the gear meshing frequency or its harmonics is chosen for further analysis, because it contains most of the information about the gear fault. The amplitude demodulation analysis can be accomplished through Fourier transforming the amplitude envelope of the chosen PF. For the frequency demodulation analysis, Fourier transform is applied to the estimated instantaneous frequency of the chosen PF to reveal its fluctuating frequency, thus obtaining the spectrum of the instantaneous frequency. By joint application of the amplitude and frequency demodulation methods, planetary gearbox faults can be diagnosed by matching the dominant peaks in the envelope spectrum and the spectrum of instantaneous frequency with the theoretical characteristic frequencies of faulty gears. The performance of the proposed method is illustrated by simulated signal analysis, and is validated by experimental signal analysis of a lab planetary gearbox with intentionally created pitting and naturally developed wear.

  16. SOM Neural Network Fault Diagnosis Method of Polymerization Kettle Equipment Optimized by Improved PSO Algorithm

    PubMed Central

    Wang, Jie-sheng; Li, Shu-xia; Gao, Jie

    2014-01-01

    For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault 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 fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis 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 fault diagnosis strategy is effective. PMID:25152929

  17. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Li, Zipeng; Pan, Jun; Chen, Gaige; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia

    2016-03-01

    As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault 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.

  18. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    PubMed Central

    Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault 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 faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

  19. A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

    2015-08-01

    Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

  20. A distributed fault-detection and diagnosis system using on-line parameter estimation

    NASA Technical Reports Server (NTRS)

    Guo, T.-H.; Merrill, W.; Duyar, A.

    1991-01-01

    The development of a model-based fault-detection and diagnosis system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the faults 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 diagnosis models which include process faults is presented. There are three distinct classes of fault modes covered by the system performance model equation: actuator faults, sensor faults, and performance degradation. A system equation for a complete model that describes all three classes of faults is given. The strategy for detecting the fault and estimating the fault 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 faults. The second step is the fault diagnosis module which checks all the information obtained from the HTM level, isolates the fault, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor faults added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the faults and estimate their magnitudes.

  1. A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform.

    PubMed

    Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li

    2015-01-01

    Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration 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 vibration 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 fault diagnosis 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 fault 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, fault 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 fault classification accuracy and a good performance in Gauss white noise reduction. PMID:26540059

  2. Fault Diagnosis in Mixed-Signal Low Testability System Jing Pang Janusz A. Starzyk

    E-print Network

    Starzyk, Janusz A.

    -0007 jingpang@bobcat.ent.ohiou.edu starzyk@bobcat.ent.ohiou.edu KEY WORDS: ambiguity groups, fault diagnosis University Athens, OH 45701, U. S. A. Tel.(740) 593-1580 Fax (740) 593-0007 jingpang@bobcat.ent.ohiou.edu starzyk@bobcat.ent.ohiou.edu ABSTRACT This paper describes a new approach for fault diagnosis of analog

  3. Algorithms for Green Buildings: Learning-Based Techniques for Energy Prediction and Fault Diagnosis

    E-print Network

    Seshia, Sanjit A.

    Algorithms for Green Buildings: Learning-Based Techniques for Energy Prediction and Fault Diagnosis Buildings: Learning-Based Techniques for Energy Prediction and Fault Diagnosis Daniel Holcomb Wenchao Li consider two problems in the design and operation of energy-efficient buildings. The first

  4. Knowledge-based fault diagnosis system for refuse collection vehicle

    NASA Astrophysics Data System (ADS)

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-05-01

    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 knowledge 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 fault diagnosis system framework for the refuse collection vehicle.

  5. Knowledge-based fault diagnosis system for refuse collection vehicle

    SciTech Connect

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-05-15

    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 knowledge 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 fault diagnosis system framework for the refuse collection vehicle.

  6. Real-time antenna fault diagnosis experiments at DSS 13

    NASA Technical Reports Server (NTRS)

    Mellstrom, J.; Pierson, C.; Smyth, P.

    1992-01-01

    Experimental results obtained when a previously described fault diagnosis 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.

  7. Polymer electrolyte membrane fuel cell fault diagnosis based on empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Damour, Cédric; Benne, Michel; Grondin-Perez, Brigitte; Bessafi, Miloud; Hissel, Daniel; Chabriat, Jean-Pierre

    2015-12-01

    Diagnosis tool for water management is relevant to improve the reliability and lifetime of polymer electrolyte membrane fuel cells (PEMFCs). This paper presents a novel signal-based diagnosis approach, based on Empirical Mode Decomposition (EMD), dedicated to PEMFCs. EMD is an empirical, intuitive, direct and adaptive signal processing method, without pre-determined basis functions. The proposed diagnosis approach relies on the decomposition of FC output voltage to detect and isolate flooding and drying faults. The low computational cost of EMD, the reduced number of required measurements, and the high diagnosis accuracy of flooding and drying faults diagnosis make this approach a promising online diagnosis tool for PEMFC degraded modes management.

  8. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

    PubMed

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. PMID:24981891

  9. Maximum margin classification based on flexible convex hulls for fault diagnosis of roller bearings

    NASA Astrophysics Data System (ADS)

    Zeng, Ming; Yang, Yu; Zheng, Jinde; Cheng, Junsheng

    2016-01-01

    A maximum margin classification based on flexible convex hulls (MMC-FCH) is proposed and applied to fault diagnosis of roller bearings. In this method, the class region of each sample set is approximated by a flexible convex hull of its training samples, and then an optimal separating hyper-plane that maximizes the geometric margin between flexible convex hulls is constructed by solving a closest pair of points problem. By using the kernel trick, MMC-FCH can be extended to nonlinear cases. In addition, multi-class classification problems can be processed by constructing binary pairwise classifiers as in support vector machine (SVM). Actually, the classical SVM also can be regarded as a maximum margin classification based on convex hulls (MMC-CH), which approximates each class region with a convex hull. The convex hull is a special case of the flexible convex hull. To train a MMC-FCH classifier, time-domain and frequency-domain statistical parameters are extracted not only from raw vibration signals but also from the resulting intrinsic mode functions (IMFs) by performing empirical mode decomposition (EMD) on the raw signals, and then the distance evaluation technique (DET) is used to select salient features from the whole statistical features. The experiments on bearing datasets show that the proposed method can reliably recognize different bearing faults.

  10. Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

    PubMed Central

    Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

    2015-01-01

    The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration 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 fault 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

  11. Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals.

    PubMed

    Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

    2015-01-01

    The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration 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 fault 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

  12. The Realization of Drilling Fault Diagnosis Based on Hybrid Programming with Matlab and VB

    NASA Astrophysics Data System (ADS)

    Wang, Jiangping; Hu, Yingcai

    This paper presents a method using hybrid programming with Matlab and VB based on ActiveX to design the system of drilling accident prediction and diagnosis. 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 diagnosis system is compiled in VB,and the analysis and fault diagnosis are implemented by neural network tool boxes in Matlab.The system has favorable interactive interface,and the fault example validation shows that the diagnosis result is feasible and can meet the demands of drilling accident prediction and diagnosis.

  13. Airbus Toulouse Flight test data centre. Diagnosis and treatment of noisy vibration flight test data.

    E-print Network

    Dobigeon, Nicolas

    Airbus Toulouse ­ Flight test data centre. Diagnosis and treatment of noisy vibration flight test data. The trainee will work within flight test vibration analysis team.The main missions and activities are the followings: - Use an automatic tool of vibration data quality defaults detection; evaluate this tool

  14. Early Oscillation Detection for DC/DC Converter Fault Diagnosis

    NASA Technical Reports Server (NTRS)

    Wang, Bright L.

    2011-01-01

    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 fault diagnosis. 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.

  15. Sensor fault diagnosis for fast steering mirror system based on Kalman filter

    NASA Astrophysics Data System (ADS)

    Wang, Hongju; Bao, Qiliang; Yang, Haifeng; Tao, Sunjie

    2015-10-01

    In this paper, to improve the reliability of a two-axis fast steering mirror system with minimum hardware consumption, a fault diagnosis method based on Kalman filter was developed. The dynamics model of the two-axis FSM was established firstly, and then the state-space form of the FSM was adopted. A bank of Kalman filters for fault detection was designed based on the state-space form. The effects of the sensor faults on the innovation sequence were investigated, and a decision approach called weighted sum-squared residual (WSSR) was adopted to isolate the sensor faults. Sensor faults could be detected and isolated when the decision statistics changed. Experimental studies on a prototype system show that the faulty sensor can be isolated timely and accurately. Meanwhile, the mathematical model of FSM system was used to design fault diagnosis scheme in the proposed method, thus the consumption of the hardware and space is decreased.

  16. A time domain approach to diagnose gearbox fault based on measured vibration signals

    NASA Astrophysics Data System (ADS)

    Hong, Liu; Dhupia, Jaspreet Singh

    2014-03-01

    Spectral analysis techniques to process vibration measurements have been widely studied to characterize the state of gearboxes. However, in practice, the modulated sidebands resulting from the local gear fault are often difficult to extract accurately from an ambiguous/blurred measured vibration spectrum due to the limited frequency resolution and small fluctuations in the operating speed of the machine that often occurs in an industrial environment. To address this issue, a new time-domain diagnostic algorithm is developed and presented herein for monitoring of gear faults, which shows an improved fault extraction capability from such measured vibration signals. This new time-domain fault detection method combines the fast dynamic time warping (Fast DTW) as well as the correlated kurtosis (CK) techniques to characterize the local gear fault, and identify the corresponding faulty gear and its position. Fast DTW is employed to extract the periodic impulse excitations caused from the faulty gear tooth using an estimated reference signal that has the same frequency as the nominal gear mesh harmonic and is built using vibration characteristics of the gearbox operation under presumed healthy conditions. This technique is beneficial in practical analysis to highlight sideband patterns in situations where data is often contaminated by process/measurement noises and small fluctuations in operating speeds that occur even at otherwise presumed steady-state conditions. The extracted signal is then resampled for subsequent diagnostic analysis using CK technique. CK takes advantages of the periodicity of the geared faults; it is used to identify the position of the local gear fault in the gearbox. Based on simulated gear vibration signals, the Fast DTW and CK based approach is shown to be useful for condition monitoring in both fixed axis as well as epicyclic gearboxes. Finally the effectiveness of the proposed method in fault detection of gears is validated using experimental signals from a planetary gearbox test rig. For fault detection in planetary gear-sets, a window function is introduced to account for the planet motion with respect to the fixed sensor, which is experimentally determined and is later employed for the estimation of reference signal used in Fast DTW algorithm.

  17. An efficient logic fault diagnosis framework based on effect-cause approach 

    E-print Network

    Wu, Lei

    2009-05-15

    Fault diagnosis plays an important role in improving the circuit design process and the manufacturing yield. With the increasing number of gates in modern circuits, determining the source of failure in a defective circuit is becoming more and more...

  18. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

    PubMed Central

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    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 fault diagnosis 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 fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. 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 fault diagnosis results and their confidence values. Eventually, the final fault diagnosis 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 faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

  19. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.

    PubMed

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    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 fault diagnosis 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 fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. 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 fault diagnosis results and their confidence values. Eventually, the final fault diagnosis 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 faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

  20. Time-frequency demodulation analysis based on iterative generalized demodulation for fault diagnosis of planetary gearbox under nonstationary conditions

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Chen, Xiaowang; Liang, Ming; Ma, Fei

    2015-10-01

    The vibration signal of planetary gearboxes exhibits the characteristics of both amplitude modulation (AM) and frequency modulation (FM), and thus has a complex sideband structure. Time-varying speed and/or load will result in time variant characteristic frequency components. Since the modulating frequency is related to the gear fault characteristic frequency, the AM and FM parts each alone contains the information of the gear fault. We propose a time-frequency amplitude and frequency demodulation analysis metbhod to avoid the complex time-variant sideband analysis, and thereby identify the time-variant gear fault characteristic frequency. We enhance the time-frequency analysis via iterative generalized demodulation (IGD). The time-varying amplitude and frequency demodulated spectra have fine time-frequency resolution and are free of cross term interferences. They do not involve complex time-variant sidebands, thus considerably facilitating fault diagnosis of planetary gearboxes under nonstationary conditions. The method is validated using both numerically simulated data and experimental signals.

  1. Fault Diagnosis of Continuous Systems Using Discrete-Event Methods Matthew Daigle, Xenofon Koutsoukos, and Gautam Biswas

    E-print Network

    Daigle, Matthew

    Fault Diagnosis of Continuous Systems Using Discrete-Event Methods Matthew Daigle, Xenofon.j.daigle,xenofon.koutsoukos,gautam.biswas@vanderbilt.edu Abstract-- Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems fault isolation in systems with complex continuous dynamics. This paper presents a novel discrete- event

  2. Fault diagnosis of pneumatic systems with artificial neural network algorithms M. Demetgul a,*, I.N. Tansel b

    E-print Network

    Rucci, Michele

    Fault diagnosis of pneumatic systems with artificial neural network algorithms M. Demetgul a,*, I) Back propagation (Bp) Fault diagnosis Pneumatic Modular production system a b s t r a c t Pneumatic the pneumatic system worked perfectly and had some faults including empty magazine, zero vacuum, inappropriate

  3. Thermal image based fault diagnosis for rotating machinery

    NASA Astrophysics Data System (ADS)

    Janssens, Olivier; Schulz, Raiko; Slavkovikj, Viktor; Stockman, Kurt; Loccufier, Mia; Van de Walle, Rik; Van Hoecke, Sofie

    2015-11-01

    Infrared imaging is crucial for condition monitoring as the thermographic patterns will differ depending on the fault or machine condition. Currently, a limited number of machine faults have been studied using thermal imaging. Therefore, this paper proposes a novel automatic fault detection system using infrared imaging, focussing on bearings of rotating machinery. The set of bearing faults monitored contain faults for which state-of-the-art techniques have no general solutions such as bearing-lubricant starvation. For each fault, several recordings are made using different bearings to ensure generalization of the fault-detection system. The system contains two image-processing pipelines, each with their own respective purposes. The first pipeline focusses on detecting rotor imbalance, regardless of the bearing faults. The second pipeline focusses on the bearing faults, regardless of whether the machine is balanced or not. Within the first pipeline, imbalance is detected by differencing the consecutive image frames which are subsequently summarized by their distribution along the image axes. For the second pipeline, three features are introduced which are the standard deviation of the temperature, the Gini coefficient, and the Moment of Light. The final system is able to distinguish between all eight different conditions with an accuracy of 88.25%.

  4. Sensor Fault Detection and Diagnosis Simulation of a Helicopter Engine in an Intelligent Control Framework

    NASA Technical Reports Server (NTRS)

    Litt, Jonathan; Kurtkaya, Mehmet; Duyar, Ahmet

    1994-01-01

    This paper presents an application of a fault detection and diagnosis scheme for the sensor faults 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 diagnosis 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 faults, 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 fault is detected. The isolation of sensor failures is accomplished through a fault parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The fault parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and diagnosis of sensor faults 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 faults.

  5. Fault Diagnosis for Micro-Gas Turbine Engine Sensors via Wavelet Entropy

    PubMed Central

    Yu, Bing; Liu, Dongdong; Zhang, Tianhong

    2011-01-01

    Sensor fault diagnosis 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 fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis 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 fault diagnosis 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 faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient. PMID:22163734

  6. Design of fault diagnosis system for inertial navigation system based on virtual technology

    NASA Astrophysics Data System (ADS)

    Hu, Baiqing; Wang, Boxiong; Li, An; Zhang, Mingzhao; Qin, Fangjun; Pan, Hua

    2006-11-01

    With regard to the complex structure of the inertial navigation system and the low rate of fault detection with BITE (built-in test equipment), a fault diagnosis system for INS based on virtual technologies (virtual instrument and virtual equipment) is proposed in this paper. The hardware of the system is a PXI computer with highly stable performance and strong extensibility. In addition to the basic functions of digital multimeter, oscilloscope and cymometer, it can also measure the attitude of the ship in real-time, connect and control the measurement instruments with digital interface. The software is designed with the languages of Measurement Studio for VB, JAVA, and CULT3D. Using the extensively applied fault-tree reasoning and fault cases makes fault diagnosis. To suit the system to the diagnosis for various navigation electronic equipments, the modular design concept is adopted for the software programming. Knowledge of the expert system is digitally processed and the parameters of the system's interface and the expert diagnosis knowledge are stored in the database. The application shows that system is stable in operation, easy to use, quick and accurate in fault diagnosis.

  7. Nuclear power plant fault-diagnosis using neural networks with error estimation

    SciTech Connect

    Kim, K.; Bartlett, E.B.

    1994-12-31

    The assurance of the diagnosis obtained from a nuclear power plant (NPP) fault-diagnostic advisor based on artificial neural networks (ANNs) is essential for the practical implementation of the advisor to fault detection and identification. The objectives of this study are to develop an error estimation technique (EET) for diagnosis validation and apply it to the NPP fault-diagnostic advisor. Diagnosis validation is realized by estimating error bounds on the advisor`s diagnoses. The 22 transients obtained from the Duane Arnold Energy Center (DAEC) training simulator are used for this research. The results show that the NPP fault-diagnostic advisor are effective at producing proper diagnoses on which errors are assessed for validation and verification purposes.

  8. A model-based solution for fault diagnosis of thruster faults: application to the rendezvous phase of the mars sample return mission

    NASA Astrophysics Data System (ADS)

    Henry, D.; Bornschlegl, E.; Olive, X.; Charbonnel, C.

    2013-12-01

    This paper addresses the design of model-based fault diagnosis schemes to detect and isolate faults occurring in the orbiter thrusters of the Mars Sample Return (MSR) mission. The proposed fault diagnosis method is based on a H(0) filter with robust poles assignment to detect quickly any kind of thruster faults and a cross-correlation test to isolate them. Simulation results from the MSR "high-fidelity" nonlinear simulator provided by Thales Alenia Space demonstrate that the proposed method is able to diagnose thruster faults with a detection and isolation delay less than 1.1 s.

  9. Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution.

    PubMed

    Jia, Feng; Lei, Yaguo; Shan, Hongkai; Lin, Jing

    2015-01-01

    The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault 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 fault 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 fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults 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 faults of rolling element bearings. PMID:26610501

  10. Non-cooperative Diagnosis of Submarine Cable Faults

    E-print Network

    Chang, Rocky Kow-Chuen

    to the Hengchun earthquake in 2006 [1]. Moreover, a submarine cable fault requires considerable time for tracing communication is to reroute the affected traffic to other submarine/terrestrial/satellite links. However

  11. Multiwavelet transform and its applications in mechanical fault diagnosis - A review

    NASA Astrophysics Data System (ADS)

    Sun, Hailiang; He, Zhengjia; Zi, Yanyang; Yuan, Jing; Wang, Xiaodong; Chen, Jinglong; He, Shuilong

    2014-02-01

    Mechanical fault diagnosis is important to reduce unscheduled machine downtime and avoid catastrophic accidents. It is significant to extract incipient fault and compound fault features as early as possible, which is a complex and challenging task that requests advanced analytical methods with high reliability, high accuracy and high efficiency. Compound fault features are mutually coupled in dynamic signals from the complex system. Weak features of incipient faults are always submersed in background noises. Multiwavelet transform is a remarkable development of wavelet transform, which uses vector scaling functions and wavelet functions. Multiwavelets possess the property of orthogonality, symmetry, compact support and high vanishing moments simultaneously. These advantages promote the development of multiwavelets and their applications in mechanical fault diagnosis in the past decades. This paper attempts to summarize the recent development of multiwavelet transform and its applications in mechanical fault diagnosis. First, the history of wavelets and multiwavelets is introduced. Second, the necessity and the overview of preprocessing methods for multiwavelets are summarized. Third, the advantages of multiwavelets and improvements of different generation multiwavelets are addressed. Fourth, different algorithms of these multiwavelet transforms and their flow charts are presented. Fifth, engineering applications of multiwavelets in mechanical fault diagnosis are investigated. This review also describes a simulation experiment and three application examples which provide a better understanding of different generation multiwavelets for compound fault detection. Finally, existent problems and prospects of further researches are discussed. It is expected that this review will construct an image of the contributions of different generation multiwavelets and link the current frontiers with engineering applications for readers interested in this field.

  12. Neural networks and fault probability evaluation for diagnosis issues.

    PubMed

    Kourd, Yahia; Lefebvre, Dimitri; Guersi, Noureddine

    2014-01-01

    This paper presents a new FDI technique for fault 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 fault-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 fault among a set of candidate faults. 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 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method. PMID:25132845

  13. Neural Networks and Fault Probability Evaluation for Diagnosis Issues

    PubMed Central

    Lefebvre, Dimitri; Guersi, Noureddine

    2014-01-01

    This paper presents a new FDI technique for fault 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 fault-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 fault among a set of candidate faults. 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 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method. PMID:25132845

  14. Model-Based Fault Diagnosis for Turboshaft Engines

    NASA Technical Reports Server (NTRS)

    Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.

    1998-01-01

    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 faults 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 faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.

  15. Fault detection and diagnosis using neural network approaches

    NASA Technical Reports Server (NTRS)

    Kramer, Mark A.

    1992-01-01

    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 faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault 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 faults. 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.

  16. Reasoning about fault diagnosis for the space station common module thermal control system

    NASA Technical Reports Server (NTRS)

    Vachtsevanos, G.; Hexmoor, H.; Purves, B.

    1988-01-01

    The proposed common module thermal control system for the Space Station is designed to integrate thermal distribution and thermal control functions in order to transport heat and provide environmental temperature control through the common module. When the thermal system is operating in an off-normal state, due to component faults, an intelligent controller is called upon to diagnose the fault type, identify the fault location and determine the appropriate control action required to isolate the faulty component. A methodology is introduced for fault diagnosis based upon a combination of signal redundancy techniques and fuzzy logic. An expert system utilizes parity space representation and analytic redundancy to derive fault symptoms, the aggregate of which is assessed by a multivalued rule based system. A subscale laboratory model of the thermal control system designed is used as the testbed for the study.

  17. Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Lo, Chun; Lynch, Jerome P.; Liu, Mingyan

    2016-01-01

    Wireless sensors operating in harsh environments have the potential to be error-prone. This paper presents a distributive model-based diagnosis algorithm that identifies nonlinear sensor faults. The diagnosis algorithm has advantages over existing fault diagnosis methods such as centralized model-based and distributive model-free methods. An algorithm is presented for detecting common non-linearity faults without using reference sensors. The study introduces a model-based fault diagnosis framework that is implemented within a pair of wireless sensors. The detection of sensor nonlinearities is shown to be equivalent to solving the largest empty rectangle (LER) problem, given a set of features extracted from an analysis of sensor outputs. A low-complexity algorithm that gives an approximate solution to the LER problem is proposed for embedment in resource constrained wireless sensors. By solving the LER problem, sensors corrupted by non-linearity faults can be isolated and identified. Extensive analysis evaluates the performance of the proposed algorithm through simulation.

  18. Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform

    NASA Astrophysics Data System (ADS)

    Li, Chuan; Liang, Ming

    2012-01-01

    The vibration data, especially those collected during the system run-up and run-down periods, contain rich information for gearbox condition monitoring. Time-frequency (TF) signal analysis is an effective tool to detect gearbox faults under varying shaft speed. However, the feature of the amplitude modulated-frequency modulated (AM-FM) gearbox fault signal usually cannot be directly extracted from the blurred time-frequency representation (TFR) caused by the time-varying frequency and noisy multicomponent measurement. As such, we propose to use a generalized synchrosqueezing transform (GST)-based TF method to detect and diagnose gearbox faults. With this method, the original vibration signal is first mapped into another analytical signal to facilitate synchrosqueezing of the TF picture. A time-scale domain restoration process is then applied to recover the instantaneous frequency profile with concentrated TFR. The gearbox fault, if any, can then be detected by observing the presence of the meshing frequency and sideband components in the TFR. The faulty gear can be identified via frequency relation analysis of AM-FM components. The proposed method is evaluated using both simulated and experimental gearbox vibration signals. The results show that the proposed approach is effective for gearbox condition monitoring.

  19. Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

    NASA Astrophysics Data System (ADS)

    Zhen, D.; Wang, T.; Gu, F.; Ball, A. D.

    2013-01-01

    Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.

  20. Artificial neural network application for space station power system fault diagnosis

    NASA Technical Reports Server (NTRS)

    Momoh, James A.; Oliver, Walter E.; Dias, Lakshman G.

    1995-01-01

    This study presents a methodology for fault diagnosis 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 fault diagnosis 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 fault diagnostic tool. The results from the two studies are contrasted. In the event of a major fault, ground controllers need the ability to identify the type of fault, isolate the fault 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 fault 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 fault diagnosis using ANN versus the results obtained with the SPICE models.

  1. Customized multiwavelets for planetary gearbox fault detection based on vibration sensor signals.

    PubMed

    Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

    2013-01-01

    Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration 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 fault 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 fault 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 vibration 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 faults on two neighboring teeth in the planetary gearbox. PMID:23334609

  2. Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals

    PubMed Central

    Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

    2013-01-01

    Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration 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 fault 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 fault 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 vibration 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 faults on two neighboring teeth in the planetary gearbox. PMID:23334609

  3. An integrated approach to performance monitoring and fault diagnosis of nuclear power systems

    NASA Astrophysics Data System (ADS)

    Zhao, Ke

    2005-07-01

    An integrated approach to performance monitoring and fault diagnosis was developed in this dissertation for nuclear power plants using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for fault diagnosis. In the applications to nuclear power plants, on the one hand, routine operation data may not be able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components may experience degradation. On the other hand, physical models always have uncertainty and are often too complicated in terms of model structure to design residual generators for fault diagnosis. Therefore, a realistic fault diagnosis method needs to combine the strength of physical models in modeling a wide range of anticipated operation conditions and the strength of statistical data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using physical models and model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed method could eliminate false alarms due to model uncertainty and deal with operating condition changes of nuclear power plants. The developed algorithms were demonstrated using the International Reactor Innovative and Secure (IRIS) Helical Coil Steam Generator (HCSG) systems. A thermal hydraulic model was developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and fault diagnosis module was developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operating conditions. This dissertation will bridge the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and fault diagnosis for nuclear power plants. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors.

  4. The Broadcast Comparison Model for On-Line Fault Diagnosis in Multicomputer Systems

    E-print Network

    Blough, Douglas M.

    processors in the system and their outputs compared by a central observer. This central observer diagnosis [2], [5], [6], [15], instead of relying on a central observer, each fault-free processor performs to as the BGM model. In these two models, it is assumed that a processor can test the status of another

  5. Fault Diagnosis of VLSI Circuits with Cellular Automata based Pattern Classifier

    E-print Network

    Ganguly, Niloy

    Fault Diagnosis of VLSI Circuits with Cellular Automata based Pattern Classifier Biplab K Sikdar 1 Niloy Ganguly 2 P Pal Chaudhuri 3 1 Department of Computer Science & Tech, Bengal Engineering & Science. A special class of Cellular Automata (CA) referred to as Multiple Attractor CA (MACA) is employed

  6. Runtime Verification in Context : Can Optimizing Error Detection Improve Fault Diagnosis

    NASA Technical Reports Server (NTRS)

    Dwyer, Matthew B.; Purandare, Rahul; Person, Suzette

    2010-01-01

    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 fault 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, fault diagnosis, 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 fault diagnosis.

  7. Data Mining in Multi-Dimensional Functional Data for Manufacturing Fault Diagnosis

    SciTech Connect

    Jeong, Myong K; Kong, Seong G; Omitaomu, Olufemi A

    2008-09-01

    Multi-dimensional functional data, such as time series data and images from manufacturing processes, have been used for fault detection and quality improvement in many engineering applications such as automobile manufacturing, semiconductor manufacturing, and nano-machining systems. Extracting interesting and useful features from multi-dimensional functional data for manufacturing fault diagnosis is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of functional data types, high correlation, and nonstationary nature of the data. This chapter discusses accomplishments and research issues of multi-dimensional functional data mining in the following areas: dimensionality reduction for functional data, multi-scale fault diagnosis, misalignment prediction of rotating machinery, and agricultural product inspection based on hyperspectral image analysis.

  8. Switched Fault Diagnosis Approach for Industrial Processes based on Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Wang, Lin; Yang, Chunjie; Sun, Youxian; Pan, Yijun; An, Ruqiao

    2015-11-01

    Traditional fault diagnosis methods based on hidden Markov model (HMM) use a unified method for feature extraction, such as principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA). However, every method has its own limitations. For example, PCA cannot extract nonlinear relationships among process variables. So it is inappropriate to extract all features of variables by only one method, especially when data characteristics are very complex. This article proposes a switched feature extraction procedure using PCA and KPCA based on nonlinearity measure. By the proposed method, we are able to choose the most suitable feature extraction method, which could improve the accuracy of fault diagnosis. A simulation from the Tennessee Eastman (TE) process demonstrates that the proposed approach is superior to the traditional one based on HMM and could achieve more accurate classification of various process faults.

  9. Online motor fault detection and diagnosis using a hybrid FMM-CART model.

    PubMed

    Seera, Manjeevan; Lim, Chee Peng

    2014-04-01

    In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks. PMID:24807956

  10. Combinatorial Optimization Algorithms for Dynamic Multiple Fault Diagnosis in Automotive and Aerospace Applications

    NASA Astrophysics Data System (ADS)

    Kodali, Anuradha

    In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault 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 fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault 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 fault-test dependency matrix that couples the failed tests and faults 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 fault diagnosis 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 facility, respectively. The set-covering matrix encapsulates the relationship among the rows (tests or demand points) and columns (faults or locations) of the system at each time. By relaxing the coupling constraints using Lagrange multipliers, the DSC problem can be decoupled into independent subproblems, one for each column. Each subproblem is solved using the Viterbi decoding algorithm, and a primal feasible solution is constructed by modifying the Viterbi solutions via a heuristic. The proposed Viterbi-Lagrangian relaxation algorithm (VLRA) provides a measure of suboptimality via an approximate duality gap. As a major practical extension of the above problem, we also consider the problem of diagnosing faults with delayed test outcomes, termed delay-dynamic set-covering (DDSC), and experiment with real-world problems that exhibit masking faults. Also, we present simulation results on OR-library datasets (set-covering formulations are predominantly validated on these matrices in the literature), posed as facility location problems. Finally, we implement these algorithms to solve problems in aerospace and automotive applications. Firstly, we address the diagnostic ambiguity problem in aerospace and automotive applications by developing a dynamic fusion framework that includes dynamic multiple fault diagnosis algorithms. This improves the correct fault isolation rate, while minimizing the false alarm rates, by considering multiple faults instead of the traditional data-driven techniques based on single fault (class)-single epoch (static) assumption. The dynamic fusion problem is formulated as a maximum a posteriori decision problem of inferring the fault sequence based on uncertain outcomes of multiple binary classifiers over time. The fusion process involves three steps: the first step transforms the multi-class problem into dichotomies using error correcting output codes (ECOC), thereby solving the concomitant binary classification problems; the second step fuses the outcomes of multiple binary classifiers over time using a sliding window or block dynamic fusi

  11. An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Xue, Xiaoming; Zhou, Jianzhong; Xu, Yanhe; Zhu, Wenlong; Li, Chaoshun

    2015-10-01

    Ensemble empirical mode decomposition (EEMD) represents a significant improvement over the original empirical mode decomposition (EMD) method for eliminating the mode mixing problem. However, the added white noises generate some tough problems including the high computational cost, the determination of the two critical parameters (the amplitude of the added white noise and the number of ensemble trials), and the contamination of the residue noise in the signal reconstruction. To solve these problems, an adaptively fast EEMD (AFEEMD) method combined with complementary EEMD (CEEMD) is proposed in this paper. In the proposed method, the two critical parameters are respectively fixed as 0.01 times standard deviation of the original signal and two ensemble trials. Instead, the upper frequency limit of the added white noise is the key parameter which needs to be prescribed beforehand. Unlike the original EEMD method, only two high-frequency white noises are added to the signal to be investigated with anti-phase in AFEEMD. Furthermore, an index termed relative root-mean-square error is employed for the adaptive selection of the proper upper frequency limit of the added white noises. Simulation test and vibration signals based fault diagnosis of rolling element bearing under different fault types are utilized to demonstrate the feasibility and effectiveness of the proposed method. The analysis results indicate that the AFEEMD method represents a sound improvement over the original EEMD method, and has strong practicability.

  12. Deep-reasoning fault diagnosis - An aid and a model

    NASA Technical Reports Server (NTRS)

    Yoon, Wan Chul; Hammer, John M.

    1988-01-01

    The design and evaluation are presented for the knowledge-based assistance of a human operator who must diagnose a novel fault in a dynamic, physical system. A computer aid based on a qualitative model of the system was built to help the operators overcome some of their cognitive limitations. This aid differs from most expert systems in that it operates at several levels of interaction that are believed to be more suitable for deep reasoning. Four aiding approaches, each of which provided unique information to the operator, were evaluated. The aiding features were designed to help the human's casual reasoning about the system in predicting normal system behavior (N aiding), integrating observations into actual system behavior (O aiding), finding discrepancies between the two (O-N aiding), or finding discrepancies between observed behavior and hypothetical behavior (O-HN aiding). Human diagnostic performance was found to improve by almost a factor of two with O aiding and O-N aiding.

  13. A novel identification method of Volterra series in rotor-bearing system for fault diagnosis

    NASA Astrophysics Data System (ADS)

    Xia, Xin; Zhou, Jianzhong; Xiao, Jian; Xiao, Han

    2016-01-01

    Volterra series is widely employed in the fault diagnosis of rotor-bearing system to prevent dangerous accidents and improve economic efficiency. The identification of the Volterra series involves the infinite-solution problems which is caused by the periodic characteristic of the excitation signal of rotor-bearing system. But this problem has not been considered in the current identification methods of the Volterra series. In this paper, a key kernels-PSO (KK-PSO) method is proposed for Volterra series identification. Instead of identifying the Volterra series directly, the key kernels of Volterra are found out to simply the Volterra model firstly. Then, the Volterra series with the simplest formation is identified by the PSO method. Next, simulation verification is utilized to verify the feasibility and effectiveness of the KK-PSO method by comparison to the least square (LS) method and traditional PSO method. Finally, experimental tests have been done to get the Volterra series of a rotor-bearing test rig in different states, and a fault diagnosis system is built with a neural network to classify different fault conditions by the kernels of the Volterra series. The analysis results indicate that the KK-PSO method performs good capability on the identification of Volterra series of rotor-bearing system, and the proposed method can further improve the accuracy of fault diagnosis.

  14. The Marshall Space Flight Center Fault Detection Diagnosis and Recovery Laboratory

    NASA Technical Reports Server (NTRS)

    Burchett, Bradley T.; Gamble, Jonathan; Rabban, Michael

    2008-01-01

    The Fault Detection Diagnosis and Recovery Lab (FDDR) has been developed to support development of,fault detection algorithms for the flight computer aboard the Ares I and follow-on vehicles. It consists of several workstations using Ethernet and TCP/IP to simulate communications between vehicle sensors, flight computers, and ground based support computers. Isolation of tasks between workstations was set up intentionally to limit information flow and provide a realistic simulation of communication channels within the vehicle and between the vehicle and ground station.

  15. Fault diagnosis of rotating machinery using an intelligent order tracking system

    NASA Astrophysics Data System (ADS)

    Bai, Mingsian; Huang, Jiamin; Hong, Minghong; Su, Fucheng

    2005-02-01

    This research focuses on the development of an intelligent diagnostic system for rotating machinery. The system is composed of a signal processing module and a state inference module. In the signal processing module, the recursive least square (RLS) algorithm and the Kalman filter are exploited to extract the order amplitudes of vibration signals, followed by fault classification using the fuzzy state inference module. The RLS algorithm and Kalman filter provide advantages in order tracking over conventional Fourier-based techniques in that they are insensitive to smearing problems arising from closely spaced orders or crossing orders. On the basis of thus obtained order features, the potential fault types are then deduced with the aid of a state inference engine. Human diagnostic rules are fuzzified for various common faults, including the single fault and double fault situations. This system is implemented on the platform of a floating point digital signal processor, where a photo switch and an accelerometer supply the shaft speed and acceleration signals, respectively. Experiments were carried out for a rotor kit and a practical four-cylinder engine to show the effectiveness of the proposed system in tracking the rotating order with precise inference.

  16. Robust fault diagnosis of physical systems in operation. Ph.D. Thesis - Rutgers - The State Univ.

    NASA Technical Reports Server (NTRS)

    Abbott, Kathy Hamilton

    1991-01-01

    Ideas are presented and demonstrated for improved robustness in diagnostic problem solving of complex physical systems in operation, or operative diagnosis. 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 diagnosis. 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 diagnosis 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 diagnosis. The suitability of this constructive approach is shown for diagnosing fault 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 fault propagation behavior. An approach is demonstrated that threats these different behaviors as different fault 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.

  17. Fault diagnosis model for power transformers based on information fusion

    NASA Astrophysics Data System (ADS)

    Dong, Ming; Yan, Zhang; Yang, Li; Judd, Martin D.

    2005-07-01

    Methods used to assess the insulation status of power transformers before they deteriorate to a critical state include dissolved gas analysis (DGA), partial discharge (PD) detection and transfer function techniques, etc. All of these approaches require experience in order to correctly interpret the observations. Artificial intelligence (AI) is increasingly used to improve interpretation of the individual datasets. However, a satisfactory diagnosis may not be obtained if only one technique is used. For example, the exact location of PD cannot be predicted if only DGA is performed. However, using diverse methods may result in different diagnosis solutions, a problem that is addressed in this paper through the introduction of a fuzzy information infusion model. An inference scheme is proposed that yields consistent conclusions and manages the inherent uncertainty in the various methods. With the aid of information fusion, a framework is established that allows different diagnostic tools to be combined in a systematic way. The application of information fusion technique for insulation diagnostics of transformers is proved promising by means of examples.

  18. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    NASA Technical Reports Server (NTRS)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1998-01-01

    Control of air contaminants is a crucial factor in the safety considerations of crewed space flight. Indoor air quality needs to be closely monitored during long range missions such as a Mars mission, and also on large complex space structures such as the International Space Station. This work mainly pertains to the detection and simulation of air contaminants in the space station, though much of the work is easily extended to buildings, and issues of ventilation systems. Here we propose a method with which to track the presence of contaminants using an accurate physical model, and also develop a robust procedure that would raise alarms when certain tolerance levels are exceeded. A part of this research concerns the modeling of air flow inside a spacecraft, and the consequent dispersal pattern of contaminants. Our objective is to also monitor the contaminants on-line, so we develop a state estimation procedure that makes use of the measurements from a sensor system and determines an optimal estimate of the contamination in the system as a function of time and space. The real-time optimal estimates in turn are used to detect faults in the system and also offer diagnoses as to their sources. This work is concerned with the monitoring of air contaminants aboard future generation spacecraft and seeks to satisfy NASA's requirements as outlined in their Strategic Plan document (Technology Development Requirements, 1996).

  19. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    NASA Technical Reports Server (NTRS)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1996-01-01

    Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.

  20. Fault Diagnosis for the Heat Exchanger of the Aircraft Environmental Control System Based on the Strong Tracking Filter

    PubMed Central

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    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. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-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 fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

  1. Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

    PubMed

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    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. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-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 fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

  2. A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis

    PubMed Central

    Zhu, Daqi; Bai, Jie; Yang, Simon X.

    2010-01-01

    A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time. PMID:22315537

  3. An Expert Fault Diagnosis System for Vehicle Air Conditioning Product Development

    NASA Astrophysics Data System (ADS)

    Tan, C. F.; Tee, B. T.; Khalil, S. N.; Chen, W.; Rauterberg, G. W. M.

    2015-09-01

    The paper describes the development of the vehicle air-conditioning fault diagnosis system in automotive industries with expert system shell. The main aim of the research is to diagnose the problem of new vehicle air-conditioning system development process and select the most suitable solution to the problems. In the vehicle air-conditioning manufacturing industry, process can be very costly where an expert and experience personnel needed in certain circumstances. The expert of in the industry will retire or resign from time to time. When the expert is absent, their experience and knowledge is difficult to retrieve or lost forever. Expert system is a convenient method to replace expert. By replacing the expert with expert system, the accuracy of the processes will be increased compared to the conventional way. Therefore, the quality of product services that are produced will be finer and better. The inputs for the fault diagnosis are based on design data and experience of the engineer.

  4. Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

    NASA Technical Reports Server (NTRS)

    Kobayashi, Takahisa; Simon, Donald L.

    2006-01-01

    A new robust method of automated real-time diagnosis of faults 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 fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults 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 fault 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 faults 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 fault. When a sensor or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.

  5. State Tracking and Fault Diagnosis for Dynamic Systems Using Labeled Uncertainty Graph.

    PubMed

    Zhou, Gan; Feng, Wenquan; Zhao, Qi; Zhao, Hongbo

    2015-01-01

    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 fault diagnosis 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 fault diagnosis 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 faults, and a roll-backward process that analyzes possible system trajectories once the faults 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. PMID:26556358

  6. Electrical Motor Current Signal Analysis using a Dynamic Time Warping Method for Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    Zhen, D.; Alibarbar, A.; Zhou, X.; Gu, F.; Ball, A. D.

    2011-07-01

    This paper presents the analysis of phase current signals to identify and quantify common faults from an electrical motor based on dynamic time warping (DTW) algorithm. In condition monitoring, measurements are often taken when the motor undertakes varying loads and speeds. The signals acquired in these conditions show similar profiles but have phase shifts, which do not line up in the time-axis for adequate comparison to discriminate the small changes in machine health conditions. In this study, DTW algorithms are exploited to align the signals to an ideal current signal constructed based on average operating conditions. In this way, comparisons between the signals can be made directly in the time domain to obtain residual signals. These residual signals are then based on to extract features for detecting and diagnosing the faults of the motor and components operating under different loads and speeds. This study provides a novel approach to the analysis of electrical current signal for diagnosis of motor faults. Experimental data sets of electrical motor current signals have been studied using DTW algorithms. Results show that DTW based residual signals highlights more the modulations due to the compressor process. And hence can obtain better fault detection and diagnosis results.

  7. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    PubMed

    Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

    2015-01-01

    In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis 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 fault diagnosis 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 fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis. PMID:25993810

  8. Optimizing the Adaptive Stochastic Resonance and Its Application in Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    Liu, Xiaole; Yang, Jianhua; Liu, Houguang; Cheng, Gang; Chen, Xihui; Xu, Dan

    2015-10-01

    This paper presents an adaptive stochastic resonance method based on the improved artificial fish swarm algorithm. By this method, we can enhance the weak characteristic signal which is submerged in a heavy noise. We can also adaptively lead the stochastic resonance to be optimized to the greatest extent. The effectiveness of the proposed method is verified by both numerical simulation and lab experimental vibration signals including normal, a chipped tooth and a missing tooth of planetary gearboxes under the loaded condition. Both theoretical and experimental results show that this method can effectively extract weak characteristics in a heavy noise. In the experiment, each weak fault feature is extracted successfully from the fault planetary gear. When compared with the ensemble empirical mode decomposition (EEMD) method, the method proposed in this paper has been found to give remarkable performance.

  9. Fault detection in heavy duty wheels by advanced vibration processing techniques and lumped parameter modeling

    NASA Astrophysics Data System (ADS)

    Malago`, M.; Mucchi, E.; Dalpiaz, G.

    2016-03-01

    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 fault detection to be used at the end of the manufacturing process has been developed. This procedure is based on vibration 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.

  10. A New On-Line Diagnosis Protocol for the SPIDER Family of Byzantine Fault Tolerant Architectures

    NASA Technical Reports Server (NTRS)

    Geser, Alfons; Miner, Paul S.

    2004-01-01

    This paper presents the formal verification of a new protocol for online distributed diagnosis 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 fault-tolerant device that guarantees Interactive Consistency, Distributed Diagnosis (Group Membership), and Synchronization in the presence of a bounded number of physical faults. Formal verification of the original SPIDER diagnosis 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 diagnosis protocol and a formal proof of its correctness using PVS.

  11. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection.

    PubMed

    Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

    2015-01-01

    Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis 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 diagnosis 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 fault 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 faults promptly and effectively, which improves the WSN reliability. PMID:26193280

  12. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

    PubMed Central

    Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

    2015-01-01

    Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis 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 diagnosis 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 fault 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 faults promptly and effectively, which improves the WSN reliability. PMID:26193280

  13. Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint

    SciTech Connect

    Zappala, D.; Tavner, P.; Crabtree, C.; Sheng, S.

    2013-01-01

    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 diagnosis 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 represent one of the main limitations of most current CMSs, so automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. The algorithm allowed the assessment of gear fault 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.

  14. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    PubMed Central

    Flores, Agustín; Morant, Francisco

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis 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 fault 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 fault. 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

  15. Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

    NASA Technical Reports Server (NTRS)

    Ricks, Brian W.; Mengshoel, Ole J.

    2009-01-01

    Health management systems that more accurately and quickly diagnose faults 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 diagnosis of abrupt continuous (or parametric) faults 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 diagnosis of abrupt discrete faults. 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.

  16. A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

    SciTech Connect

    Zhang Yumin; Lum, Kai-Yew; Wang Qingguo

    2009-03-05

    In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault 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 fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.

  17. Feature extraction of kernel regress reconstruction for fault diagnosis based on self-organizing manifold learning

    NASA Astrophysics Data System (ADS)

    Chen, Xiaoguang; Liang, Lin; Xu, Guanghua; Liu, Dan

    2013-09-01

    The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension. Currently, nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings, such as manifold learning. However, these methods are all based on manual intervention, which have some shortages in stability, and suppressing the disturbance noise. To extract features automatically, a manifold learning method with self-organization mapping is introduced for the first time. Under the non-uniform sample distribution reconstructed by the phase space, the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation. Finally, the signal is reconstructed by the kernel regression. Several typical states include the Lorenz system, engine fault with piston pin defect, and bearing fault with outer-race defect are analyzed. Compared with the LTSA and continuous wavelet transform, the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified. A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed.

  18. Fault Diagnosis with Multi-State Alarms in a Nuclear Power Control Simulation

    SciTech Connect

    Stuart A. Ragsdale; Roger Lew; Ronald L. Boring

    2014-09-01

    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 diagnosis for two types of faults differing in complexity. We hypothesized the use of three-state alarms would improve performance in alarm recognition and fault 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 better 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.

  19. FAULT DIAGNOSIS WITH MULTI-STATE ALARMS IN A NUCLEAR POWER CONTROL SIMULATOR

    SciTech Connect

    Austin Ragsdale; Roger Lew; Brian P. Dyre; Ronald L. Boring

    2012-10-01

    This research addresses how alarm systems can increase operator performance within nuclear power plant operations. The experiment examined the effect of two types of alarm systems (two-state and three-state alarms) on alarm compliance and diagnosis for two types of faults differing in complexity. We hypothesized three-state alarms would improve performance in alarm recognition and fault diagnoses over that of two-state alarms. We used sensitivity and criterion based on Signal Detection Theory 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 better 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.

  20. Fault diagnosis of rolling bearing based on fast nonlocal means and envelop spectrum.

    PubMed

    Lv, Yong; Zhu, Qinglin; Yuan, Rui

    2015-01-01

    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 fault 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 fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures. PMID:25585105

  1. Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum

    PubMed Central

    Lv, Yong; Zhu, Qinglin; Yuan, Rui

    2015-01-01

    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 fault 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 fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures. PMID:25585105

  2. Nuclear power plant fault diagnosis using neural networks with error estimation by series association

    SciTech Connect

    Kim, K.

    1996-08-01

    The accuracy of the diagnosis obtained from a nuclear power plant fault-diagnostic advisor using neural networks is addressed in this paper in order to ensure the credibility of the diagnosis. A new error estimation scheme called error estimation by series association provides a measure of the accuracy associated with the advisor`s diagnoses. This error estimation is performed by a secondary neural network that is fed both the input features for and the outputs of the advisor. The error estimation by series association outperforms previous error estimation techniques in providing more accurate confidence information with considerably reduced computational requirements. The authors demonstrate the extensive usability of their method by applying it to a complicated transient recognition problem of 33 transient scenarios. The simulated transient data at different severities consists of 25 distinct transients for the Duane Arnold Energy Center nuclear power station ranging from a main steam line break to anticipated transient without scram (ATWS) conditions. The fault-diagnostic advisor system with the secondary error prediction network is tested on the transients at various severity levels and degraded noise conditions. The results show that the error estimation scheme provides a useful measure of the validity of the advisor`s output or diagnosis with considerable reduction in computational requirements over previous error estimation schemes.

  3. Fault Tree Based Diagnosis with Optimal Test Sequencing for Field Service Engineers

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; George, Laurence L.; Patterson-Hine, F. A.; Lum, Henry, Jr. (Technical Monitor)

    1994-01-01

    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 diagnosis. 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 Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis 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 fault 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 fault 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 developed easily.

  4. SPECTRUM CLASSIFICATION FOR EARLY FAULT DIAGNOSIS OF THE LP GAS PRESSURE REGULATOR BASED ON THE KULLBACK-LEIBLER KERNEL

    E-print Network

    Higuchi, Tomoyuki

    on the particular sit- uation. The spectrum analysis method is generally used for the diagnosis of machines that have a vibrating or rotating structure. The use of spectrum analysis in this case is appro- priate [5 inspection of the transient response. In such cases, the spectrum diagnosis method is superior to time

  5. Improved 2D model of a ball bearing for the simulation of vibrations due to faults during run-up

    NASA Astrophysics Data System (ADS)

    Tadina, Matej; Boltežar, Miha

    2011-07-01

    This paper present an improved 2D bearing model for investigation of the vibrations of a ball-bearing during run-up. The presented numerical model assumes deformable outer race, which is modelled with finite elements, centrifugal load effects and radial clearance. The contact force for the balls is described by a nonlinear Hertzian contact deformation. Various surface defects due to local deformations are introduced into the developed model. The detailed geometry of the local defects is modelled as an impressed ellipsoid on the races and as a flattened sphere for the rolling balls. The obtained equations of motion were solved numerically with a modified Newmark time-integration method for the increasing rotational frequency of the shaft. The simulated vibrational response of the bearing with different local faults was used to test the suitability of the continuous wavelet transformation for the bearing fault identification and classification.

  6. Balancing filters: An approach to improve model-based fault diagnosis based on parity equations

    NASA Astrophysics Data System (ADS)

    Beckerle, Philipp; Schaede, Hendrik; Butzek, Norman; Rinderknecht, Stephan

    2012-05-01

    Model-based fault detection often deals with the problem that fault states cannot be distinguished clearly. One way to improve the results is the use of balancing filters. The purpose of these filters is to balance the magnitude response over its full frequency range, since fault states show deviations from the nominal behavior at different frequencies and therefore at diverse magnitude levels. Their application aims on increasing the magnitude response levels in frequency ranges where it is low and to decrease it where the magnitude is on a higher level. Hence, the influence of the deviations caused by the fault states is weighted equally at all examined frequencies. The compensation of the system's basic characteristics leads to a stronger influence of the fault-caused deviations. Since these are useable features for fault identification, balancing filters lead to a better distinction between the states and faults. To apply such filters on real systems they must be designed and adapted to the particular system. This paper describes the idea of balancing filters for a diagnosis concept based on feature extraction by means of parity equations and shows several methods to design these filters. The first design method is based on placing poles and zeros heuristically to model the global characteristics of the frequency response and inverting this model to get a balancing filter. In contrast to this, the second approach uses measured data by inverting an experimentally identified model of the process. For the third method simple Butterworth filter elements are used to build up an inverted model of the global frequency behavior of the system directly. Since an adaptation of the filters to the investigated system is required experimental results show the improvements induced by these filters. The filters' effects are investigated on a test rig of a centrifugal pump with magnetic bearings. A second system that shows a more complex transfer behavior is used for the evaluation of the repeatability of the resulting improvements. Finally the idea of balancing filters, the presented design methods and the achieved experimental results are discussed in details.

  7. Qualitative multiple-fault diagnosis of continuous dynamic systems using behavioral modes

    SciTech Connect

    Subramanian, S.; Mooney, R.J.

    1996-12-31

    Most model-based diagnosis systems, such as GDE and Sherlock, have concerned discrete, static systems such as logic circuits and use simple constraint propagation to detect inconsistencies. However, sophisticated systems such as QSIM and QPE have been developed for qualitative modeling and simulation of continuous dynamic systems. We present an integration of these two lines of research as implemented in a system called QDOCS for multiple-fault diagnosis of continuous dynamic systems using QSIM models. The main contributions of the algorithm include a method for propagating dependencies while solving a general constraint satisfaction problem and a method for verifying the consistency of a behavior with a model across time. Through systematic experiments on two realistic engineering systems, we demonstrate that QDOCS demonstrates a better balance of generality, accuracy, and efficiency than competing methods.

  8. A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.

    PubMed

    Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi

    2014-01-01

    A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault 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 faults diagnose of rolling bearings via signals' separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration 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 faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

  9. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

    PubMed Central

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    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 faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis 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 fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

  10. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    PubMed

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    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 faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis 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 fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

  11. Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients

    NASA Astrophysics Data System (ADS)

    Baraldi, Piero; Di Maio, Francesco; Rigamonti, Marco; Zio, Enrico; Seraoui, Redouane

    2015-06-01

    Empirical methods for fault diagnosis usually entail a process of supervised training based on a set of examples of signal evolutions "labeled" with the corresponding, known classes of fault. However, in practice, the signals collected during plant operation may be, very often, "unlabeled", i.e., the information on the corresponding type of occurred fault 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.

  12. Distributed intrusion monitoring system with fiber link backup and on-line fault diagnosis functions

    NASA Astrophysics Data System (ADS)

    Xu, Jiwei; Wu, Huijuan; Xiao, Shunkun

    2014-12-01

    A novel multi-channel distributed optical fiber intrusion monitoring system with smart fiber link backup and on-line fault diagnosis 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 fault 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 fault 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.

  13. Model-based monitoring and fault diagnosis of fossil power plant process units using Group Method of Data Handling.

    PubMed

    Li, Fan; Upadhyaya, Belle R; Coffey, Lonnie A

    2009-04-01

    This paper presents an incipient fault diagnosis approach based on the Group Method of Data Handling (GMDH) technique. The GMDH algorithm provides a generic framework for characterizing the interrelationships among a set of process variables of fossil power plant sub-systems and is employed to generate estimates of important variables in a data-driven fashion. In this paper, ridge regression techniques are incorporated into the ordinary least squares (OLS) estimator to solve regression coefficients at each layer of the GMDH network. The fault diagnosis method is applied to feedwater heater leak detection with data from an operating coal-fired plant. The results demonstrate the proposed method is capable of providing an early warning to operators when a process fault or an equipment fault occurs in a fossil power plant. PMID:19084227

  14. The fault monitoring and diagnosis knowledge-based system for space power systems: AMPERES, phase 1

    NASA Technical Reports Server (NTRS)

    Lee, S. C.

    1989-01-01

    The objective is to develop a real time fault monitoring and diagnosis 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 fault monitoring and diagnosis, and its supporting knowledge representation scheme.

  15. Combined expert system/neural networks method for process fault diagnosis

    DOEpatents

    Reifman, Jaques (Westchester, IL); Wei, Thomas Y. C. (Downers Grove, IL)

    1995-01-01

    A two-level hierarchical approach for process fault diagnosis 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 diagnosis 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 fault can be determined, i.e., a component characteristic-oriented approach.

  16. Combined expert system/neural networks method for process fault diagnosis

    DOEpatents

    Reifman, J.; Wei, T.Y.C.

    1995-08-15

    A two-level hierarchical approach for process fault diagnosis 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 diagnosis 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 fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.

  17. Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition

    PubMed Central

    Lei, Yaguo; Li, Naipeng; Lin, Jing; Wang, Sizhe

    2013-01-01

    The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition (EEMD) was proposed accordingly. EEMD is able to reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithms. In most of the studies on EEMD, the parameters were selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed in this paper. In the method, the sifting number is adaptively selected, and the amplitude of the added noise changes with the signal frequency components during the decomposition process. The simulation, the experimental and the application results demonstrate that the adaptive EEMD provides the improved results compared with the original EEMD in diagnosing rotating machinery. PMID:24351666

  18. A fault-tolerant attitude control system for a satellite based on fuzzy global sliding mode control algorithm

    NASA Astrophysics Data System (ADS)

    Liang, Jinjin; Dong, Chaoyang; Wang, Qing

    2008-10-01

    An effective approach for fault diagnosis of aeroengine based on integration of wavelet analysis and neural networks is presented. The wavelet transform can accurately localizes the characteristics of a signal in time-frequency domains and in a view of the inter relationship of wavelet transform between exponent theory, the whole and local exponents obtained from wavelet transform coefficients as features are presented for extracting fault signals, which are inputted into radial basis function for fault pattern recognition. The fault diagnosis model of aero-engine is established and the improved Levenberg-Marquardt training algorithm is used to fulfill the network structure and parameter identification. By choosing enough samples to train the fault diagnosis network and the information representing the faults input into the neural network, the fault pattern can be determined. The robustness of wavelet neural network for fault diagnosis is discussed. The practical fault diagnosis for aeroengine vibration approves to be accurate and comprehensive.

  19. Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

    PubMed Central

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-01-01

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. 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 faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

  20. Intelligent fault diagnosis and failure management of flight control actuation systems

    NASA Technical Reports Server (NTRS)

    Bonnice, William F.; Baker, Walter

    1988-01-01

    The real-time fault diagnosis 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.

  1. Incipient interturn fault diagnosis in induction machines using an analytic wavelet-based optimized Bayesian inference.

    PubMed

    Seshadrinath, Jeevanand; Singh, Bhim; Panigrahi, Bijaya Ketan

    2014-05-01

    Interturn fault diagnosis of induction machines has been discussed using various neural network-based techniques. The main challenge in such methods is the computational complexity due to the huge size of the network, and in pruning a large number of parameters. In this paper, a nearly shift insensitive complex wavelet-based probabilistic neural network (PNN) model, which has only a single parameter to be optimized, is proposed for interturn fault detection. The algorithm constitutes two parts and runs in an iterative way. In the first part, the PNN structure determination has been discussed, which finds out the optimum size of the network using an orthogonal least squares regression algorithm, thereby reducing its size. In the second part, a Bayesian classifier fusion has been recommended as an effective solution for deciding the machine condition. The testing accuracy, sensitivity, and specificity values are highest for the product rule-based fusion scheme, which is obtained under load, supply, and frequency variations. The point of overfitting of PNN is determined, which reduces the size, without compromising the performance. Moreover, a comparative evaluation with traditional discrete wavelet transform-based method is demonstrated for performance evaluation and to appreciate the obtained results. PMID:24808044

  2. Empirical mode decomposition and neural networks on FPGA for fault diagnosis in induction motors.

    PubMed

    Camarena-Martinez, David; Valtierra-Rodriguez, Martin; Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus

    2014-01-01

    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 faults 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 diagnosis during the startup transient of motor faults 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

  3. Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

    PubMed Central

    Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus

    2014-01-01

    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 faults 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 diagnosis during the startup transient of motor faults 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

  4. An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space.

    PubMed

    Aydin, Ilhan; Karakose, Mehmet; Akin, Erhan

    2014-03-01

    Although reconstructed phase space is one of the most powerful methods for analyzing a time series, it can fail in fault diagnosis 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 diagnosis of induction motor faults. 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 faults. The results indicate that the proposed approach has a higher recognition rate than other methods on the same dataset. PMID:24296116

  5. Differential diagnosis of lung carcinoma with three-dimensional quantitative molecular vibrational imaging

    NASA Astrophysics Data System (ADS)

    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.

    2012-06-01

    The advent of molecularly targeted therapies requires effective identification of the various cell types of non-small cell lung carcinomas (NSCLC). Currently, cell type diagnosis 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 diagnosis and preserve tissue samples for subsequent molecular testing in targeted therapy. We report a label-free molecular vibrational 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 diagnosis 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 diagnosis, 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.

  6. Detection and Modeling of High-Dimensional Thresholds for Fault Detection and Diagnosis

    NASA Technical Reports Server (NTRS)

    He, Yuning

    2015-01-01

    Many Fault Detection and Diagnosis (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 diagnosis. 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.

  7. The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator.

    PubMed

    Roh, S D; Kim, S W; Cho, W S

    2001-10-01

    The numerical modelling and process simulation for the fault diagnosis 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 diagnosis, analysis and control. PMID:11954726

  8. A novel intelligent fault diagnosis method for electrical equipment using infrared thermography

    NASA Astrophysics Data System (ADS)

    Zou, Hui; Huang, Fuzhen

    2015-11-01

    Infrared thermography (IRT) has taken a very important role in monitoring and inspecting thermal defects of electrical equipment without shutting down, which has important significance for the stability of power systems. It has many advantages such as non-contact detection, freedom from electromagnetic interference, safety, reliability and providing large inspection coverage. Manual analysis of infrared images for detecting defects and classifying the status of equipment may take a lot of time and efforts, and may also lead to incorrect diagnosis results. To avoid the lack of manual analysis of infrared images, many intelligent fault diagnosis methods for electrical equipment are proposed, but there are two difficulties when using these methods: one is to find the region of interest, another is to extract features which can represent the condition of electrical equipment, as it is difficult to segment infrared images due to their over-centralized distributions and low intensity contrasts, which are quite different from those in visual light images. In this paper, a new intelligent diagnosis method for classification different conditions of electrical equipment using data obtained from infrared images is presented. In the first stage of our method, an infrared image of electrical equipment is clustered using K-means algorithm, then statistical characteristics containing temperature and area information are extracted in each region. In the second stage, in order to select the salient features which can better represent the condition of electrical equipment, some or all statistical characteristics from each region are combined as input data for support vector machine (SVM) classifier. To improve the classification performance of SVM, a coarse-to-fine parameter optimization approach is adopted. The performance of SVM is compared with that of back propagation neural network. The comparison results show that our method can achieve a better performance with accuracy 97.8495%.

  9. Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Chen, Xiaowang; Liang, Ming

    2015-02-01

    The synchrosqueezing transform can effectively improve the readability of time-frequency representation of mono-component and constant frequency signals. However, for multi-component and time-variant frequency signals, it still suffers from time-frequency blurs. In order to address this issue, the synchrosqueezing transform is improved using iterative generalized demodulation. Firstly, the complex nonstationary signal is decomposed into mono-components of constant frequency by iterative generalized demodulation. Then, the instantaneous frequency of each mono-component is accurately estimated via the synchrosqueezing transform, by exploiting its merit of enhanced time-frequency resolution. Finally, the time-frequency representation of the original signal is obtained by superposing the time-frequency representations of all the mono-components with restored instantaneous frequency. This proposed method generalizes the synchrosqueezing transform to multi-component and time-variant frequency signals, and it has fine time-frequency resolution and is free of cross-term interferences. The proposed method was validated using both numerically simulated and lab experimental vibration signals of planetary gearboxes under nonstationary conditions. The time-variant planetary gearbox characteristic frequencies were effectively identified, and the gear faults were correctly diagnosed.

  10. Fault diagnosis of locomotive electro-pneumatic brake through uncertain bond graph modeling and robust online monitoring

    NASA Astrophysics Data System (ADS)

    Niu, Gang; Zhao, Yajun; Defoort, Michael; Pecht, Michael

    2015-01-01

    To improve reliability, safety and efficiency, advanced methods of fault detection and diagnosis become increasingly important for many technical fields, especially for safety related complex systems like aircraft, trains, automobiles, power plants and chemical plants. This paper presents a robust fault detection and diagnostic scheme for a multi-energy domain system that integrates a model-based strategy for system fault modeling and a data-driven approach for online anomaly monitoring. The developed scheme uses LFT (linear fractional transformations)-based bond graph for physical parameter uncertainty modeling and fault simulation, and employs AAKR (auto-associative kernel regression)-based empirical estimation followed by SPRT (sequential probability ratio test)-based threshold monitoring to improve the accuracy of fault detection. Moreover, pre- and post-denoising processes are applied to eliminate the cumulative influence of parameter uncertainty and measurement uncertainty. The scheme is demonstrated on the main unit of a locomotive electro-pneumatic brake in a simulated experiment. The results show robust fault detection and diagnostic performance.

  11. Improved model of a ball bearing for the simulation of vibration signals due to faults during run-up

    NASA Astrophysics Data System (ADS)

    Tadina, Matej; Boltežar, Miha

    2011-08-01

    In this paper an improved bearing model is developed in order to investigate the vibrations of a ball bearing during run-up. The numerical bearing model was developed with the assumptions that the inner race has only 2 DOF and that the outer race is deformable in the radial direction, and is modelled with finite elements. The centrifugal load effect and the radial clearance are taken into account. The contact force for the balls is described by a nonlinear Hertzian contact deformation. Various surface defects due to local deformations are introduced into the developed model. The detailed geometry of the local defects is modelled as an impressed ellipsoid on the races and as a flattened sphere for the rolling balls. With the developed bearing model the transmission path of the bearing housing can be taken into account, since the outer ring can be coupled with the FE model of the housing. The obtained equations of motion were solved numerically with a modified Newmark time-integration method for the increasing rotational frequency of the shaft. The simulated vibrational response of the bearing with different local faults was used to test the suitability of the envelope analysis technique and the continuous wavelet transformation was used for the bearing-fault identification and classification.

  12. Multiple Fault Diagnosis in Complex Physical Systems Matthew Daigle, Xenofon Koutsoukos, and Gautam Biswas

    E-print Network

    Daigle, Matthew

    - tially in the number of faults. In addition, multiple faults in dynamic systems may be hard to detect recognition and candi- date generation. The system, GDE, utilizes the notion of min- imal candidates

  13. Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme

    NASA Technical Reports Server (NTRS)

    Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong

    2011-01-01

    A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault 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 faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred

  14. Modeling fault diagnosis as the activation and use of a frame system. [for pilot problem-solving rating

    NASA Technical Reports Server (NTRS)

    Smith, Philip J.; Giffin, Walter C.; Rockwell, Thomas H.; Thomas, Mark

    1986-01-01

    Twenty pilots with instrument flight ratings were asked to perform a fault-diagnosis 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.

  15. Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Liang, Ming; Chu, Fulei

    2013-07-01

    Nonstationary signal analysis is one of the main topics in the field of machinery fault diagnosis. Time-frequency analysis can identify the signal frequency components, reveals their time variant features, and is an effective tool to extract machinery health information contained in nonstationary signals. Various time-frequency analysis methods have been proposed and applied to machinery fault diagnosis. These include linear and bilinear time-frequency representations (e.g., wavelet transform, Cohen and affine class distributions), adaptive parametric time-frequency analysis (based on atomic decomposition and time-frequency auto-regressive moving average models), adaptive non-parametric time-frequency analysis (e.g., Hilbert-Huang transform, local mean decomposition, and energy separation), and time varying higher order spectra. This paper presents a systematic review of over 20 major such methods reported in more than 100 representative articles published since 1990. Their fundamental principles, advantages and disadvantages, and applications to fault diagnosis of machinery have been examined. Some examples have also been provided to illustrate their performance.

  16. Application of novelty detection methods to health monitoring and typical fault diagnosis of a turbopump

    NASA Astrophysics Data System (ADS)

    Hu, Lei; Hu, Niaoqing; Fan, Bin; Gu, Fengshou

    2012-05-01

    Novelty detection is the identification of deviations from a training set. It is suitable for monitoring the health of mechanical systems where it usually is impossible to know every potential fault. In this paper, two novelty detectors are presented. The first detector which integrates One-Class Support Vector Machine (OCSVM) with an incremental clustering algorithm is designed for health monitoring of the turbopump, while the second one which is trained on sensor fault samples is designed to recognize faults from sensors and faults actually from the turbopump. Analysis results showed that these two detectors are both sensitive and efficient for the health monitoring of the turbopump.

  17. Comparison of Fault Detection Algorithms for Real-time Diagnosis in Large-Scale System. Appendix E

    NASA Technical Reports Server (NTRS)

    Kirubarajan, Thiagalingam; Malepati, Venkat; Deb, Somnath; Ying, Jie

    2001-01-01

    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 faults. 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 fault diagnosis 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 diagnosis tool), we test the performances of 1) TEAMSAT's built-in diagnosis algorithm, 2) Hamming distance based diagnosis, 3) Maximum Likelihood based diagnosis, and 4) HidderMarkov Model based diagnosis.

  18. ARGES: an Expert System for Fault Diagnosis Within Space-Based ECLS Systems

    NASA Technical Reports Server (NTRS)

    Pachura, David W.; Suleiman, Salem A.; Mendler, Andrew P.

    1988-01-01

    ARGES (Atmospheric Revitalization Group Expert System) is a demonstration prototype expert system for fault 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 fault 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, fault identification, and explanation of reasoning in a rapidly assimulated manner. In addition, ARGES recommends possible courses of action for predicted and actual faults. ARGES is seen as a forerunner of AI-based fault management systems for manned space systems.

  19. Fault Diagnosis in Scan-Based BIST Using Both Time and Space Information JayabrataGhosh-Dastidar,Debaleena Das, and Nur A. Touba

    E-print Network

    Touba, Nur A.

    Fault Diagnosis in Scan-Based BIST Using Both Time and Space Information JayabrataEngineering University of Texas, Austin,TX 78712-1084 E-mail: {dghosh,ddas, touba)@cat.ece.utexas.edu Abstract A new technique for diagnosis in a scan-based BIST environment is presented. it allows non-adaptive identijkation

  20. Optimal Design of the Absolute Positioning Sensor for a High-Speed Maglev Train and Research on Its Fault Diagnosis

    PubMed Central

    Zhang, Dapeng; Long, Zhiqiang; Xue, Song; Zhang, Junge

    2012-01-01

    This paper studies an absolute positioning sensor for a high-speed maglev train and its fault diagnosis 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 fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis 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 diagnosis 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

  1. Model-Based Fault Diagnosis: Performing Root Cause and Impact Analyses in Real Time

    NASA Technical Reports Server (NTRS)

    Figueroa, Jorge F.; Walker, Mark G.; Kapadia, Ravi; Morris, Jonathan

    2012-01-01

    Generic, object-oriented fault models, built according to causal-directed graph theory, have been integrated into an overall software architecture dedicated to monitoring and predicting the health of mission- critical systems. Processing over the generic fault models is triggered by event detection logic that is defined according to the specific functional requirements of the system and its components. Once triggered, the fault models provide an automated way for performing both upstream root cause analysis (RCA), and for predicting downstream effects or impact analysis. The methodology has been applied to integrated system health management (ISHM) implementations at NASA SSC's Rocket Engine Test Stands (RETS).

  2. Robust Condition Monitoring and Fault Diagnosis of Variable Speed Induction Motor Drives 

    E-print Network

    Choi, Seungdeog

    2012-02-14

    has been another concern. In this work, the reliability of an electric motor diagnosis signal processing algorithm itself is studied in detail under harsh industrial conditions. Reliability and robustness of the diagnosis has especially been...

  3. A Qualitive Modeling Approach for Fault Detection and Diagnosis on HVAC Systems 

    E-print Network

    Muller, T.; Rehault, N.; Rist, T.

    2013-01-01

    This paper describes the basics and first test results of a model based approach using qualitative modeling to perform Fault Detection and Diagnostics (FDD) on HVAC and R systems. A quantized system describing the qualitative behavior of a...

  4. Development of New Whole Building Fault Detection and Diagnosis Techniques for Commissioning Persistence 

    E-print Network

    Lin, Guanjing

    2012-12-07

    to detect abnormal whole building energy consumption, and two approaches called Cosine Similarity method and Euclidean Distance Similarity method are developed to isolate the possible fault reasons. The effectiveness of these approaches is demonstrated...

  5. On-line implementation of a fault diagnosis system for three-phase induction motors 

    E-print Network

    Alladi, Vijaya Mallikarjun

    2002-01-01

    provide advanced warnings regarding the health of machinery. The implementation of an efficient fault detection system into a form that allows for continuous on-line condition monitoring in industry could reduce the uncertainties related to machinery...

  6. Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank of Kalman Filters

    NASA Technical Reports Server (NTRS)

    Kobayashi, Takahisa; Simon, Donald L. (Technical Monitor)

    2003-01-01

    In this report, a fault detection and isolation (FDI) system which utilizes a bank of Kalman filters is developed for aircraft engine sensor and actuator FDI in conjunction with the detection of component faults. This FDI approach uses multiple Kalman filters, each of which is designed based on a specific hypothesis for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, from which a specific fault is isolated. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The performance of the FDI system is evaluated against a nonlinear engine simulation for various engine faults at cruise operating conditions. In order to mimic the real engine environment, the nonlinear simulation is executed not only at the nominal, or healthy, condition but also at aged conditions. When the FDI system designed at the healthy condition is applied to an aged engine, the effectiveness of the FDI system is impacted by the mismatch in the engine health condition. Depending on its severity, this mismatch can cause the FDI system to generate incorrect diagnostic results, such as false alarms and missed detections. To partially recover the nominal performance, two approaches, which incorporate information regarding the engine s aging condition in the FDI system, will be discussed and evaluated. The results indicate that the proposed FDI system is promising for reliable diagnostics of aircraft engines.

  7. Use of fuzzy cause-effect digraph for resolution fault diagnosis for process plants. 1: Fuzzy cause-effect digraph

    SciTech Connect

    Shih, R.F.; Lee, L.S.

    1995-05-01

    In order to remain efficiently functioning, chemical factories make heavy use of automated systems, such as warning systems and instrumentations, to monitor process variables and to control deviations within an allowable range in production processes. A process abnormality occurs when process variables (such as temperature/pressure) or process parameters (such as catalyst activity) deviate from the designed allowable ranges. A new model graph called fuzzy cause-effect digraph (FCDG) is proposed. This model expresses quantitative deviations of variables from the normal values with fuzzy set. It uses dynamic constraints (confluences) which are converted to dynamic fuzzy relations to express the dynamic gain between the variables in a chemical process. This replaces the steady-state gain between the variables originally expressed with a +, {minus}, or 0 by signed directed graph (SDG). Using this FCDG model would eliminate spurious interpretations attributed to system compensations and inverse responses from backward loops and forward paths in the process. The basic idea and development of this proposed methods are described in this paper. Moreover, this method can apply fuzzy reasoning to estimate the states of the unmeasured variables, to explain fault propagation paths, and to ascertain fault origins. The algorithm of fault diagnosis and its application proposed in this paper are described in part 2.

  8. Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems

    PubMed Central

    Huang, Gang; Luo, Yi-Ping; Zhang, Chang-Fan; Huang, Yi-Shan; Zhao, Kai-Hui

    2015-01-01

    This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in ?? coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method. PMID:25970258

  9. Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems.

    PubMed

    Huang, Gang; Luo, Yi-Ping; Zhang, Chang-Fan; Huang, Yi-Shan; Zhao, Kai-Hui

    2015-01-01

    This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in ?? coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method. PMID:25970258

  10. Parallel Scan-Like Testing and Fault Diagnosis Techniques for Digital Microfluidic Biochips*

    E-print Network

    Chakrabarty, Krishnendu

    testing using parallel droplet pathways in both on-line and off-line scenarios. The diagnosis outcome can chip yield and defect tolerance. We evaluate the proposed test and diagnosis methods using complexity crystallization, blood chemistry for clinical diagnostics, and environmental toxicity monitoring. Biochips offer

  11. A Hybrid Rule-Based/Case-Based Reasoning Approach for Service Fault Diagnosis

    E-print Network

    with its environment. Event correlation techniques have proven to be useful in fault management for network in opposition to network and systems management. While the events that are encountered in network and systems management (called resource events) denote ob- jective facts that are in most cases defined by the vendors

  12. Quantification of multiple fault parameters in flexible turbo-generator systems with incomplete rundown vibration data

    NASA Astrophysics Data System (ADS)

    Lal, Mohit; Tiwari, Rajiv

    2013-12-01

    A turbo-generator system of the modern rotating machinery consists of the driver and driven shafts, which are coupled through flexible couplings and mounted on flexible bearings. Dynamic characterisation of vital machine elements of such rotating machinery is a challenging problem for reliable and accurate response predictions. In this paper, a key intention is to estimate the bearing and coupling dynamic parameters along with residual unbalances at predefined planes, and the misalignment forces and moments at the coupling based on the rundown vibration data. To tackle a practical difficulty of limited measurements and a numerical difficulty of the conventional dynamic condensation in the development of identification algorithm, a novel condensation technique has been implemented especially to overcome measurement of transverse rotational DOFs. Numerical examples are also presented to show the effectiveness of the proposed method. The measurement noise has been added in numerically simulated responses that are used in the present algorithm to identify the parameters and it is found to be robust. Modelling errors of few physical parameters are also considered and estimates are found to be very good.

  13. Research on Diagnosing the Gearbox Faults Based on Near Field Acoustic Holography

    NASA Astrophysics Data System (ADS)

    Jiang, W. K.; Hou, J. J.; Xing, J. T.

    2011-07-01

    The gearbox fault diagnosis was developed for some decades. The current diagnosis techniques were mostly based on analyzing the shell vibration signals especially close to the bearing seat of gearbox. In order to utilize the spatial distribution information of fault signal, the near field acoustic holography (NAH) is employed for the condition monitoring and fault diagnosis of the gearbox in this presentation. The distribution images of sound pressure on the surface of gearbox are reconstructed by NAH, and the feature extraction and pattern recognition can be made by image processing techniques. A gearbox is studied in a semi-anechoic chamber to verify the fault diagnosis technique based on NAH. The pitting and partial broken tooth faults of gears are artificially made on one gear as the fault statuses, and the differences of acoustic images among normal and fault working states under the idling condition are analyzed. It can be found that the acoustic images of gearbox in the three different situations change regularly, and the main sound sources can be recognized from the acoustic images which also contain rich diagnosis information. After feature extraction of the acoustic images, the pattern reorganization technique is employed for diagnosis. The results indicate that this diagnosis procedure based on acoustic images is available and feasible for the gearbox fault diagnosis.

  14. Initial results on fault diagnosis of DSN antenna control assemblies using pattern recognition techniques

    NASA Technical Reports Server (NTRS)

    Smyth, P.; Mellstrom, J.

    1990-01-01

    Initial results obtained from an investigation using pattern recognition techniques for identifying fault modes in the Deep Space Network (DSN) 70 m antenna control loops are described. The overall background to the problem is described, the motivation and potential benefits of this approach are outlined. In particular, an experiment is described in which fault modes were introduced into a state-space simulation of the antenna control loops. By training a multilayer feed-forward neural network on the simulated sensor output, classification rates of over 95 percent were achieved with a false alarm rate of zero on unseen tests data. It concludes that although the neural classifier has certain practical limitations at present, it also has considerable potential for problems of this nature.

  15. Fault diagnosis in gas turbines using a model-based technique

    SciTech Connect

    Merrington, G.L. )

    1994-04-01

    Reliable methods for diagnosing faults and detecting degraded performance in gas turbine engines are continually being sought. In this paper, a model-based technique is applied to the problem of detect in degraded performance in a military turbofan engine from take-off acceleration-type transients. In the past, difficulty has been experienced in isolating the effects of some of the physical processes involved. One such effect is the influence of the bulk metal temperature on the measured engine parameters during large power excursions. It will be shown that the model-based technique provides a simple and convenient way of separating this effect from the faster dynamic components. The important conclusion from this work is at good fault coverage can be gleaned from the resultant pseudo-steady-state gain estimates derived in this way.

  16. Parameter estimation for uncertain systems based on fault diagnosis using Takagi-Sugeno model.

    PubMed

    Nagy-Kiss, A M; Schutz, G; Ragot, J

    2015-05-01

    The paper addresses a systematic procedure to deal with state and parameter uncertainty estimation for nonlinear time-varying systems. A robust observer with respect to states, inputs and perturbations is designed, using a Takagi-Sugeno (T-S) approach with unknown premise variables. Tools of the linear automatic to the nonlinear systems are applied, using the Linear Matrix Inequalities optimization. The observer estimates the uncertainties, the states and minimizes the effect of external disturbances on the estimation error. The uncertainties are modelled in a polynomial way which allows considering the uncertainty estimation as a fault detection problem. The residual sensitivity to faults while maintaining robustness according to a noise signal is handled by H?/H- approach. The method performance is illustrated using the three-tank system. PMID:25677711

  17. Optimal Sensor Allocation for Fault Detection and Isolation

    NASA Technical Reports Server (NTRS)

    Azam, Mohammad; Pattipati, Krishna; Patterson-Hine, Ann

    2004-01-01

    Automatic fault diagnostic schemes rely on various types of sensors (e.g., temperature, pressure, vibration, 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 fault 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 fault diagnosis (MFD) and optimization techniques for optimal sensor placement for fault detection and isolation (FDI) in complex systems. Keywords: sensor allocation, multiple fault diagnosis, Lagrangian relaxation, approximate belief revision, multidimensional knapsack problem.

  18. Recovery of vibration signal based on a super-exponential algorithm

    NASA Astrophysics Data System (ADS)

    Li, Yujun; Tse, Peter W.; Wang, Xiaojuan

    2008-03-01

    Vibration-based analysis is an important technique in machine fault diagnosis. In complex machines, the vibration generated by a component is easily affected by the vibration of other components or is corrupted by noise from other sources. Hence, the fault-related vibration must be recovered from among those sources for accurate diagnosis. In this paper, a super-exponential algorithm (SEA) based on the single-input single-output (SISO) convolution mixing model is investigated to deconvolve the vibration signal of interest. Based on simulated vibration signals generated by a test bench in a laboratory and real signals generated by industrial machines, the characteristics of the SEA with skewness and kurtosis schemes are studied extensively for the recovery of signals with different statistical distributions. It is shown that the SEA with the skewness scheme is more suitable for the isolation of a vibration signal with an asymmetric distribution, whereas the SEA with the kurtosis scheme is more efficient for detecting a vibration with abrupt changes in its distribution. Furthermore, the effect of the equalizer length on the characteristics of the equalized signal is discussed. A performance curve that presents the equalization performance is defined, and a method that uses this curve to select the optimal equalizer length for successfully recovering the defect signal from the mixed signal is then proposed. The study presented in this paper thus shows that the SEA is a promising method for the recovery of vibrations in machine fault diagnosis.

  19. 160 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART A: SYSTEMS AND HUMANS, VOL. 39, NO. 1, JANUARY 2009 Dynamic Multiple Fault Diagnosis: Mathematical

    E-print Network

    Prokhorov, Danil

    multiple fault diagnosis (DMFD) problems in the presence of imperfect test outcomes. The DMFD problem Namburu, Shunsuke Chigusa Sean, Danil V. Prokhorov, and Liu Qiao Abstract--Imperfect test outcomes, due to factors such as un- reliable sensors, electromagnetic interference, and environmental conditions, manifest

  20. A flight expert system for on-board fault monitoring and diagnosis

    NASA Technical Reports Server (NTRS)

    Ali, Moonis

    1990-01-01

    An architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing, and recovering from inflight faults is described. A prototype was implemented and an attempt was made to automate the knowledge acquisition process by employing a learning by being told methodology. The scope of acquired knowledge ranges from domain knowledge, including the information about objects and their relationships, to the procedural knowledge associated with the functionality of the mechanisms. AKAS (automatic knowledge acquisition system) is the constructed prototype for demonstration proof of concept, in which the expert directly interfaces with the knowledge acquisition system to ultimately construct the knowledge base for the particular application. The expert talks directly to the system using a natural language restricted only by the extent of the definitions in an analyzer dictionary, i.e., the interface understands a subset of concepts related to a given domain. In this case, the domain is the electrical system of the Boeing 737. Efforts were made to define and employ heuristics as well as algorithmic rules to conceptualize data produced by normal and faulty jet engine behavior examples. These rules were employed in developing the machine learning system (MLS). The input to MLS is examples which contain data of normal and faulty engine behavior and which are obtained from an engine simulation program. MLS first transforms the data into discrete selectors. Partial descriptions formed by those selectors are then generalized or specialized to generate concept descriptions about faults. The concepts are represented in the form of characteristic and discriminant descriptions, which are stored in the knowledge base and are employed to diagnose faults. MLS was successfully tested on jet engine examples.

  1. Optimization of Fault Detection/Diagnosis Model for Thermal Storage System Using AIC 

    E-print Network

    Pan, S.; Zheng, M.; Nakahara, N.

    2006-01-01

    -distance was used, have been reported. In the present paper, a new method for optimal model selection based on AIC(Akaike In-formation Criteria) is examined. In addition, by exam-ining the influence to the distinction and diagnosis rate of the optimal detection...

  2. The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

    NASA Technical Reports Server (NTRS)

    Ricks, Brian W.; Mengshoel, Ole J.

    2009-01-01

    Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.

  3. A quantum annealing approach for fault detection and diagnosis of graph-based systems

    NASA Astrophysics Data System (ADS)

    Perdomo-Ortiz, A.; Fluegemann, J.; Narasimhan, S.; Biswas, R.; Smelyanskiy, V. N.

    2015-02-01

    Diagnosing the minimal set of faults capable of explaining a set of given observations, e.g., from sensor readouts, is a hard combinatorial optimization problem usually tackled with artificial intelligence techniques. We present the mapping of this combinatorial problem to quadratic unconstrained binary optimization (QUBO), and the experimental results of instances embedded onto a quantum annealing device with 509 quantum bits. Besides being the first time a quantum approach has been proposed for problems in the advanced diagnostics community, to the best of our knowledge this work is also the first research utilizing the route Problem ? QUBO ? Direct embedding into quantum hardware, where we are able to implement and tackle problem instances with sizes that go beyond previously reported toy-model proof-of-principle quantum annealing implementations; this is a significant leap in the solution of problems via direct-embedding adiabatic quantum optimization. We discuss some of the programmability challenges in the current generation of the quantum device as well as a few possible ways to extend this work to more complex arbitrary network graphs.

  4. Incipient fault diagnosis of power transformers using optical spectro-photometric technique

    NASA Astrophysics Data System (ADS)

    Hussain, K.; Karmakar, Subrata

    2015-06-01

    Power transformers are the vital equipment in the network of power generation, transmission and distribution. Mineral oil in oil-filled transformers plays very important role as far as electrical insulation for the winding and cooling of the transformer is concerned. As transformers are always under the influence of electrical and thermal stresses, incipient faults like partial discharge, sparking and arcing take place. As a result, mineral oil deteriorates there by premature failure of the transformer occurs causing huge losses in terms of revenue and assets. Therefore, the transformer health condition has to be monitored continuously. The Dissolved Gas Analysis (DGA) is being extensively used for this purpose, but it has some drawbacks like it needs carrier gas, regular instrument calibration, etc. To overcome these drawbacks, Ultraviolet (UV) -Visible and Fourier Transform Infrared (FTIR) Spectro-photometric techniques are used as diagnostic tools for investigating the degraded transformer oil affected by electrical, mechanical and thermal stresses. The technique has several advantages over the conventional DGA technique.

  5. Detection and diagnosis of faults and energy monitoring of HVAC systems with least-intrusive power analysis

    E-print Network

    Luo, Dong, 1966-

    2001-01-01

    Faults indicate degradation or sudden failure of equipment in a system. Widely existing in heating, ventilating, and air conditioning (HVAC) systems, faults always lead to inefficient energy consumption, undesirable indoor ...

  6. Sensors and systems for space applications: a methodology for developing fault detection, diagnosis, and recovery

    NASA Astrophysics Data System (ADS)

    Edwards, John L.; Beekman, Randy M.; Buchanan, David B.; Farner, Scott; Gershzohn, Gary R.; Khuzadi, Mbuyi; Mikula, D. F.; Nissen, Gerry; Peck, James; Taylor, Shaun

    2007-04-01

    Human space travel is inherently dangerous. Hazardous conditions will exist. Real time health monitoring of critical subsystems is essential for providing a safe abort timeline in the event of a catastrophic subsystem failure. In this paper, we discuss a practical and cost effective process for developing critical subsystem failure detection, diagnosis and response (FDDR). We also present the results of a real time health monitoring simulation of a propellant ullage pressurization subsystem failure. The health monitoring development process identifies hazards, isolates hazard causes, defines software partitioning requirements and quantifies software algorithm development. The process provides a means to establish the number and placement of sensors necessary to provide real time health monitoring. We discuss how health monitoring software tracks subsystem control commands, interprets off-nominal operational sensor data, predicts failure propagation timelines, corroborate failures predictions and formats failure protocol.

  7. A novel method for high-performance fault detection of induction machine

    NASA Astrophysics Data System (ADS)

    Su, Hua; Kim, Yeong-Min; Chong, Kil To

    2005-12-01

    Induction machine is probably the most commonly utilized electromechanical device in modern society. However, there are many undesirable problems arising in the machine operation of industrial plants. It is desirable for early detection and diagnosis of incipient faults for online condition monitoring, product quality assurance, and improved operational efficiency of induction motors. In this paper, a high-performance residual-based novel method is developed for induction machine fault detection, using Fourier-based signal processing for steady-state vibration signals. The proposed approach uses only motor vibration measurements without the nameplate information. The reference model in spectra is obtained statistically to represent the healthy condition. The effectiveness of the proposed approach in detecting a wide range of mechanical and electrical faults is demonstrated through staged motor faults, and it is shown that a robust and reliable induction machine fault detection system has been produced.

  8. Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis.

    PubMed

    Talhaoui, Hicham; Menacer, Arezki; Kessal, Abdelhalim; Kechida, Ridha

    2014-09-01

    This paper presents new techniques to evaluate faults in case of broken rotor bars of induction motors. Procedures are applied with closed-loop control. Electrical and mechanical variables are treated using fast Fourier transform (FFT), and discrete wavelet transform (DWT) at start-up and steady state. The wavelet transform has proven to be an excellent mathematical tool for the detection of the faults particularly broken rotor bars type. As a performance, DWT can provide a local representation of the non-stationary current signals for the healthy machine and with fault. For sensorless control, a Luenberger observer is applied; the estimation rotor speed is analyzed; the effect of the faults in the speed pulsation is compensated; a quadratic current appears and used for fault detection. PMID:25004798

  9. Use of the continuous wavelet tranform to enhance early diagnosis of incipient faults in rotating element bearings 

    E-print Network

    Weatherwax, Scott Eric

    2009-05-15

    This thesis focused on developing a new wavelet for use with the continuous wavelet transform, a new detection method and two de-noising algorithms for rolling element bearing fault signals. The work is based on the continuous wavelet transform...

  10. Demagnetization fault diagnosis in permanent magnet synchronous motors: A review of the state-of-the-art

    NASA Astrophysics Data System (ADS)

    Moosavi, S. S.; Djerdir, A.; Amirat, Y. Ait.; Khaburi, D. A.

    2015-10-01

    There are a lot of research activities on developing techniques to detect permanent magnet (PM) demagnetization faults (DF). These faults decrease the performance, the reliability and the efficiency of permanent magnet synchronous motor (PMSM) drive systems. In this work, we draw a broad perspective on the status of these studies. The advantages, disadvantages of each method, a deeper view investigated and a comprehensive list of references are reported.

  11. A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis

    PubMed Central

    Shen, Changqing; Liu, Fang; Wang, Dong; Zhang, Ao; Kong, Fanrang; Tse, Peter W.

    2013-01-01

    The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully. PMID:24253191

  12. Experimental Fault Diagnosis in Systems Containing Finite Elements of Plate of Kirchoff by Using State Observers Methodology

    NASA Astrophysics Data System (ADS)

    Alegre, D. M.; Koroishi, E. H.; Melo, G. P.

    2015-07-01

    This paper presents a methodology for detection and localization of faults by using state observers. State Observers can rebuild the states not measured or values from points of difficult access in the system. So faults can be detected in these points without the knowledge of its measures, and can be track by the reconstructions of their states. In this paper this methodology will be applied in a system which represents a simplified model of a vehicle. In this model the chassis of the car was represented by a flat plate, which was divided in finite elements of plate (plate of Kirchoff), in addition, was considered the car suspension (springs and dampers). A test rig was built and the developed methodology was used to detect and locate faults on this system. In analyses done, the idea is to use a system with a specific fault, and then use the state observers to locate it, checking on a quantitative variation of the parameter of the system which caused this crash. For the computational simulations the software MATLAB was used.

  13. Flight elements: Fault detection and fault management

    NASA Technical Reports Server (NTRS)

    Lum, H.; Patterson-Hine, A.; Edge, J. T.; Lawler, D.

    1990-01-01

    Fault 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; fault 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 fault detection and management system will be accomplished via the implementation of several objectives, which are: Development of fault tolerant/FDIR requirement and specification from a systems level which will carry through from conceptual design through implementation and mission operations; Implementation of monitoring, diagnosis, and reconfiguration at all system levels providing fault 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 fault detection and fault management system and an information management system.

  14. An architecture for the development of real-time fault diagnosis systems using model-based reasoning

    NASA Technical Reports Server (NTRS)

    Hall, Gardiner A.; Schuetzle, James; Lavallee, David; Gupta, Uday

    1992-01-01

    Presented here is an architecture for implementing real-time telemetry based diagnostic systems using model-based reasoning. First, we describe Paragon, a knowledge acquisition tool for offline entry and validation of physical system models. Paragon provides domain experts with a structured editing capability to capture the physical component's structure, behavior, and causal relationships. We next describe the architecture of the run time diagnostic system. The diagnostic system, written entirely in Ada, uses the behavioral model developed offline by Paragon to simulate expected component states as reflected in the telemetry stream. The diagnostic algorithm traces causal relationships contained within the model to isolate system faults. Since the diagnostic process relies exclusively on the behavioral model and is implemented without the use of heuristic rules, it can be used to isolate unpredicted faults in a wide variety of systems. Finally, we discuss the implementation of a prototype system constructed using this technique for diagnosing faults in a science instrument. The prototype demonstrates the use of model-based reasoning to develop maintainable systems with greater diagnostic capabilities at a lower cost.

  15. Fault Diagnosis approach based on a model-based reasoner and a functional designer for a wind turbine. An approach towards self-maintenance

    NASA Astrophysics Data System (ADS)

    Echavarria, E.; Tomiyama, T.; van Bussel, G. J. W.

    2007-07-01

    The objective of this on-going research is to develop a design methodology to increase the availability for offshore wind farms, by means of an intelligent maintenance system capable of responding to faults by reconfiguring the system or subsystems, without increasing service visits, complexity, or costs. The idea is to make use of the existing functional redundancies within the system and sub-systems to keep the wind turbine operational, even at a reduced capacity if necessary. Re-configuration is intended to be a built-in capability to be used as a repair strategy, based on these existing functionalities provided by the components. The possible solutions can range from using information from adjacent wind turbines, such as wind speed and direction, to setting up different operational modes, for instance re-wiring, re-connecting, changing parameters or control strategy. The methodology described in this paper is based on qualitative physics and consists of a fault diagnosis system based on a model-based reasoner (MBR), and on a functional redundancy designer (FRD). Both design tools make use of a function-behaviour-state (FBS) model. A design methodology based on the re-configuration concept to achieve self-maintained wind turbines is an interesting and promising approach to reduce stoppage rate, failure events, maintenance visits, and to maintain energy output possibly at reduced rate until the next scheduled maintenance.

  16. Robust Diagnosis Method Based on Parameter Estimation for an Interturn Short-Circuit Fault in Multipole PMSM under High-Speed Operation.

    PubMed

    Lee, Jewon; Moon, Seokbae; Jeong, Hyeyun; Kim, Sang Woo

    2015-01-01

    This paper proposes a diagnosis method for a multipole permanent magnet synchronous motor (PMSM) under an interturn short circuit fault. Previous works in this area have suffered from the uncertainties of the PMSM parameters, which can lead to misdiagnosis. The proposed method estimates the q-axis inductance (Lq) of the faulty PMSM to solve this problem. The proposed method also estimates the faulty phase and the value of G, which serves as an index of the severity of the fault. The q-axis current is used to estimate the faulty phase, the values of G and Lq. For this reason, two open-loop observers and an optimization method based on a particle-swarm are implemented. The q-axis current of a healthy PMSM is estimated by the open-loop observer with the parameters of a healthy PMSM. The Lq estimation significantly compensates for the estimation errors in high-speed operation. The experimental results demonstrate that the proposed method can estimate the faulty phase, G, and Lq besides exhibiting robustness against parameter uncertainties. PMID:26610507

  17. Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions

    NASA Astrophysics Data System (ADS)

    Ruiz-Cárcel, C.; Jaramillo, V. H.; Mba, D.; Ottewill, J. R.; Cao, Y.

    2016-01-01

    The detection and diagnosis of faults 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 vibration analysis or other specific techniques. Conventional vibration-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 vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions.

  18. An SVM-Based Solution for Fault Detection in Wind Turbines

    PubMed Central

    Santos, Pedro; Villa, Luisa F.; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

    2015-01-01

    Research into fault diagnosis 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 diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis 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 vibration 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 fault 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

  19. An SVM-based solution for fault detection in wind turbines.

    PubMed

    Santos, Pedro; Villa, Luisa F; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

    2015-01-01

    Research into fault diagnosis 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 diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis 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 vibration 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 fault 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

  20. Study on the non-contact FBG vibration sensor and its application

    NASA Astrophysics Data System (ADS)

    Li, Tianliang; Tan, Yuegang; Zhou, Zude; Cai, Li; Liu, Sai; He, Zhongting; Zheng, Kai

    2015-06-01

    A non-contact vibration sensor based on the fiber Bragg grating (FBG) sensor has been presented, and it is used to monitor the vibration 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/?m and 4.11% within a measuring range of 2 mm-2.6 mm, respectively. When it is used to monitor the vibration of the rotating shaft, the analysis signals of vibration 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 vibration of the rotating shaft system and for fault monitoring and diagnosis of rotating machinery.

  1. Tacholess Envelope Order Analysis and Its Application to Fault Detection of Rolling Element Bearings with Varying Speeds

    PubMed Central

    Zhao, Ming; Lin, Jing; Xu, Xiaoqiang; Lei, Yaguo

    2013-01-01

    Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of rolling element bearings. Conventional diagnostic methods are based on the stationary assumption, thus they are not applicable to the diagnosis of bearings working under varying speed. This constraint limits the bearing diagnosis to the industrial application significantly. In order to extend the conventional diagnostic methods to speed variation cases, a tacholess envelope order analysis technique is proposed in this paper. In the proposed technique, a tacholess order tracking (TLOT) method is first introduced to extract the tachometer information from the vibration signal itself. On this basis, an envelope order spectrum (EOS) is utilized to recover the bearing characteristic frequencies in the order domain. By combining the advantages of TLOT and EOS, the proposed technique is capable of detecting bearing faults under varying speeds, even without the use of a tachometer. The effectiveness of the proposed method is demonstrated by both simulated signals and real vibration signals collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Analyzed results show that the proposed method could identify different bearing faults effectively and accurately under speed varying conditions. PMID:23959244

  2. Study on Unified Chaotic System-Based Wind Turbine Blade Fault Diagnostic System

    NASA Astrophysics Data System (ADS)

    Kuo, Ying-Che; Hsieh, Chin-Tsung; Yau, Her-Terng; Li, Yu-Chung

    At present, vibration 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 vibration signal to the unified chaotic system, applying the dynamic error to analyze the wind turbine vibration signal, and adopting extenics theory for artificial intelligent fault diagnosis of the analysis signal. Hence, a fault 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 vibration signal analysis. Thus, the operating conditions of wind turbines can be quickly known from this fault diagnostic system, and the maintenance schedule can be arranged before the faults worsen, making the management and implementation of wind turbines smoother, so as to reduce many unnecessary costs.

  3. Discriminant diffusion maps analysis: A robust manifold learner for dimensionality reduction and its applications in machine condition monitoring and fault diagnosis

    NASA Astrophysics Data System (ADS)

    Huang, Yixiang; Zha, Xuan F.; Lee, Jay; Liu, Chengliang

    2013-01-01

    Various features extracted from raw signals usually contain a large amount of redundant information which may impede the practical applications of machine condition monitoring and fault diagnosis. Hence, as a solution, dimensionality reduction is vital for machine condition monitoring. This paper presents a new technique for dimensionality reduction called the discriminant diffusion maps analysis (DDMA), which is implemented by integrating a discriminant kernel scheme into the framework of the diffusion maps. The effectiveness and robustness of DDMA are verified in three different experiments, including a pneumatic pressure regulator experiment, a rolling element bearing test, and an artificial noisy nonlinear test system, with empirical comparisons with both the linear and nonlinear methods of dimensionality reduction, such as principle components analysis (PCA), independent components analysis (ICA), linear discriminant analysis (LDA), kernel PCA, self-organizing maps (SOM), ISOMAP, diffusion maps (DM), Laplacian eigenmaps (LE), locally linear embedding (LLE) analysis, Hessian-based LLE analysis, and local tangent space alignment analysis (LTSA). Results show that DDMA is capable of effectively representing the high-dimensional data in a lower dimensional space while retaining most useful information. In addition, the low-dimensional features generated by DDMA are much better than those generated by most of other state-of-the-art techniques in different situations.

  4. Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

    NASA Technical Reports Server (NTRS)

    Mengshoel, Ole Jakob; Poll, Scott; Kurtoglu, Tolga

    2009-01-01

    In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specifically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. (See CASI ID 20100021910 for supplemental data disk.)

  5. Fault management for data systems

    NASA Technical Reports Server (NTRS)

    Boyd, Mark A.; Iverson, David L.; Patterson-Hine, F. Ann

    1993-01-01

    Issues related to automating the process of fault management (fault diagnosis 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 fault management system is advocated. The general problem is described and the motivation behind choosing graph-based models over other approaches for developing fault diagnosis computer programs is outlined. Some existing work in the area of graph-based fault diagnosis is reviewed, and a new fault 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.

  6. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system

    NASA Astrophysics Data System (ADS)

    Wang, Yanxue; Markert, Richard; Xiang, Jiawei; Zheng, Weiguang

    2015-08-01

    Multi-component extraction is an available method for vibration signal analysis of rotary machinery, so a novel method of rubbing fault diagnosis based on variational mode decomposition (VMD) is proposed. VMD is a newly developed technique for adaptive signal decomposition, which can non-recursively decompose a multi-component signal into a number of quasi-orthogonal intrinsic mode functions. The equivalent filtering characteristics of VMD are investigated, and the behavior of wavelet packet-like expansion is first found based on fractional Gaussian noise via numerical simulations. VMD is then applied to detect multiple rubbing-caused signatures for rotor-stator fault diagnosis via numerical simulated response signal and practical vibration signal. A comparison has also been conducted to evaluate the effectiveness of identifying the rubbing-caused signatures by using VMD, empirical wavelet transform (EWT), EEMD and EMD. The analysis results of the rubbing signals show that the multiple features can be better extracted with the VMD, simultaneously.

  7. Efficient Fault Tolerance: an Approach to Deal with Transient Faults in Multiprocessor Architectures

    E-print Network

    Firenze, Università degli Studi di

    Efficient Fault Tolerance: an Approach to Deal with Transient Faults in Multiprocessor be integrated with a fault treatment approach aiming at op- timising resource utilisation. In this paper we propose a diagnosis approach that, accounting for transient faults, tries to remove units very cautiously

  8. Improving Multiple Fault Diagnosability using Possible Conflicts

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew J.; Bregon, Anibal; Biswas, Gautam; Koutsoukos, Xenofon; Pulido, Belarmino

    2012-01-01

    Multiple fault diagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults 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.

  9. Faulted Barn

    USGS Multimedia Gallery

    This barn is faulted through the middle; the moletrack is seen in the foreground with the viewer standing on the fault. From the air one can see metal roof panels of the barn that rotated as the barn was faulted....

  10. Application of higher order spectral features and support vector machines for bearing faults classification.

    PubMed

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

    2015-01-01

    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals. PMID:25282095

  11. Fault diagnosability of arrangement graphs Shuming Zhou a,b,

    E-print Network

    Xu, Jun-Ming

    Fault diagnosability of arrangement graphs Shuming Zhou a,b, , Jun-Ming Xu c a School 27 April 2013 Available online 31 May 2013 Keywords: Fault tolerance Comparison diagnosis to maintain a system's high reliability. The fault diagnosis is the process of identifying faulty processors

  12. Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks.

    PubMed

    Lashkari, Negin; Poshtan, Javad; Azgomi, Hamid Fekri

    2015-11-01

    The three-phase shift between line current and phase voltage of induction motors can be used as an efficient fault indicator to detect and locate inter-turn stator short-circuit (ITSC) fault. However, unbalanced supply voltage is one of the contributing factors that inevitably affect stator currents and therefore the three-phase shift. Thus, it is necessary to propose a method that is able to identify whether the unbalance of three currents is caused by ITSC or supply voltage fault. This paper presents a feedforward multilayer-perceptron Neural Network (NN) trained by back propagation, based on monitoring negative sequence voltage and the three-phase shift. The data which are required for training and test NN are generated using simulated model of stator. The experimental results are presented to verify the superior accuracy of the proposed method. PMID:26412499

  13. Fault management Fault management

    E-print Network

    Giaccone, Paolo

    degradation 1 3% Natural events 1 3% Hard Failures: service interruption Network Management and Qo in the event of a failure causing few or no Network Management and QoS Provisioning - 7Andrea Bianco ­ TNGFault management Pag. 1 Fault management Network Management and QoS Provisioning - 1Andrea Bianco

  14. Improved automated diagnosis of misfire in internal combustion engines based on simulation models

    NASA Astrophysics Data System (ADS)

    Chen, Jian; Bond Randall, Robert

    2015-12-01

    In this paper, a new advance in the application of Artificial Neural Networks (ANNs) to the automated diagnosis of misfires in Internal Combustion engines(IC engines) is detailed. The automated diagnostic system comprises three stages: fault detection, fault localization and fault severity identification. Particularly, in the severity identification stage, separate Multi-Layer Perceptron networks (MLPs) with saturating linear transfer functions were designed for individual speed conditions, so they could achieve finer classification. In order to obtain sufficient data for the network training, numerical simulation was used to simulate different ranges of misfires in the engine. The simulation models need to be updated and evaluated using experimental data, so a series of experiments were first carried out on the engine test rig to capture the vibration signals for both normal condition and with a range of misfires. Two methods were used for the misfire diagnosis: one is based on the torsional vibration signals of the crankshaft and the other on the angular acceleration signals (rotational motion) of the engine block. Following the signal processing of the experimental and simulation signals, the best features were selected as the inputs to ANN networks. The ANN systems were trained using only the simulated data and tested using real experimental cases, indicating that the simulation model can be used for a wider range of faults for which it can still be considered valid. The final results have shown that the diagnostic system based on simulation can efficiently diagnose misfire, including location and severity.

  15. Evaluation of a Decoupling-Based Fault Detection and Diagnostic Technique - Part I: Field Emulation Evaluation 

    E-print Network

    Li, H.; Braun, J.

    2006-01-01

    Existing methods addressing automated fault detection and diagnosis (FDD) for vapor compression air conditioning system have good performance for faults that occur individually, but they have difficulty in handling multiple-simultaneous faults...

  16. Vibration manual

    NASA Technical Reports Server (NTRS)

    Green, C.

    1971-01-01

    Guidelines of the methods and applications used in vibration technology at the MSFC are presented. The purpose of the guidelines is to provide a practical tool for coordination and understanding between industry and government groups concerned with vibration of systems and equipments. Topics covered include measuring, reducing, analyzing, and methods for obtaining simulated environments and formulating vibration specifications. Methods for vibration and shock testing, theoretical aspects of data processing, vibration response analysis, and techniques of designing for vibration are also presented.

  17. Fault finder

    DOEpatents

    Bunch, Richard H. (1614 NW. 106th St., Vancouver, WA 98665)

    1986-01-01

    A fault finder for locating faults along a high voltage electrical transmission line. Real time monitoring of background noise and improved filtering of input signals is used to identify the occurrence of a fault. A fault is detected at both a master and remote unit spaced along the line. A master clock synchronizes operation of a similar clock at the remote unit. Both units include modulator and demodulator circuits for transmission of clock signals and data. All data is received at the master unit for processing to determine an accurate fault distance calculation.

  18. Analysis of electrical signatures in synchronous generators characterized by bearing faults 

    E-print Network

    Choi, Jae-Won

    2009-05-15

    Synchronous generators play a vital role in power systems. One of the major mechanical faults in synchronous generators is related to bearings. The popular vibration analysis method has been utilized to detect bearing faults for years. However...

  19. Multiple Fault Isolation in Redundant Systems

    NASA Technical Reports Server (NTRS)

    Pattipati, Krishna R.; Patterson-Hine, Ann; Iverson, David

    1997-01-01

    Fault diagnosis 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 fault-tolerant systems and systems with infrequent opportunity for maintenance (e.g., Hubble telescope, space station), the assumption of at most a single fault in the system is unrealistic. In this project, we have developed novel block and sequential diagnostic strategies to isolate multiple faults in the shortest possible time without making the unrealistic single fault assumption.

  20. Multiple Fault Isolation in Redundant Systems

    NASA Technical Reports Server (NTRS)

    Pattipati, Krishna R.

    1997-01-01

    Fault diagnosis 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 fault-tolerant systems and systems with infrequent opportunity for maintenance (e.g., Hubble telescope, space station), the assumption of at most a single fault in the system is unrealistic. In this project, we have developed novel block and sequential diagnostic strategies to isolate multiple faults in the shortest possible time without making the unrealistic single fault assumption.

  1. Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

    PubMed Central

    Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu

    2012-01-01

    Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault 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, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault 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 vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017

  2. Automatic translation of digraph to fault-tree models

    NASA Technical Reports Server (NTRS)

    Iverson, David L.

    1992-01-01

    The author presents a technique for converting digraph models, including those models containing cycles, to a fault-tree format. A computer program which automatically performs this translation using an object-oriented representation of the models has been developed. The fault-trees resulting from translations can be used for fault-tree analysis and diagnosis. Programs to calculate fault-tree and digraph cut sets and perform diagnosis with fault-tree models have also been developed. The digraph to fault-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. Fault-trees produced by the translator have been successfully used with NASA's Fault-Tree Diagnosis System (FTDS) to produce automated diagnostic systems.

  3. Evaluation of a Decoupling-Based Fault Detection and Diagnostic Technique - Part II: Field Evaluation and Application 

    E-print Network

    Li, H.; Braun, J.

    2006-01-01

    Existing methods addressing automated fault detection and diagnosis (FDD) for vapor compression air conditioning system have good performance for faults that occur individually, but they have difficulty in handling multiple-simultaneous faults...

  4. Novel neural networks-based fault tolerant control scheme with fault alarm.

    PubMed

    Shen, Qikun; Jiang, Bin; Shi, Peng; Lim, Cheng-Chew

    2014-11-01

    In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques. PMID:25014982

  5. Machine learning techniques for fault isolation and sensor placement

    NASA Technical Reports Server (NTRS)

    Carnes, James R.; Fisher, Douglas H.

    1993-01-01

    Fault isolation and sensor placement are vital for monitoring and diagnosis. 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 fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance.

  6. Model-based fault localization in bottling plants Tobias Voigt a,

    E-print Network

    Cengarle, María Victoria

    Model-based fault localization in bottling plants Tobias Voigt a, , Stefan Flad a , Peter Struss b-based fault localization Automatic fault diagnosis Consistency-based diagnosis Bottling plant Packaging line a b s t r a c t The bottling of beverages is carried out in complex plants that consist of several

  7. JOURNAL OF TNNLS 1 A Self-building and Cluster-based Cognitive Fault

    E-print Network

    Alippi, Cesare

    JOURNAL OF TNNLS 1 A Self-building and Cluster-based Cognitive Fault Diagnosis System for Sensor Abstract--Cognitive fault diagnosis systems differentiate from more traditional solutions by providing online strategies to create and update the fault-free and the faulty classes directly from incoming data

  8. Fault mechanics

    SciTech Connect

    Segall, P. )

    1991-01-01

    Recent observational, experimental, and theoretical modeling studies of fault mechanics are discussed in a critical review of U.S. research from the period 1987-1990. Topics examined include interseismic strain accumulation, coseismic deformation, postseismic deformation, and the earthquake cycle; long-term deformation; fault friction and the instability mechanism; pore pressure and normal stress effects; instability models; strain measurements prior to earthquakes; stochastic modeling of earthquakes; and deep-focus earthquakes. Maps, graphs, and a comprehensive bibliography are provided. 220 refs.

  9. Application of fault location system in power substations

    SciTech Connect

    Mukaiyama, Y.; Mizuno, K.; Horide, A. ); Sakakibara, T.; Maruyama, S.; Kamata, I.; Shinoda, A. )

    1993-01-01

    Relating to GIS (gas-insulated switchgear) fault location sensors and systems, this paper describes a series of development tests carried out when developing sensors for detecting pressure waves, pressure rises, arc lights, tank vibrations, and ground wire currents caused by fault current arcs, while presenting methods for applying and actual applications of systems.

  10. Maneuver Classification for Aircraft Fault Detection

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.

    2003-01-01

    Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault 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 fault detection in aircraft, identifying all possible faulty and proper operating modes is clearly impossible. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration 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 fault detection system as well as experiments showing the promise of this classification subsystem.

  11. Classification of Aircraft Maneuvers for Fault Detection

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Koga, Dennis (Technical Monitor)

    2002-01-01

    Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault 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 fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration 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 fault detection system as well as experiments showing the promise of this classification subsystem.

  12. Classification of Aircraft Maneuvers for Fault Detection

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Clancy, Daniel (Technical Monitor)

    2002-01-01

    Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault 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 fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. We explain where this subsystem fits into our envisioned fault detection system as well its experiments showing the promise of this classification subsystem.

  13. New technology for America`s electric power industry. Diagnosis and control of flow-induced tube vibration in heat exchangers and steam generators

    SciTech Connect

    1995-03-01

    At present, it is not possible to predict the coupled tube/fluid interaction using the fundamental principles of fluid dynamics. Argonne has developed advances in applications of turbulent fluid flow theory to provide an alternate pathway for resolving practical tube wear problems. In Argonne`s approach, the fluid forces that act on tube arrays in cross-flow are measured. These forces result from the individual motions of the tubes within the tube array. From the fluid forces, fluid damping and stiffness are quantified with respect to their association with coupled tube motion. A characterization of the fluid flow state is then established, based on the measured motion-dependent fluid forces. Design guides and diagnosis techniques can subsequently be provided, based on the flow characterization profile.

  14. Vibrational Coupling

    SciTech Connect

    2011-01-01

    By homing in on the distribution patterns of electrons around an atom, a team of scientists team with Berkeley Lab's Molecular Foundry showed how certain vibrations from benzene thiol cause electrical charge to "slosh" onto a gold surface (left), while others do not (right). The vibrations that cause this "sloshing" behavior yield a stronger SERS signal.

  15. Analysis of Remote Diagnosis Architecture for a PLCBbased Automated Assembly System 

    E-print Network

    Sekar, Ramnath

    2011-10-21

    frameworks have been proposed incorporating advancements such as automated fault diagnosis, collaborative diagnosis and mobile communication techniques. Standards exist for the capabilities representative of different levels of remote equipment diagnosis...

  16. A Primer on Architectural Level Fault Tolerance

    NASA Technical Reports Server (NTRS)

    Butler, Ricky W.

    2008-01-01

    This paper introduces the fundamental concepts of fault tolerant computing. Key topics covered are voting, fault detection, clock synchronization, Byzantine Agreement, diagnosis, and reliability analysis. Low level mechanisms such as Hamming codes or low level communications protocols are not covered. The paper is tutorial in nature and does not cover any topic in detail. The focus is on rationale and approach rather than detailed exposition.

  17. Sensor-Based Vibration Signal Feature Extraction Using an Improved Composite Dictionary Matching Pursuit Algorithm

    PubMed Central

    Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

    2014-01-01

    This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis 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 fault 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 vibration signals with noise. The simulation sensor-based vibration 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 vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective. PMID:25207870

  18. Exploring hypotheses in attitude control fault diagnosis

    NASA Technical Reports Server (NTRS)

    Bell, Benjamin

    1987-01-01

    A system which analyzes telemetry and evaluates hypotheses to explain any anomalies that are observed is described. Results achieved from a sample set of failure cases are presented, followed by a brief discussion of the benefits derived from this approach.

  19. Application of Phase Space Warping on Damage Tracking for Bearing Fault

    NASA Astrophysics Data System (ADS)

    Fan, Bin; Hu, Niaoqing; Hu, Lei; Gu, Fengshou

    2012-05-01

    Nowadays, the significance of keeping equipment function properly each time is obvious. If equipment fails during its use, it may have disastrous consequences. Estimating remaining useful life (RUL) of equipment is a key to prevent such calamities, improve its reliability, provide security and reduce unnecessary maintenance and operational cost. The evolution and tracking of damage is the foundation of RUL predicting, and also is one of the most important content of mechanical fault diagnosis. Slow-time variable process of mechanical damage would lead the phase space reconstructed by fast-time variable vibrate signals warping. Search the dynamics characteristic law of damage evolution analysis in the phase space, and build the relationship between fast-time variable signals and slow-time variable damage, and then damage evolution tracking is possible. To validate the theory, simulation model of bearing damage evolution is built, the outer-race fault evolution signals is obtained, and the trend of evolution of degradation of bearing fault is described with Phase Space Warping (PSW) theory and Smooth Orthogonal Decomposition (SOD). The results proved the feasibility of the methodology of PSW in damage evolution tracking.

  20. Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model

    PubMed Central

    Lu, Feng; Huang, Jinquan; Xing, Yaodong

    2012-01-01

    Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis 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 fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient. PMID:23112645

  1. Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

    PubMed

    Lu, Feng; Huang, Jinquan; Xing, Yaodong

    2012-01-01

    Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis 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 fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient. PMID:23112645

  2. Vibration sensors

    NASA Astrophysics Data System (ADS)

    Gupta, Amita; Singh, Ranvir; Ahmad, Amir; Kumar, Mahesh

    2003-10-01

    Today, vibration sensors with low and medium sensitivities are in great demand. Their applications include robotics, navigation, machine vibration monitoring, isolation of precision equipment & activation of safety systems e.g. airbags in automobiles. Vibration sensors have been developed at SSPL, using silicon micromachining to sense vibrations in a system in the 30 - 200 Hz frequency band. The sensing element in the silicon vibration sensor is a seismic mass suspended by thin silicon hinges mounted on a metallized glass plate forming a parallel plate capacitor. The movement of the seismic mass along the vertical axis is monitored to sense vibrations. This is obtained by measuring the change in capacitance. The movable plate of the parallel plate capacitor is formed by a block connected to a surrounding frame by four cantilever beams located on sides or corners of the seismic mass. This element is fabricated by silicon micromachining. Several sensors in the chip sizes 1.6 cm x 1.6 cm, 1 cm x 1 cm and 0.7 cm x 0.7 cm have been fabricated. Work done on these sensors, techniques used in processing and silicon to glass bonding are presented in the paper. Performance evaluation of these sensors is also discussed.

  3. Random Vibrations

    NASA Technical Reports Server (NTRS)

    Messaro. Semma; Harrison, Phillip

    2010-01-01

    Ares I Zonal Random vibration environments due to acoustic impingement and combustion processes are develop for liftoff, ascent and reentry. Random Vibration test criteria for Ares I Upper Stage pyrotechnic components are developed by enveloping the applicable zonal environments where each component is located. Random vibration tests will be conducted to assure that these components will survive and function appropriately after exposure to the expected vibration environments. Methodology: Random Vibration test criteria for Ares I Upper Stage pyrotechnic components were desired that would envelope all the applicable environments where each component was located. Applicable Ares I Vehicle drawings and design information needed to be assessed to determine the location(s) for each component on the Ares I Upper Stage. Design and test criteria needed to be developed by plotting and enveloping the applicable environments using Microsoft Excel Spreadsheet Software and documenting them in a report Using Microsoft Word Processing Software. Conclusion: Random vibration liftoff, ascent, and green run design & test criteria for the Upper Stage Pyrotechnic Components were developed by using Microsoft Excel to envelope zonal environments applicable to each component. Results were transferred from Excel into a report using Microsoft Word. After the report is reviewed and edited by my mentor it will be submitted for publication as an attachment to a memorandum. Pyrotechnic component designers will extract criteria from my report for incorporation into the design and test specifications for components. Eventually the hardware will be tested to the environments I developed to assure that the components will survive and function appropriately after exposure to the expected vibration environments.

  4. Shaft instantaneous angular speed for blade vibration in rotating machine

    NASA Astrophysics Data System (ADS)

    Gubran, Ahmed A.; Sinha, Jyoti K.

    2014-02-01

    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 vibration is often transmitted to the shaft as torsional vibration. The shaft instantaneous angular speed (IAS) is nothing but the representing the shaft torsional vibration. Hence the shaft IAS has been extracted from the measured encoder data during machine run-up to understand the blade vibration 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 faults simulation in the blades. The measured shaft torsional vibration 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 faults, experiments conducted, observations and results.

  5. Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew; Roychoudhury, Indranil

    2010-01-01

    We describe a diagnosis 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 diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults 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 faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

  6. Induction machine faults detection using stator current parametric spectral estimation

    NASA Astrophysics Data System (ADS)

    El Bouchikhi, El Houssin; Choqueuse, Vincent; Benbouzid, Mohamed

    2015-02-01

    Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detection criterion. Therefore, this paper proposes a new parametric spectral estimator that fully exploits the faults sensitive frequencies. The proposed technique is based on the maximum likelihood estimator (MLE) and offers high-resolution capabilities. Based on this approach, a fault criterion is derived for detecting several fault types. The proposed technique is assessed using simulation signals, issued from a coupled electromagnetic circuits approach-based simulation tool for mechanical unbalance and electrical asymmetry faults detection. It is afterward validated using experiments on a 0.75-kW induction machine test bed for the particular case of bearing faults.

  7. Fault Detection in Nonlinear Continuous-Time Systems with Uncertain Parameters

    E-print Network

    Stadtherr, Mark A.

    Fault Detection in Nonlinear Continuous-Time Systems with Uncertain Parameters Youdong Lin and Mark. E-mail: markst@nd.edu #12;Abstract In model-based fault diagnosis for dynamic systems with uncertain parameters, an envelope of all fault-free behaviors can be determined from the model and used as a reference

  8. On-line diagnosis of sequential systems

    NASA Technical Reports Server (NTRS)

    Sundstrom, R. J.

    1973-01-01

    A model for on-line diagnosis was investigated for discrete-time systems, and resettable sequential systems. Generalized notions of a realization are discussed along with fault tolerance and errors. Further investigation into the theory of on-line diagnosis is recommended for three levels: binary state-assigned level, logical circuit level, and the subsystem-network level.

  9. The micromechanics of fault gouge and dynamic earthquake triggering: investigation by Discrete Element Method numerical simulations

    E-print Network

    Daub,Eric G.

    The micromechanics of fault gouge and dynamic earthquake triggering: investigation by Discrete Element Method numerical simulations 4. Department of Physics, University of Nevada, Reno DYNAMIC EARTHQUAKE TRIGGERING ARE TRIGGERED EVENTS DIFFERENT? DISCRETE ELEMENT MODEL MODEL DYNAMICS VIBRATION AND NON

  10. Scabies Diagnosis

    MedlinePLUS

    ... CDC.gov . Parasites - Scabies Parasites Home Share Compartir Diagnosis Diagnosis of a scabies infestation usually is made based ... and the presence of burrows. Whenever possible, the diagnosis of scabies should be confirmed by identifying the ...

  11. Fault detection in programmable logic arrays

    NASA Astrophysics Data System (ADS)

    Somenzi, F.; Gai, S.

    1986-05-01

    When designing fault-tolerant systems including programmable logic arrays (PLAs), the various aspects of these circuits concerning fault diagnosis have to be taken into account. The peculiarity of these aspects, ranging from fault models to test generation algorithms and to self-checking structures, is due to the regularity of PLAs. The fault model generally accepted for PLAs is the crosspoint defect; it is employed by dedicated test generation algorithms, based on the fact that PLAs implement a two-level combinational function. The problem of accessing inputs and outputs of the PLA can be alleviated by augmenting the PLA itself so as to simplify the test vectors to be applied, making them function independent in the limit. A further step consists in the addition of the circuitry required to generate test vectors and to evaluate the answer, thus obtaining a built-in self-test (BIST) architecture. Finally, high reliability can be achieved with PLAs featuring concurrent error detection.

  12. Methodology for Designing Fault-Protection Software

    NASA Technical Reports Server (NTRS)

    Barltrop, Kevin; Levison, Jeffrey; Kan, Edwin

    2006-01-01

    A document describes a methodology for designing fault-protection (FP) software for autonomous spacecraft. The methodology embodies and extends established engineering practices in the technical discipline of Fault Detection, Diagnosis, Mitigation, and Recovery; and has been successfully implemented in the Deep Impact Spacecraft, a NASA Discovery mission. Based on established concepts of Fault Monitors and Responses, this FP methodology extends the notion of Opinion, Symptom, Alarm (aka Fault), and Response with numerous new notions, sub-notions, software constructs, and logic and timing gates. For example, Monitor generates a RawOpinion, which graduates into Opinion, categorized into no-opinion, acceptable, or unacceptable opinion. RaiseSymptom, ForceSymptom, and ClearSymptom govern the establishment and then mapping to an Alarm (aka Fault). Local Response is distinguished from FP System Response. A 1-to-n and n-to- 1 mapping is established among Monitors, Symptoms, and Responses. Responses are categorized by device versus by function. Responses operate in tiers, where the early tiers attempt to resolve the Fault in a localized step-by-step fashion, relegating more system-level response to later tier(s). Recovery actions are gated by epoch recovery timing, enabling strategy, urgency, MaxRetry gate, hardware availability, hazardous versus ordinary fault, and many other priority gates. This methodology is systematic, logical, and uses multiple linked tables, parameter files, and recovery command sequences. The credibility of the FP design is proven via a fault-tree analysis "top-down" approach, and a functional fault-mode-effects-and-analysis via "bottoms-up" approach. Via this process, the mitigation and recovery strategy(s) per Fault Containment Region scope (width versus depth) the FP architecture.

  13. KIM, SO YEON. Human and Machine Co-Investigate Intelligence System (HM-CII) for Fault Diagnosis and Detection in Complex Systems. (Under the direction of Dr. Simon M.

    E-print Network

    Kaber, David B.

    ABSTRACT KIM, SO YEON. Human and Machine Co-Investigate Intelligence System (HM-CII) for Fault-Investigate Intelligence System (HM-CII). Although HM-CII framework could be used widely where the human and machine of intelligence, human experts may not be able to read results based on these decision support systems easily

  14. Data Fault Detection in Medical Sensor Networks

    PubMed Central

    Yang, Yang; Liu, Qian; Gao, Zhipeng; Qiu, Xuesong; Meng, Luoming

    2015-01-01

    Medical body sensors can be implanted or attached to the human body to monitor the physiological parameters of patients all the time. Inaccurate data due to sensor faults or incorrect placement on the body will seriously influence clinicians’ diagnosis, therefore detecting sensor data faults has been widely researched in recent years. Most of the typical approaches to sensor fault detection in the medical area ignore the fact that the physiological indexes of patients aren’t changing synchronously at the same time, and fault values mixed with abnormal physiological data due to illness make it difficult to determine true faults. Based on these facts, we propose a Data Fault Detection mechanism in Medical sensor networks (DFD-M). Its mechanism includes: (1) use of a dynamic-local outlier factor (D-LOF) algorithm to identify outlying sensed data vectors; (2) use of a linear regression model based on trapezoidal fuzzy numbers to predict which readings in the outlying data vector are suspected to be faulty; (3) the proposal of a novel judgment criterion of fault state according to the prediction values. The simulation results demonstrate the efficiency and superiority of DFD-M. PMID:25774708

  15. Data fault detection in medical sensor networks.

    PubMed

    Yang, Yang; Liu, Qian; Gao, Zhipeng; Qiu, Xuesong; Meng, Luoming

    2015-01-01

    Medical body sensors can be implanted or attached to the human body to monitor the physiological parameters of patients all the time. Inaccurate data due to sensor faults or incorrect placement on the body will seriously influence clinicians' diagnosis, therefore detecting sensor data faults has been widely researched in recent years. Most of the typical approaches to sensor fault detection in the medical area ignore the fact that the physiological indexes of patients aren't changing synchronously at the same time, and fault values mixed with abnormal physiological data due to illness make it difficult to determine true faults. Based on these facts, we propose a Data Fault Detection mechanism in Medical sensor networks (DFD-M). Its mechanism includes: (1) use of a dynamic-local outlier factor (D-LOF) algorithm to identify outlying sensed data vectors; (2) use of a linear regression model based on trapezoidal fuzzy numbers to predict which readings in the outlying data vector are suspected to be faulty; (3) the proposal of a novel judgment criterion of fault state according to the prediction values. The simulation results demonstrate the efficiency and superiority of DFD-M. PMID:25774708

  16. 2011 Vibrations -1 VIBRATION OF PLATES & BARS

    E-print Network

    Gustafsson, Torgny

    2011 Vibrations - 1 VIBRATION OF PLATES & BARS The objectives of this experiment are: To observe and short flat bars, Chladni plates, salt, salt trays INTRODUCTION he equations of vibrational motion;2011 Vibrations - 2 2f 6.27 1f 3f 17.55 1f etc. Where y = transverse displacement of bar element x = distance

  17. Unbalanced Underground Distribution Systems Fault Detection and Section Estimation

    NASA Astrophysics Data System (ADS)

    de Oliveira, Karen Rezende Caino; Salim, Rodrigo Hartstein; Filomena, André Darós; Resener, Mariana; Bretas, Arturo Suman

    This paper presents a novel fault detection and section estimation method for unbalanced underground distribution systems (UDS). The method proposed is based on artificial neural networks (ANNs) and wavelet transforms (WTs). The majority of UDS are characterized by having several single/double phase laterals and non-symmetrical lines. Also, Digital Fourier Transforms (DFT), used in the majority of traditional protection relays, supplies a low level of robustness to the fault diagnosis process due to its inversely proportional time-frequency characteristic. These characteristics compromise the traditional fault diagnosis methods performance. ANNs are capable of learning and generalizing, whereas WTs are robust tools capable of evaluating a signal's frequency range that can characterize the fault phenomenon. This paper describes the proposed diagnosis method and discusses the results obtained from simulated implementation. The obtained results demonstrate the capability and robustness of the technique indicating its potential for on-line applications.

  18. Abstract-A fault detection and reconfiguration technique for a cascaded H-bridge 11-level inverter drives during faulty condition

    E-print Network

    Tolbert, Leon M.

    Abstract-A fault detection and reconfiguration technique for a cascaded H-bridge 11-level inverter can be used as a diagnostic signal to detect faults and their locations. AI-based techniques are used to perform the fault classification. A neural network (NN) classification is applied to the fault diagnosis

  19. Rapid detection of small oscillation faults via deterministic learning.

    PubMed

    Wang, Cong; Chen, Tianrui

    2011-08-01

    Detection of small faults is one of the most important and challenging tasks in the area of fault diagnosis. In this paper, we present an approach for the rapid detection of small oscillation faults based on a recently proposed deterministic learning (DL) theory. The approach consists of two phases: the training phase and the test phase. In the training phase, the system dynamics underlying normal and fault oscillations are locally accurately approximated through DL. The obtained knowledge of system dynamics is stored in constant radial basis function (RBF) networks. In the diagnosis phase, rapid detection is implemented. Specially, a bank of estimators are constructed using the constant RBF neural networks to represent the training normal and fault modes. By comparing the set of estimators with the test monitored system, a set of residuals are generated, and the average L(1) norms of the residuals are taken as the measure of the differences between the dynamics of the monitored system and the dynamics of the training normal mode and oscillation faults. The occurrence of a test oscillation fault can be rapidly detected according to the smallest residual principle. A rigorous analysis of the performance of the detection scheme is also given. The novelty of the paper lies in that the modeling uncertainty and nonlinear fault functions are accurately approximated and then the knowledge is utilized to achieve rapid detection of small oscillation faults. Simulation studies are included to demonstrate the effectiveness of the approach. PMID:21813356

  20. Summary: beyond fault trees to fault graphs

    SciTech Connect

    Alesso, H.P.; Prassinos, P.; Smith, C.F.

    1984-09-01

    Fault Graphs are the natural evolutionary step over a traditional fault-tree model. A Fault Graph is a failure-oriented directed graph with logic connectives that allows cycles. We intentionally construct the Fault Graph to trace the piping and instrumentation drawing (P and ID) of the system, but with logical AND and OR conditions added. Then we evaluate the Fault Graph with computer codes based on graph-theoretic methods. Fault Graph computer codes are based on graph concepts, such as path set (a set of nodes traveled on a path from one node to another) and reachability (the complete set of all possible paths between any two nodes). These codes are used to find the cut-sets (any minimal set of component failures that will fail the system) and to evaluate the system reliability.

  1. Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Roychoudhury, Indranil; Balaban, Edward; Saxena, Abhinav

    2010-01-01

    Model-based diagnosis 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 vibration 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 diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.

  2. An Ontology for Identifying Cyber Intrusion Induced Faults in Process Control Systems

    NASA Astrophysics Data System (ADS)

    Hieb, Jeffrey; Graham, James; Guan, Jian

    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 fault diagnosis 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 fault sources. When these elements are identified as a likely fault source, the possibility exists that the process fault 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 fault diagnosis approach provides cyber security enhanced fault diagnosis information to plant operators and can help identify that a cyber attack is underway before a major process failure is experienced.

  3. Fault Mapping in Haiti

    USGS Multimedia Gallery

    USGS geologist Carol Prentice surveying features that have been displaced by young movements on the Enriquillo fault in southwest Haiti.  The January 2010 Haiti earthquake was associated with the Enriquillo fault....

  4. Protecting Against Faults in JPL Spacecraft

    NASA Technical Reports Server (NTRS)

    Morgan, Paula

    2007-01-01

    A paper discusses techniques for protecting against faults in spacecraft designed and operated by NASA s Jet Propulsion Laboratory (JPL). The paper addresses, more specifically, fault-protection requirements and techniques common to most JPL spacecraft (in contradistinction to unique, mission specific techniques), standard practices in the implementation of these techniques, and fault-protection software architectures. Common requirements include those to protect onboard command, data-processing, and control computers; protect against loss of Earth/spacecraft radio communication; maintain safe temperatures; and recover from power overloads. The paper describes fault-protection techniques as part of a fault-management strategy that also includes functional redundancy, redundant hardware, and autonomous monitoring of (1) the operational and health statuses of spacecraft components, (2) temperatures inside and outside the spacecraft, and (3) allocation of power. The strategy also provides for preprogrammed automated responses to anomalous conditions. In addition, the software running in almost every JPL spacecraft incorporates a general-purpose "Safe Mode" response algorithm that configures the spacecraft in a lower-power state that is safe and predictable, thereby facilitating diagnosis of more complex faults by a team of human experts on Earth.

  5. Anthrax: Diagnosis

    MedlinePLUS

    ... EID Journal Articles Anthrax-Related MMWRs Medscape Commentaries Diagnosis Language: English Español (Spanish) Recommend on Facebook Tweet ... anthrax. The only ways to confirm an Anthrax diagnosis are: To measure antibodies or toxin in blood ...

  6. Dermatomyositis: Diagnosis

    MedlinePLUS

    ... Partners in Progress Search form Search Dermatomyositis (DM) Diagnosis As with other muscle diseases, a doctor diagnoses ... biopsy can enable the physician to pinpoint the diagnosis to a type of myositis. In DM, the ...

  7. Polymyositis: Diagnosis

    MedlinePLUS

    ... Partners in Progress Search form Search Polymyositis (PM) Diagnosis As with other muscle diseases, a doctor diagnoses ... biopsy can enable the physician to pinpoint the diagnosis to a type of myositis. In PM, the ...

  8. A probabilistic method to diagnose faults of air handling units

    NASA Astrophysics Data System (ADS)

    Dey, Debashis

    Air handling unit (AHU) is one of the most extensively used equipment in large commercial buildings. This device is typically customized and lacks quality system integration which can result in hardwire failures and controller errors. Air handling unit Performance Assessment Rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units. APAR is computationally simple enough that it can be embedded in commercial building automation and control systems and relies only upon sensor data and control signals that are commonly available in these systems. Although APAR has many advantages over other methods, for example no training data required and easy to implement commercially, most of the time it is unable to provide the diagnosis of the faults. For instance, a fault on temperature sensor could be fixed bias, drifting bias, inappropriate location, complete failure. Also a fault in mixing box can be return and outdoor damper leak or stuck. In addition, when multiple rules are satisfied the list of faults increases. There is no proper way to have the correct diagnosis for rule based fault detection system. To overcome this limitation we proposed Bayesian Belief Network (BBN) as a diagnostic tool. BBN can be used to simulate diagnostic thinking of FDD experts through a probabilistic way. In this study we developed a new way to detect and diagnose faults in AHU through combining APAR rules and Bayesian Belief network. Bayesian Belief Network is used as a decision support tool for rule based expert system. BBN is highly capable to prioritize faults when multiple rules are satisfied simultaneously. Also it can get information from previous AHU operating conditions and maintenance records to provide proper diagnosis. The proposed model is validated with real time measured data of a campus building at University of Texas at San Antonio (UTSA).The results show that BBN is correctly able to prioritize faults which can be verified by manual investigation.

  9. Downhole vibration sensing by vibration energy harvesting

    E-print Network

    Trimble, A. Zachary

    2007-01-01

    This thesis outlines the design of a prototype electromagnetic induction vibration energy harvesting device for use in a downhole environment. First order models of the necessary components for a generic vibration energy ...

  10. Creating an automated chiller fault detection and diagnostics tool using a data fault library.

    PubMed

    Bailey, Margaret B; Kreider, Jan F

    2003-07-01

    Reliable, automated detection and diagnosis 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 fault detection and diagnostic (FDD) tool developed as part of this research analyzes chiller operating data and detects faults 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 fault empirical data for training purposes and therefore a fault library of empirical data is assembled. This paper presents procedures for conducting sophisticated fault experiments on chillers that simulate air-cooled condenser, refrigerant, and oil related faults. 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 fault operation. The chiller's performance degradation is successfully detected and classified by the NN FDD classifier as discussed in the paper's final section. PMID:12858981

  11. Commissioning and Diagnosis of VAV Air-Conditioning Systems 

    E-print Network

    Qin, J.; Wang, S.; Chan, C.; Xiao, F.

    2006-01-01

    This paper presents a fault detection and diagnosis (FDD) strategy based on system knowledge, qualitative states and object-oriented statistical process control (SPC) models for typical pressure-independent variable air volume (VAV) air...

  12. Fault model development for fault tolerant VLSI design

    NASA Astrophysics Data System (ADS)

    Hartmann, C. R.; Lala, P. K.; Ali, A. M.; Visweswaran, G. S.; Ganguly, S.

    1988-05-01

    Fault models provide systematic and precise representations of physical defects in microcircuits in a form suitable for simulation and test generation. The current difficulty in testing VLSI circuits can be attributed to the tremendous increase in design complexity and the inappropriateness of traditional stuck-at fault models. This report develops fault models for three different types of common defects that are not accurately represented by the stuck-at fault model. The faults examined in this report are: bridging faults, transistor stuck-open faults, and transient faults caused by alpha particle radiation. A generalized fault model could not be developed for the three fault types. However, microcircuit behavior and fault detection strategies are described for the bridging, transistor stuck-open, and transient (alpha particle strike) faults. The results of this study can be applied to the simulation and analysis of faults in fault tolerant VLSI circuits.

  13. Fault tolerant data structures

    SciTech Connect

    Aumann, Y.; Bender, M.A.

    1996-12-31

    We consider the tolerance of data structures to memory faults. We observe that many pointer-based data structures (e.g. linked lists, trees, etc.) are highly nonresilient to faults. A single fault in a linked list or tree may result in the loss of the entire set of data. In this paper we present a formal framework for studying the fault tolerance properties of pointer-based data structures, and we provide fault tolerant versions of the stack, the linked list, and the dictionary tree.

  14. Vibration Based Sun Gear Damage Detection

    NASA Technical Reports Server (NTRS)

    Hood, Adrian; LaBerge, Kelsen; Lewicki, David; Pines, Darryll

    2013-01-01

    Seeded fault experiments were conducted on the planetary stage of an OH-58C helicopter transmission. Two vibration 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 vibration 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 vibration 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.

  15. FTAPE: A fault injection tool to measure fault tolerance

    NASA Technical Reports Server (NTRS)

    Tsai, Timothy K.; Iyer, Ravishankar K.

    1994-01-01

    The paper introduces FTAPE (Fault Tolerance And Performance Evaluator), a tool that can be used to compare fault-tolerant computers. The tool combines system-wide fault injection with a controllable workload. A workload generator is used to create high stress conditions for the machine. Faults are injected based on this workload activity in order to ensure a high level of fault propagation. The errors/fault ratio and performance degradation are presented as measures of fault tolerance.

  16. FTAPE: A fault injection tool to measure fault tolerance

    NASA Technical Reports Server (NTRS)

    Tsai, Timothy K.; Iyer, Ravishankar K.

    1995-01-01

    The paper introduces FTAPE (Fault Tolerance And Performance Evaluator), a tool that can be used to compare fault-tolerant computers. The tool combines system-wide fault injection with a controllable workload. A workload generator is used to create high stress conditions for the machine. Faults are injected based on this workload activity in order to ensure a high level of fault propagation. The errors/fault ratio and performance degradation are presented as measures of fault tolerance.

  17. Trishear for curved faults

    NASA Astrophysics Data System (ADS)

    Brandenburg, J. P.

    2013-08-01

    Fault-propagation folds form an important trapping element in both onshore and offshore fold-thrust belts, and as such benefit from reliable interpretation. Building an accurate geologic interpretation of such structures requires palinspastic restorations, which are made more challenging by the interplay between folding and faulting. Trishear (Erslev, 1991; Allmendinger, 1998) is a useful tool to unravel this relationship kinematically, but is limited by a restriction to planar fault geometries, or at least planar fault segments. Here, new methods are presented for trishear along continuously curved reverse faults defining a flat-ramp transition. In these methods, rotation of the hanging wall above a curved fault is coupled to translation along a horizontal detachment. Including hanging wall rotation allows for investigation of structures with progressive backlimb rotation. Application of the new algorithms are shown for two fault-propagation fold structures: the Turner Valley Anticline in Southwestern Alberta, and the Alpha Structure in the Niger Delta.

  18. Robust Fault Detection

    NASA Technical Reports Server (NTRS)

    Guo, Ten-Huei (Technical Monitor); Collins, Emmanuel G.; Song, Tinglun; Curry, Tramone; Selekwa, Majura

    2003-01-01

    This research used mixed structured singular value theory to develop new estimator (or observer) based approaches to fault detection for dynamic systems. The initial developments were based on minimizing the H-infinity, I-1 and H2 system norms. The resultant fault detection algorithms were each shown to be successful, but the fault detection algorithm based on the I-1 norm was best able to detect abrupt faults. This latter technique was further improved by using fuzzy logic for the fault evaluation. Based on an anomaly observed in this research and apparently ignored in the literature, current research focuses on the determination of a fault using a norm of the change in the residual (the difference between the output of the system and observer) and not simply a norm of the residual itself. This research may lead to a fundamental contribution to research in fault detection and isolation.

  19. Noncontact Electromagnetic Vibration Source

    NASA Technical Reports Server (NTRS)

    Namkung, Min; Fulton, James P.; Wincheski, Buzz A.

    1994-01-01

    Metal aircraft skins scanned rapidly in vibration tests. Relatively simple combination of permanent magnets and electromagnet serves as noncontact vibration source for nondestructive testing of metal aircraft skins. In test, source excites vibrations, and vibration waveforms measured, then analyzed for changes in resonances signifying cracks and other flaws.

  20. Flow-induced vibration

    SciTech Connect

    Blevins, R.D.

    1990-01-01

    This book reports on dimensional analysis; ideal fluid models; vortex-induced vibration; galloping and flutter; instability of tube and cylinder arrays; vibrations induced by oscillating flow; vibration induced by turbulence and sound; damping of structures; sound induced by vortex shedding; vibrations of a pipe containing a fluid flow; indices. It covers the analysis of the vibrations of structures exposed to fluid flows; explores applications for offshore platforms and piping; wind-induced vibration of buildings, bridges, and towers; and acoustic and mechanical vibration of heat exchangers, power lines, and process ducting.

  1. Fault analysis of space station dc power systems-using neutral network adaptive wavelets to detect faults

    NASA Astrophysics Data System (ADS)

    Momoh, James A.; Wang, Yanchun; Dolce, James L.

    1997-01-01

    This paper describes the application of neutral network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

  2. Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

    NASA Technical Reports Server (NTRS)

    Momoh, James A.; Wang, Yanchun; Dolce, James L.

    1997-01-01

    This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

  3. Fault tolerant architectures for integrated aircraft electronics systems, task 2

    NASA Technical Reports Server (NTRS)

    Levitt, K. N.; Melliar-Smith, P. M.; Schwartz, R. L.

    1984-01-01

    The architectural basis for an advanced fault tolerant on-board computer to succeed the current generation of fault tolerant computers is examined. The network error tolerant system architecture is studied with particular attention to intercluster configurations and communication protocols, and to refined reliability estimates. The diagnosis of faults, so that appropriate choices for reconfiguration can be made is discussed. The analysis relates particularly to the recognition of transient faults in a system with tasks at many levels of priority. The demand driven data-flow architecture, which appears to have possible application in fault tolerant systems is described and work investigating the feasibility of automatic generation of aircraft flight control programs from abstract specifications is reported.

  4. FIESTA: An operational decision aid for space network fault isolation

    NASA Technical Reports Server (NTRS)

    Lowe, Dawn; Quillin, Bob; Matteson, Nadine; Wilkinson, Bill; Miksell, Steve

    1987-01-01

    The Fault Tolerance Expert System for Tracking and Data Relay Satellite System (TDRSS) Applications (FIESTA) is a fault detection and fault diagnosis expert system being developed as a decision aid to support operations in the Network Control Center (NCC) for NASA's Space Network. The operational objectives which influenced FIESTA development are presented and an overview of the architecture used to achieve these goals are provided. The approach to the knowledge engineering effort and the methodology employed are also presented and illustrated with examples drawn from the FIESTA domain.

  5. How Faults Shape the Earth.

    ERIC Educational Resources Information Center

    Bykerk-Kauffman, Ann

    1992-01-01

    Presents fault activity with an emphasis on earthquakes and changes in continent shapes. Identifies three types of fault movement: normal, reverse, and strike faults. Discusses the seismic gap theory, plate tectonics, and the principle of superposition. Vignettes portray fault movement, and the locations of the San Andreas fault and epicenters of…

  6. Fault simulation and test generation for small delay faults 

    E-print Network

    Qiu, Wangqi

    2007-04-25

    Delay faults are an increasingly important test challenge. Traditional delay fault models are incomplete in that they model only a subset of delay defect behaviors. To solve this problem, a more realistic delay fault model has been developed which...

  7. An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Daigle, Matthew J.; Roychoudhury, Indranil

    2012-01-01

    Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

  8. Model-based reconfiguration: Diagnosis and recovery

    NASA Technical Reports Server (NTRS)

    Crow, Judy; Rushby, John

    1994-01-01

    We extend Reiter's general theory of model-based diagnosis to a theory of fault detection, identification, and reconfiguration (FDIR). The generality of Reiter's theory readily supports an extension in which the problem of reconfiguration is viewed as a close analog of the problem of diagnosis. Using a reconfiguration predicate 'rcfg' analogous to the abnormality predicate 'ab,' we derive a strategy for reconfiguration by transforming the corresponding strategy for diagnosis. There are two obvious benefits of this approach: algorithms for diagnosis can be exploited as algorithms for reconfiguration and we have a theoretical framework for an integrated approach to FDIR. As a first step toward realizing these benefits we show that a class of diagnosis engines can be used for reconfiguration and we discuss algorithms for integrated FDIR. We argue that integrating recovery and diagnosis is an essential next step if this technology is to be useful for practical applications.

  9. Comparison of Chiller Models for Use in Model-Based Fault Detection 

    E-print Network

    Sreedhara, P.; Haves, P.

    2001-01-01

    Selecting the model is an important and essential step in model based fault detection and diagnosis (FDD). Factors that are considered in evaluating a model include accuracy, training data requirements, calibration effort, generality...

  10. From diagnosis to social diagnosis.

    PubMed

    Brown, Phil; Lyson, Mercedes; Jenkins, Tania

    2011-09-01

    In the past two decades, research on the sociology of diagnosis has attained considerable influence within medical sociology. Analyzing the process and factors that contribute to making a diagnosis amidst uncertainty and contestation, as well as the diagnostic encounter itself, are topics rich for sociological investigation. This paper provides a reformulation of the sociology of diagnosis by proposing the concept of 'social diagnosis' which helps us recognize the interplay between larger social structures and individual or community illness manifestations. By outlining a conceptual frame, exploring how social scientists, medical professionals and laypeople contribute to social diagnosis, and providing a case study of how the North American Mohawk Akwesasne reservation dealt with rising obesity prevalence to further illustrate the social diagnosis idea, we embark on developing a cohesive and updated framework for a sociology of diagnosis. This approach is useful not just for sociological research, but has direct implications for the fields of medicine and public health. Approaching diagnosis from this integrated perspective potentially provides a broader context for practitioners and researchers to understand extra-medical factors, which in turn has consequences for patient care and health outcomes. PMID:21705128

  11. A Comparison of Fault Detection Methods For a Transcritical Refrigeration System 

    E-print Network

    Janecke, Alex Karl

    2012-10-19

    in the 1970’s. Wilsky summarized many of the early fault detection and diagnosis (FDD) schemes used [7]. Clark gave an early observer based method with the example of a hydrofoil [8]. Overviews of dynamic fault detection as a general field can be found...

  12. Denali Fault: Alaska Pipeline

    USGS Multimedia Gallery

    View south along the Trans Alaska Pipeline in the zone where it was engineered for the Denali fault. The fault trace passes beneath the pipeline between the 2nd and 3rd slider supports at the far end of the zone. A large arc in the pipe can be seen in the pipe on the right, due to shortening of the ...

  13. Denali Fault: Susitna Glacier

    USGS Multimedia Gallery

    Helicopters and satellite phones were integral to the geologic field response. Here, Peter Haeussler is calling a seismologist to pass along the discovery of the Susitna Glacier thrust fault. View is to the north up the Susitna Glacier. The Denali fault trace lies in the background where the two lan...

  14. Denali Fault: Gillette Pass

    USGS Multimedia Gallery

    View northward of mountain near Gillette Pass showing sackung features. Here the mountaintop moved downward like a keystone, producing an uphill-facing scarp. The main Denali fault trace is on the far side of the mountain and a small splay fault is out of view below the photo....

  15. Denali Fault: Gillette Pass

    USGS Multimedia Gallery

    View north of Denali fault trace at Gillette Pass. this view shows that the surface rupture reoccupies the previous fault scarp. Also the right-lateral offset of these stream gullies has developed since deglaciation in the last 10,000 years or so....

  16. Locating of the earth fault of the single-phase at the tree distribution network

    NASA Astrophysics Data System (ADS)

    Guo, Li-Ping

    2013-07-01

    This paper proposes method through the combination of ranging the C-type traveling wave and the analysis and selection of line to the component of the line mode to locate the fault point. Inject into high amplitude and narrow signals in the beginning of a line and detect circuit returning from the arrival time of the waveform. Comparing of the normal waveform and failure waveform in both cases, receive the reflected wave fault at the arrival time. And then determine the fault distance. Using the effect from the fault which generate the shocks of the traveling wave and comparing the shock time of each branch line, the line which acquires the longest duration of vibration is the fault line. Through the theoretical analysis, Matlab simulation and effective analysis of the selected data, this paper proves the correctness of the method and demonstrate that the method of fault location in distribution networks is practical.

  17. Characterization of leaky faults

    SciTech Connect

    Shan, Chao

    1990-05-01

    Leaky faults provide a flow path for fluids to move underground. It is very important to characterize such faults in various engineering projects. The purpose of this work is to develop mathematical solutions for this characterization. The flow of water in an aquifer system and the flow of air in the unsaturated fault-rock system were studied. If the leaky fault cuts through two aquifers, characterization of the fault can be achieved by pumping water from one of the aquifers, which are assumed to be horizontal and of uniform thickness. Analytical solutions have been developed for two cases of either a negligibly small or a significantly large drawdown in the unpumped aquifer. Some practical methods for using these solutions are presented. 45 refs., 72 figs., 11 tabs.

  18. Solar system fault detection

    DOEpatents

    Farrington, R.B.; Pruett, J.C. Jr.

    1984-05-14

    A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

  19. Solar system fault detection

    DOEpatents

    Farrington, Robert B. (Wheatridge, CO); Pruett, Jr., James C. (Lakewood, CO)

    1986-01-01

    A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

  20. Diagnosis of automotive fuel cell power generators

    NASA Astrophysics Data System (ADS)

    Hissel, D.; Péra, M. C.; Kauffmann, J. M.

    Most of car manufacturers around the world have launched important research programs on the integration of fuel cell (FC) power generators into cars. Despite the first achievements, fuel cell systems are still badly known, particularly when talking about fault diagnosis and predictive maintenance. This paper proposes a first step in this way by introducing a simple but also efficient diagnosis-oriented model of a proton exchange membrane fuel cell (PEMFC). The considered diagnosis model is here a fuzzy one and is tuned thanks to genetic algorithms.

  1. Novel cyclostationarity-based blind source separation algorithm using second order statistical properties: Theory and application to the bearing defect diagnosis

    NASA Astrophysics Data System (ADS)

    Bouguerriou, N.; Haritopoulos, M.; Capdessus, C.; Allam, L.

    2005-11-01

    Among signal processing techniques, blind source separation (BSS) and the underlying mathematical tool of independent component analysis (ICA) are of continuously growing interest in the scientific community of various research domains. Vibration analysis is a potential application field of this quite recent technique. Actually, BSS methods aim to retrieve unknown source signals from a set of their observations coming to a matrix of sensors, without necessarily having any prior knowledge about the sources. In monitoring and diagnosis purposes, bearing defects constitute a problem for manufacturers who aim at predicting those faults as well as potential engines breakdowns. These defects may be the unknown sources one wants to estimate from a set of recorded signals by a matrix of accelerometers placed close to the rotating machine. It has been shown that these vibration signals are wide-sense cyclostationary [ [11] R.B.Randall, J. Antoni, S. Chobsaard, The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals, Mechanical Systems and Signal Processing 15 (5) (2001) 945-962]. The new algorithm of BSS proposed in this work is based, precisely, on that property. Second-order statistics of such processes led us to a new separation criterion for blind source separation. The theoretical results of this study, simulation and experimental analysis are presented in here. Perspectives for future research conclude this paper.

  2. Double fault detection of cone-shaped redundant IMUs using wavelet transformation and EPSA.

    PubMed

    Lee, Wonhee; Park, Chan Gook

    2014-01-01

    A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU). PMID:24556675

  3. A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

    2015-01-01

    Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

  4. ROTATION-VIBRATION TETRAHEDRAL

    E-print Network

    Sadovskií, Dmitrií

    ANALYSIS OF ROTATION-VIBRATION RELATIVE EQUILIBRIA ON THE EXAMPLE OF A TETRAHEDRAL FOUR ATOM (RE) of a nonrigid molecule which vibrates about a well de#12;ned equilibrium con#12;guration and rotates as a whole. Our analysis uni#12;es the theory of rotational and vibrational RE. We rely

  5. 2798 IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 30, NO. 5, MAY 2015 A Fault-Tolerant Switching Scheme for an Isolated

    E-print Network

    Mazumder, Sudip K.

    diagnosis algorithm for a VSI used as a PMSM drive system. [19] also proposes a fault-detection method2798 IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 30, NO. 5, MAY 2015 A Fault-Tolerant Switching, Senior Member, IEEE Abstract--A fault-tolerant scheme is proposed for a multistage isolated three

  6. Transient fault modeling and fault injection simulation 

    E-print Network

    Yuan, Xuejun

    1996-01-01

    An accurate transient fault model is presented in this thesis. A 7-term exponential current upset model is derived from the results of a device-level, 3-dimensional, single-event-upset simulation. A curve-fitting algorithm is used to extract...

  7. Differential Fault Analysis of AES: Toward Reducing Number of Faults

    E-print Network

    International Association for Cryptologic Research (IACR)

    Differential Fault Analysis of AES: Toward Reducing Number of Faults Chong Hee KIM Information-la-Neuve, Belgium. Abstract Differential Fault Analysis (DFA) finds the key of a block cipher using differ- ential information between correct and faulty ciphertexts obtained by induc- ing faults during the computation

  8. Analytical and Experimental Vibration Analysis of a Faulty Gear System

    NASA Technical Reports Server (NTRS)

    Choy, F. K.; Braun, M. J.; Polyshchuk, V.; Zakrajsek, J. J.; Townsend, D. P.; Handschuh, R. F.

    1994-01-01

    A comprehensive analytical procedure was developed for predicting faults in gear transmission systems under normal operating conditions. A gear tooth fault model is developed to simulate the effects of pitting and wear on the vibration signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth faults. 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 fault 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 vibration data of the damaged test gear and the predicted vibration 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.

  9. Improving Distributed Diagnosis Through Structural Model Decomposition

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Daigle, Matthew John; Roychoudhury, Indranil; Biswas, Gautam; Koutsoukos, Xenofon; Pulido, Belarmino

    2011-01-01

    Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals computed by extending Possible Conflicts to build local event-based diagnosers based on global diagnosability analysis. The proposed approach is applied to a multitank system, and results demonstrate an improvement in the design of local diagnosers. Since local diagnosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed distributed approaches.

  10. The Kunlun Fault

    NASA Technical Reports Server (NTRS)

    2002-01-01

    The Kunlun fault is one of the gigantic strike-slip faults that bound the north side of Tibet. Left-lateral motion along the 1,500-kilometer (932-mile) length of the Kunlun has occurred uniformly for the last 40,000 years at a rate of 1.1 centimeter per year, creating a cumulative offset of more than 400 meters. In this image, two splays of the fault are clearly seen crossing from east to west. The northern fault juxtaposes sedimentary rocks of the mountains against alluvial fans. Its trace is also marked by lines of vegetation, which appear red in the image. The southern, younger fault cuts through the alluvium. A dark linear area in the center of the image is wet ground where groundwater has ponded against the fault. Measurements from the image of displacements of young streams that cross the fault show 15 to 75 meters (16 to 82 yards) of left-lateral offset. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) acquired the visible light and near infrared scene on July 20, 2000. Image courtesy NASA/GSFC/MITI/ERSDAC/JAROS, and the U.S./Japan ASTER Science Team

  11. Incipient fault detection study for advanced spacecraft systems

    NASA Technical Reports Server (NTRS)

    Milner, G. Martin; Black, Michael C.; Hovenga, J. Mike; Mcclure, Paul F.

    1986-01-01

    A feasibility study to investigate the application of vibration 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 fault 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 faulted tests. Both detection and classification performance are quantified. Vibration 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.

  12. Development and Application of Nonlinear PCA for Fault

    E-print Network

    Gorban, Alexander N.

    Development and Application of Nonlinear PCA for Fault Diagnosis in Internal Combustion Engines Uwe to an Internal Combustion Engine · Conclusions and Future Challenges #12;Slide 3 Uwe Kruger Development Kruger School of Electronics, Electrical Engineering & Computer Science Ashby Building Stranmillis Road

  13. Fault detection and isolation

    NASA Technical Reports Server (NTRS)

    Bernath, Greg

    1994-01-01

    In order for a current satellite-based navigation system (such as the Global Positioning System, GPS) to meet integrity requirements, there must be a way of detecting erroneous measurements, without help from outside the system. This process is called Fault Detection and Isolation (FDI). Fault detection requires at least one redundant measurement, and can be done with a parity space algorithm. The best way around the fault isolation problem is not necessarily isolating the bad measurement, but finding a new combination of measurements which excludes it.

  14. Anti-vibration gloves?

    PubMed

    Hewitt, Sue; Dong, Ren G; Welcome, Daniel E; McDowell, Thomas W

    2015-03-01

    For exposure to hand-transmitted vibration (HTV), personal protective equipment is sold in the form of anti-vibration (AV) gloves, but it remains unclear how much these gloves actually reduce vibration exposure or prevent the development of hand-arm vibration syndrome in the workplace. This commentary describes some of the issues that surround the classification of AV gloves, the assessment of their effectiveness and their applicability in the workplace. The available information shows that AV gloves are unreliable as devices for controlling HTV exposures. Other means of vibration control, such as using alternative production techniques, low-vibration machinery, routine preventative maintenance regimes, and controlling exposure durations are far more likely to deliver effective vibration reductions and should be implemented. Furthermore, AV gloves may introduce some adverse effects such as increasing grip force and reducing manual dexterity. Therefore, one should balance the benefits of AV gloves and their potential adverse effects if their use is considered. PMID:25381184

  15. Dynamics of a Gear System with Faults in Meshing Stiffness

    E-print Network

    Grzegorz Litak; Michael I. Friswell

    2004-05-23

    Gear box dynamics is characterised by a periodically changing stiffness. In real gear systems, a backlash also exists that can lead to a loss in contact between the teeth. Due to this loss of contact the gear has piecewise linear stiffness characteristics, and the gears can vibrate regularly and chaotically. In this paper we examine the effect of tooth shape imperfections and defects. Using standard methods for nonlinear systems we examine the dynamics of gear systems with various faults in meshing stiffness.

  16. Bayesian sequential change diagnosis

    E-print Network

    Dayanik, Savas; Poor, H Vincent

    2007-01-01

    Sequential change diagnosis is the joint problem of detection and identification of a sudden and unobservable change in the distribution of a random sequence. In this problem, the common probability law of a sequence of i.i.d. random variables suddenly changes at some disorder time to one of finitely many alternatives. This disorder time marks the start of a new regime, whose fingerprint is the new law of observations. Both the disorder time and the identity of the new regime are unknown and unobservable. The objective is to detect the regime-change as soon as possible, and, at the same time, to determine its identity as accurately as possible. Prompt and correct diagnosis is crucial for quick execution of the most appropriate measures in response to the new regime, as in fault detection and isolation in industrial processes, and target detection and identification in national defense. The problem is formulated in a Bayesian framework. An optimal sequential decision strategy is found, and an accurate numerica...

  17. Eggshell Cutter Using Ultrasonic Vibration

    NASA Astrophysics Data System (ADS)

    Miura, Hikaru

    2003-05-01

    An eggshell cutting apparatus which utilizes ultrasonic vibration was developed, replacing the conventional apparatus which uses an air cutter, to cut eggshells at the blunt end of eggs. Two ultrasonic vibration sources were used: one with longitudinal vibration only and the other with torsional vibration plus longitudinal vibration. Eggshell cutting experiments using these vibration sources were conducted. The eggshell cutting time sharply decreased with increasing longitudinal vibration amplitude as well as increasing input power. When the source with torsional vibration plus longitudinal vibration was used and the amplitude of longitudinal vibration was 12 ?m or less, the torsional vibration was effective for cutting eggshells. Furthermore, at the same input power, the eggshell cutting time by the source with longitudinal vibration only was shorter than that by the source with torsional vibration plus longitudinal vibration. When an egg was cut using the apparatus, there was essentially no cutting noise and the cut surface was smooth.

  18. A Unified Fault-Tolerance Protocol

    NASA Technical Reports Server (NTRS)

    Miner, Paul; Gedser, Alfons; Pike, Lee; Maddalon, Jeffrey

    2004-01-01

    Davies and Wakerly show that Byzantine fault tolerance can be achieved by a cascade of broadcasts and middle value select functions. We present an extension of the Davies and Wakerly protocol, the unified protocol, and its proof of correctness. We prove that it satisfies validity and agreement properties for communication of exact values. We then introduce bounded communication error into the model. Inexact communication is inherent for clock synchronization protocols. We prove that validity and agreement properties hold for inexact communication, and that exact communication is a special case. As a running example, we illustrate the unified protocol using the SPIDER family of fault-tolerant architectures. In particular we demonstrate that the SPIDER interactive consistency, distributed diagnosis, and clock synchronization protocols are instances of the unified protocol.

  19. A “mesh” of crossing faults: Fault networks of southern California

    NASA Astrophysics Data System (ADS)

    Janecke, S. U.

    2009-12-01

    Detailed geologic mapping of active fault systems in the western Salton Trough and northern Peninsular Ranges of southern California make it possible to expand the inventory of mapped and known faults by compiling and updating existing geologic maps, and analyzing high resolution imagery, LIDAR, InSAR, relocated hypocenters and other geophysical datasets. A fault map is being compiled on Google Earth and will ultimately discriminate between a range of different fault expressions: from well-mapped faults to subtle lineaments and geomorphic anomalies. The fault map shows deformation patterns in both crystalline and basinal deposits and reveals a complex fault mesh with many curious and unexpected relationships. Key findings are: 1) Many fault systems have mutually interpenetrating geometries, are grossly coeval, and allow faults to cross one another. A typical relationship reveals a dextral fault zone that appears to be continuous at the regional scale. In detail, however, there are no continuous NW-striking dextral fault traces and instead the master dextral fault is offset in a left-lateral sense by numerous crossing faults. Left-lateral faults also show small offsets where they interact with right lateral faults. Both fault sets show evidence of Quaternary activity. Examples occur along the Clark, Coyote Creek, Earthquake Valley and Torres Martinez fault zones. 2) Fault zones cross in other ways. There are locations where active faults continue across or beneath significant structural barriers. Major fault zones like the Clark fault of the San Jacinto fault system appears to end at NE-striking sinistral fault zones (like the Extra and Pumpkin faults) that clearly cross from the SW to the NE side of the projection of the dextral traces. Despite these blocking structures, there is good evidence for continuation of the dextral faults on the opposite sides of the crossing fault array. In some instances there is clear evidence (in deep microseismic alignments of hypocenters) that the master dextral faults zones pass beneath shallower crossing fault arrays above them and this mechanism may transfer strain through the blocking zones. 3) The curvature of strands of the Coyote Creek fault and the Elsinore fault are similar along their SE 60 km. The scale, locations and concavity of bends are so similar that their shape appears to be coordinated. The matching contractional and extensional bends suggests that originally straighter dextral fault zones may be deforming in response of coeval sinistral deformation between, beneath, and around them. 4) Deformation is strongly domainal with one style or geometry of structure dominating in one area then another in an adjacent area. Boundaries may be abrupt. 5) There are drastic lateral changes in the width of damage zones adjacent to master faults. Outlines of the deformation related to some dextral fault zones resemble a snake that has ingested a squirming cat or soccer ball. 6) A mesh of interconnected faults seems to transfer slip back and forth between structures. 7) Scarps are not necessarily more abundant on the long master faults than on connector or crossing faults. Much remains to be learned upon completion the fault map.

  20. Pen Branch Fault Program

    SciTech Connect

    Price, V.; Stieve, A.L.; Aadland, R.

    1990-09-28

    Evidence from subsurface mapping and seismic reflection surveys at Savannah River Site (SRS) suggests the presence of a fault which displaces Cretaceous through Tertiary (90--35 million years ago) sediments. This feature has been described and named the Pen Branch fault (PBF) in a recent Savannah River Laboratory (SRL) paper (DP-MS-88-219). Because the fault is located near operating nuclear facilities, public perception and federal regulations require a thorough investigation of the fault to determine whether any seismic hazard exists. A phased program with various elements has been established to investigate the PBF to address the Nuclear Regulatory Commission regulatory guidelines represented in 10 CFR 100 Appendix A. The objective of the PBF program is to fully characterize the nature of the PBF (ESS-SRL-89-395). This report briefly presents current understanding of the Pen Branch fault based on shallow drilling activities completed the fall of 1989 (PBF well series) and subsequent core analyses (SRL-ESS-90-145). The results are preliminary and ongoing: however, investigations indicate that the fault is not capable. In conjunction with the shallow drilling, other activities are planned or in progress. 7 refs., 8 figs., 1 tab.

  1. Bearing fault recognition method based on neighbourhood component analysis and coupled hidden Markov model

    NASA Astrophysics Data System (ADS)

    Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Hongchao; Yuan, Haodong

    2016-01-01

    Due to the important role rolling element bearings play in rotating machines, condition monitoring and fault diagnosis system should be established to avoid abrupt breakage during operation. Various features from time, frequency and time-frequency domain are usually used for bearing or machinery condition monitoring. In this study, NCA-based feature extraction (FE) approach is proposed to reduce the dimensionality of original feature set and avoid the "curse of dimensionality". Furthermore, coupled hidden Markov model (CHMM) based on multichannel data acquisition is applied to diagnose bearing or machinery fault. Two case studies are presented to validate the proposed approach both in bearing fault diagnosis and fault severity classification. The experiment results show that the proposed NCA-CHMM can remove redundant information, fuse data from different channels and improve the diagnosis results.

  2. Fault Roughness Records Strength

    NASA Astrophysics Data System (ADS)

    Brodsky, E. E.; Candela, T.; Kirkpatrick, J. D.

    2014-12-01

    Fault roughness is commonly ~0.1-1% at the outcrop exposure scale. More mature faults are smoother than less mature ones, but the overall range of roughness is surprisingly limited which suggests dynamic control. In addition, the power spectra of many exposed fault surfaces follow a single power law over scales from millimeters to 10's of meters. This is another surprising observation as distinct structures such as slickenlines and mullions are clearly visible on the same surfaces at well-defined scales. We can reconcile both observations by suggesting that the roughness of fault surfaces is controlled by the maximum strain that can be supported elastically in the wallrock. If the fault surface topography requires more than 0.1-1% strain, it fails. Invoking wallrock strength explains two additional observations on the Corona Heights fault for which we have extensive roughness data. Firstly, the surface is isotropic below a scale of 30 microns and has grooves at larger scales. Samples from at least three other faults (Dixie Valley, Mount St. Helens and San Andreas) also are isotropic at scales below 10's of microns. If grooves can only persist when the walls of the grooves have a sufficiently low slope to maintain the shape, this scale of isotropy can be predicted based on the measured slip perpendicular roughness data. The observed 30 micron scale at Corona Heights is consistent with an elastic strain of 0.01 estimated from the observed slip perpendicular roughness with a Hurst exponent of 0.8. The second observation at Corona Heights is that slickenlines are not deflected around meter-scale mullions. Yielding of these mullions at centimeter to meter scale is predicted from the slip parallel roughness as measured here. The success of the strain criterion for Corona Heights supports it as the appropriate control on fault roughness. Micromechanically, the criterion implies that failure of the fault surface is a continual process during slip. Macroscopically, the fundamental nature of the control means that 0.1 to 1% roughness should be ubiquitous on faults and can generally be used for simulating ground motion. An important caveat is that the scale-dependence of strength may result in a difference in the yield criterion at large-scales. The commonly observed values of the Hurst exponent below 1 may capture this scale-dependence.

  3. Validated Fault Tolerant Architectures for Space Station

    NASA Technical Reports Server (NTRS)

    Lala, Jaynarayan H.

    1990-01-01

    Viewgraphs on validated fault tolerant architectures for space station are presented. Topics covered include: fault tolerance approach; advanced information processing system (AIPS); and fault tolerant parallel processor (FTPP).

  4. Fault Detection and Classification

    NASA Astrophysics Data System (ADS)

    Scanlan, John

    2004-09-01

    Plasma processes are used widely in the manufacture of semiconductor devices. Recent trends in this industry have focussed on methods for automated process control. For limiting processes such as plasma etch, an emerging focus is on real time Fault Detection and Classification (FDC). Simply put, the aim is to provide a system that not only detects faults but also identifies the root cause. For example, semiconductor production fabs regularly encounter faults that result in unscheduled tool downtime and reduced yield. Among these are real-time process and tool faults, post maintenance recovery problems and tool mis-matching at start-up and process transfer. The objective of any FDC scheme should be to reduce this product loss and tool downtime by identifying the core problem as rapidly as possible, and replace the usual "trial-and-error" approach to fault identification. There are a couple of key requirements in any control system. Firstly, an estimation of the process state, and secondly, a scheme for providing real-time control. This paper focuses on methods for addressing both problems on plasma etch tools. A non-intrusive high-resolution RF sensor is used to provide in situ process-state and tool-state data. Examples will be presented on how such a sensor can give a repeatable fingerprint of any plasma process. The challenge then becomes the manipulation of this data into usable information. The process control scheme presented is knowledge-based, in that it is trained and does not rely on statistical methods with underlying assumptions of Gaussian data spread. A fingerprint of known fault states is the knowledge set and real-time control is provided by comparison of the sensor fingerprint to the fault fingerprints.

  5. Vibrating fuel grapple

    DOEpatents

    Chertock, deceased, Alan J. (late of San Francisco, CA); Fox, Jack N. (San Jose, CA); Weissinger, Robert B. (Santa Clara, CA)

    1982-01-01

    A reactor refueling method utilizing a vibrating fuel grapple for removing spent fuel assemblies from a reactor core which incorporates a pneumatic vibrator in the grapple head, enabling additional withdrawal capability without exceeding the allowable axial force limit. The only moving part in the vibrator is a steel ball, pneumatically driven by a gas, such as argon, around a track, with centrifugal force created by the ball being transmitted through the grapple to the assembly handling socket.

  6. Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform

    NASA Astrophysics Data System (ADS)

    Zhang, ChunLin; Li, Bing; Chen, BinQiang; Cao, HongRui; Zi, YanYang; He, ZhengJia

    2015-12-01

    Extracting and revealing the weak periodic fault vibration impulses is reasonable for damage detection of rotating machinery. However, the widely used dyadic WT suffers fixed frequency partition manner and low oscillating bases which would weaken its performance in weak fault detection. A new method based on flexible analytic wavelet transform (FAWT) is proposed in this article. Employing fractional and arbitrary scaling and translation factors, FAWT possesses attractive properties such as flexible time-frequency (TF) covering manner, better shift-invariance and tunable oscillatory nature of the bases, offering proper wavelet frame and bases shape to match the weak fault components. Moreover, FAWT is effective in revealing the amplitude modulation feature of the periodic fault impulses that occur in some damaged rotating components. The applications to a rolling bearing, a planetary gearbox of, and a flue gas turbine unit show that the proposed method is effective in extracting weak impulsive fault signature.

  7. PREFACE: Vibrations at surfaces Vibrations at surfaces

    NASA Astrophysics Data System (ADS)

    Rahman, Talat S.

    2011-12-01

    This special issue is dedicated to the phenomenon of vibrations at surfaces—a topic that was indispensible a couple of decades ago, since it was one of the few phenomena capable of revealing the nature of binding at solid surfaces. For clean surfaces, the frequencies of modes with characteristic displacement patterns revealed how surface geometry, as well as the nature of binding between atoms in the surface layers, could be different from that in the bulk solid. Dispersion of the surface phonons provided further measures of interatomic interactions. For chemisorbed molecules on surfaces, frequencies and dispersion of the vibrational modes were also critical for determining adsorption sites. In other words, vibrations at surfaces served as a reliable means of extracting information about surface structure, chemisorption and overlayer formation. Experimental techniques, such as electron energy loss spectroscopy and helium-atom-surface scattering, coupled with infra-red spectroscopy, were continually refined and their resolutions enhanced to capture subtleties in the dynamics of atoms and molecules at surfaces. Theoretical methods, whether based on empirical and semi-empirical interatomic potential or on ab initio electronic structure calculations, helped decipher experimental observations and provide deeper insights into the nature of the bond between atoms and molecules in regions of reduced symmetry, as encountered on solid surfaces. Vibrations at surfaces were thus an integral part of the set of phenomena that characterized surface science. Dedicated workshops and conferences were held to explore the variety of interesting and puzzling features revealed in experimental and theoretical investigations of surface vibrational modes and their dispersion. One such conference, Vibrations at Surfaces, first organized by Harald Ibach in Juelich in 1980, continues to this day. The 13th International Conference on Vibrations at Surfaces was held at the University of Central Florida, Orlando, in March 2010. Several speakers at this meeting were invited to contribute to the special section in this issue. As is clear from the articles in this special section, the phenomenon of vibrations at surfaces continues to be a dynamic field of investigation. In fact, there is a resurgence of effort because the insights provided by surface dynamics are still fundamental to the development of an understanding of the microscopic factors that control surface structure formation, diffusion, reaction and structural stability. Examination of dynamics at surfaces thus complements and supplements the wealth of information that is obtained from real-space techniques such as scanning tunneling microscopy. Vibrational dynamics is, of course, not limited to surfaces. Surfaces are important since they provide immediate deviation from the bulk. They display how lack of symmetry can lead to new structures, new local atomic environments and new types of dynamical modes. Nanoparticles, large molecules and nanostructures of all types, in all kinds of local environments, provide further examples of regions of reduced symmetry and coordination, and hence display characteristic vibrational modes. Given the tremendous advance in the synthesis of a variety of nanostructures whose functionalization would pave the way for nanotechnology, there is even greater need to engage in experimental and theoretical techniques that help extract their vibrational dynamics. Such knowledge would enable a more complete understanding and characterization of these nanoscale systems than would otherwise be the case. The papers presented here provide excellent examples of the kind of information that is revealed by vibrations at surfaces. Vibrations at surface contents Poisoning and non-poisoning oxygen on Cu(410)L Vattuone, V Venugopal, T Kravchuk, M Smerieri, L Savio and M Rocca Modifying protein adsorption by layers of glutathione pre-adsorbed on Au(111)Anne Vallée, Vincent Humblot, Christophe Méthivier, Paul Dumas and Claire-Marie Pradier Relating temperature dependence of atom

  8. Structural Load Analysis of a Wind Turbine under Pitch Actuator and Controller Faults

    NASA Astrophysics Data System (ADS)

    Etemaddar, Mahmoud; Gao, Zhen; Moan, Torgeir

    2014-12-01

    In this paper, we investigate the characteristics of a wind turbine under blade pitch angle and shaft speed sensor faults as well as pitch actuator faults. A land-based NREL 5MW variable speed pitch reg- ulated wind turbine is considered as a reference. The conventional collective blade pitch angle controller strategy with independent pitch actuators control is used for load reduction. The wind turbine class is IEC-BII. The main purpose is to investigate the severity of end effects on structural loads and responses and consequently identify the high-risk components according to the type and amplitude of fault using a servo-aero-elastic simulation code, HAWC2. Both transient and steady state effects of faults are studied. Such information is useful for wind turbine fault detection and identification as well as system reliability analysis. Results show the effects of faults on wind turbine power output and responses. Pitch sensor faults mainly affects the vibration of shaft main bearing, while generator power and aerodynamic thrust are not changed significantly, due to independent pitch actuator control of three blades. Shaft speed sensor faults can seriously affect the generator power and aerodynamic thrust. Pitch actuator faults can result in fully pitching of the blade, and consequently rotor stops due to negative aerodynamic torque.

  9. Nonlinear dynamic failure process of tunnel-fault system in response to strong seismic event

    NASA Astrophysics Data System (ADS)

    Yang, Zhihua; Lan, Hengxing; Zhang, Yongshuang; Gao, Xing; Li, Langping

    2013-03-01

    Strong earthquakes and faults have significant effect on the stability capability of underground tunnel structures. This study used a 3-Dimensional Discrete Element model and the real records of ground motion in the Wenchuan earthquake to investigate the dynamic response of tunnel-fault system. The typical tunnel-fault system was composed of one planned railway tunnel and one seismically active fault. The discrete numerical model was prudentially calibrated by means of the comparison between the field survey and numerical results of ground motion. It was then used to examine the detailed quantitative information on the dynamic response characteristics of tunnel-fault system, including stress distribution, strain, vibration velocity and tunnel failure process. The intensive tunnel-fault interaction during seismic loading induces the dramatic stress redistribution and stress concentration in the intersection of tunnel and fault. The tunnel-fault system behavior is characterized by the complicated nonlinear dynamic failure process in response to a real strong seismic event. It can be qualitatively divided into 5 main stages in terms of its stress, strain and rupturing behaviors: (1) strain localization, (2) rupture initiation, (3) rupture acceleration, (4) spontaneous rupture growth and (5) stabilization. This study provides the insight into the further stability estimation of underground tunnel structures under the combined effect of strong earthquakes and faults.

  10. Melanoma Diagnosis

    NASA Astrophysics Data System (ADS)

    Horsch, Alexander

    The chapter deals with the diagnosis of the malignant melanoma of the skin. This aggressive type of cancer with steadily growing incidence in white populations can hundred percent be cured if it is detected in an early stage. Imaging techniques, in particular dermoscopy, have contributed significantly to improvement of diagnostic accuracy in clinical settings, achieving sensitivities for melanoma experts of beyond 95% at specificities of 90% and more. Automatic computer analysis of dermoscopy images has, in preliminary studies, achieved classification rates comparable to those of experts. However, the diagnosis of melanoma requires a lot of training and experience, and at the time being, average numbers of lesions excised per histology-proven melanoma are around 30, a number which clearly is too high. Further improvements in computer dermoscopy systems and their competent use in clinical settings certainly have the potential to support efforts of improving this situation. In the chapter, medical basics, current state of melanoma diagnosis, image analysis methods, commercial dermoscopy systems, evaluation of systems, and methods and future directions are presented.

  11. Fault terminations, Seminoe Mountains, Wyoming

    SciTech Connect

    Dominic, J.B.; McConnell, D.A. . Dept. of Geology)

    1992-01-01

    Two basement-involved faults terminate in folds in the Seminoe Mountains. Mesoscopic and macroscopic structures in sedimentary rocks provide clues to the interrelationship of faults and folds in this region, and on the linkage between faulting and folding in general. The Hurt Creek fault trends 320[degree] and has maximum separation of 1.5 km measured at the basement/cover contact. Separation on the fault decreases upsection to zero within the Jurassic Sundance Formation. Unfaulted rock units form an anticline around the fault tip. The complementary syncline is angular with planar limbs and a narrow hinge zone. The syncline axial trace intersects the fault in the footwall at the basement/cover cut-off. Map patterns are interpreted to show thickening of Mesozoic units adjacent to the syncline hinge. In contrast, extensional structures are common in the faulted anticline within the Permian Goose Egg and Triassic Chugwater Formations. A hanging wall splay fault loses separation into the Goose Egg formation which is thinned by 50% at the fault tip. Mesoscopic normal faults are oriented 320--340[degree] and have an average inclination of 75[degree] SW. Megaboudins of Chugwater are present in the footwall of the Hurt Creek fault, immediately adjacent to the fault trace. The Black Canyon fault transported Precambrian-Pennsylvanian rocks over Pennsylvanian Tensleep sandstone. This fault is layer-parallel at the top of the Tensleep and loses separation along strike into an unfaulted syncline in the Goose Egg Formation. Shortening in the pre-Permian units is accommodated by slip on the basement-involved Black Canyon fault. Equivalent shortening in Permian-Cretaceous units occurs on a system of thin-skinned'' thrust faults.

  12. A route generator concept for aircraft onboard fault monitoring

    NASA Technical Reports Server (NTRS)

    Palmer, M. T.; Abbott, K. H.

    1984-01-01

    Because of the increasingly complex environments in which the flight crews of commercial aviation aircraft must operate, a research effort is currently underway at NASA Langley Research Center to investigate the potential benefits of intelligent cockpit aids, and to establish guidelines for the application of artificial intelligence techniques to advanced flight management concepts. The segment of this research area that concentrates on automated fault monitoring and diagnosis requires that a reference frame exist, against which the current state of the aircraft may be compared to determine the existence of a fault. This paper describes a computer program which generates the position of that reference frame that specifies the horizontal flight route.

  13. Fault tolerant linear actuator

    DOEpatents

    Tesar, Delbert

    2004-09-14

    In varying embodiments, the fault tolerant linear actuator of the present invention is a new and improved linear actuator with fault tolerance and positional control that may incorporate velocity summing, force summing, or a combination of the two. In one embodiment, the invention offers a velocity summing arrangement with a differential gear between two prime movers driving a cage, which then drives a linear spindle screw transmission. Other embodiments feature two prime movers driving separate linear spindle screw transmissions, one internal and one external, in a totally concentric and compact integrated module.

  14. Computer hardware fault administration

    DOEpatents

    Archer, Charles J. (Rochester, MN); Megerian, Mark G. (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian E. (Rochester, MN)

    2010-09-14

    Computer hardware fault administration carried out in a parallel computer, where the parallel computer includes a plurality of compute nodes. The compute nodes are coupled for data communications by at least two independent data communications networks, where each data communications network includes data communications links connected to the compute nodes. Typical embodiments carry out hardware fault administration by identifying a location of a defective link in the first data communications network of the parallel computer and routing communications data around the defective link through the second data communications network of the parallel computer.

  15. Cable fault locator research

    NASA Astrophysics Data System (ADS)

    Cole, C. A.; Honey, S. K.; Petro, J. P.; Phillips, A. C.

    1982-07-01

    Cable fault location and the construction of four field test units are discussed. Swept frequency sounding of mine cables with RF signals was the technique most thoroughly investigated. The swept frequency technique is supplemented with a form of moving target indication to provide a method for locating the position of a technician along a cable and relative to a suspected fault. Separate, more limited investigations involved high voltage time domain reflectometry and acoustical probing of mine cables. Particular areas of research included microprocessor-based control of the swept frequency system, a microprocessor based fast Fourier transform for spectral analysis, and RF synthesizers.

  16. Transition-fault test generation 

    E-print Network

    Cobb, Bradley Douglas

    2013-02-22

    . One way to detect these timing defects is to apply test patterns to the integrated circuit that are generated using the transition-fault model. Unfortunately, industry's current transition-fault test generation schemes produce test sets that are too...

  17. Multiple direction vibration fixture

    SciTech Connect

    Cericola, F.; Doggett, J.W.; Ernest, T.L.

    1991-08-27

    An apparatus is discussed for simulating a rocket launch environment on a test item undergoing centrifuge testing by subjecting the item simultaneously or separately to vibration along an axis of centripetal force and along an axis perpendicular to the centripetal force axis. The apparatus includes a shaker motor supported by centrifuge arms and a right angle fixture pivotally connected to one of the shaker motor mounts. When the shaker motor vibrates along the centripetal force axis, the vibrations are imparted to a first side of the right angle fixture. The vibrations are transmitted 90 {degrees} around the pivot and are directed to a second side of the right angle fixture which imparts vibrations perpendicular to the centripetal force axis. The test item is in contact with a third side of the right angle fixture and receives both centripetal-force-axis vibrations and perpendicular axis vibrations simultaneously. A test item can be attached to the third side near the flexible coupling or near the air bag to obtain vibrations along the centripetal force axis or transverse to the centripetal force axis.

  18. Multiple direction vibration fixture

    SciTech Connect

    Cericola, F.; Doggett, J.W.; Ernest, T.L.; Priddy, T.G.

    1990-03-21

    An apparatus for simulating a rocket launch environment on a test item undergoing centrifuge testing by subjecting the item simultaneously or separately to vibration along an axis of centripetal force and along an axis perpendicular to the centripetal force axis. The apparatus includes a shaker motor supported by centrifuge arms and a right angle fixture pivotally connected to one of the shaker motor mounts. When the shaker motor vibrates along the centripetal force axis, the vibrations are imparted to a first side of the right angle fixture. The vibrations are transmitted 90 degrees around the pivot and are directed to a second side of the right angle fixture which imparts vibrations perpendicular to the centripetal force axis. The test item is in contact with a third side of the right angle fixture and receives both centripetal-force-axis vibrations and perpendicular axis vibrations simultaneously. A test item can be attached to the third side near the flexible coupling or near the air bag to obtain vibrations along the centripetal force axis or transverse to the centripetal force axis. 1 fig.

  19. Multi axes vibration fixtures

    NASA Technical Reports Server (NTRS)

    Sims, C. R. (inventor); Taylor, R. C.

    1973-01-01

    A simplified technique and apparatus are described for testing the effects of vibration on various material specimen. Particular attention was given to tests along the orthogonal vibrational planes in order to prove the strength of the item under extraordinary conditions to which it will be subjected.

  20. Vibrational Schroedinger Cats

    NASA Technical Reports Server (NTRS)

    Kis, Z.; Janszky, J.; Vinogradov, An. V.; Kobayashi, T.

    1996-01-01

    The optical Schroedinger cat states are simple realizations of quantum states having nonclassical features. It is shown that vibrational analogues of such states can be realized in an experiment of double pulse excitation of vibrionic transitions. To track the evolution of the vibrational wave packet we derive a non-unitary time evolution operator so that calculations are made in a quasi Heisenberg picture.

  1. Force limited vibration testing

    NASA Technical Reports Server (NTRS)

    Scharton, Terry D.

    1991-01-01

    A new method of conducting lab vibration tests of spacecraft equipment was developed to more closely simulate the vibration environment experienced when the spacecraft is launched on a rocket. The improved tests are tailored to identify equipment design and workmanship problems without inducing artificial failures that would not have occurred at launch. These new, less destructive types of vibration tests are essential to JPL's protoflight test approach in which lab testing is conducted using the flight equipment, often one of a kind, to save time and money. In conventional vibration tests, only the input vibratory motion is specified; the feedback, or reaction force, between the test item and the vibration machine is ignored. Most test failures occur when the test item goes into resonance, and the reaction force becomes very large. It has long been recognized that the large reaction force is a test artifact which does not occur with the lightweight, flexible mounting structures characteristic of spacecraft and space vehicles. In new vibration tests, both the motion and the force provided to the test item by the vibration machine are controlled, so that the vibration ride experienced by the test item is as in flight.

  2. Multiple direction vibration fixture

    DOEpatents

    Cericola, Fred (Albuquerque, NM); Doggett, James W. (Albuquerque, NM); Ernest, Terry L. (Albuquerque, NM); Priddy, Tommy G. (Rockville, MD)

    1991-01-01

    An apparatus for simulating a rocket launch environment on a test item undergoing centrifuge testing by subjecting the item simultaneously or separately to vibration along an axis of centripetal force and along an axis perpendicular to the centripetal force axis. The apparatus includes a shaker motor supported by centrifuge arms and a right angle fixture pivotally connected to one of the shaker motor mounts. When the shaker motor vibrates along the centripetal force axis, the vibrations are imparted to a first side of the right angle fixture. The vibrations are transmitted 90 degrees around the pivot and are directed to a second side of the right angle fixture which imparts vibrations perpendicular to the centripetal force axis. The test item is in contact with a third side of the right angle fixture and receives both centripetal-force-axis vibrations and perpendicular axis vibrations simultaneously. A test item can be attached to the third side near the flexible coupling or near the air bag to obtain vibrations along the centripetal force axis or transverse to the centripetal force axis.

  3. Chaotic extension neural network theory-based XXY stage collision fault detection using a single accelerometer sensor.

    PubMed

    Hsieh, Chin-Tsung; Yau, Her-Terng; Wu, Shang-Yi; Lin, Huo-Cheng

    2014-01-01

    The collision fault detection of a XXY stage is proposed for the first time in this paper. The stage characteristic signals are extracted and imported into the master and slave chaos error systems by signal filtering from the vibratory magnitude of the stage. The trajectory diagram is made from the chaos synchronization dynamic error signals E1 and E2. The distance between characteristic positive and negative centers of gravity, as well as the maximum and minimum distances of trajectory diagram, are captured as the characteristics of fault recognition by observing the variation in various signal trajectory diagrams. The matter-element model of normal status and collision status is built by an extension neural network. The correlation grade of various fault statuses of the XXY stage was calculated for diagnosis. The dSPACE is used for real-time analysis of stage fault status with an accelerometer sensor. Three stage fault statuses are detected in this study, including normal status, Y collision fault and X collision fault. It is shown that the scheme can have at least 75% diagnosis rate for collision faults of the XXY stage. As a result, the fault diagnosis system can be implemented using just one sensor, and consequently the hardware cost is significantly reduced. PMID:25405512

  4. Chaotic Extension Neural Network Theory-Based XXY Stage Collision Fault Detection Using a Single Accelerometer Sensor

    PubMed Central

    Hsieh, Chin-Tsung; Yau, Her-Terng; Wu, Shang-Yi; Lin, Huo-Cheng

    2014-01-01

    The collision fault detection of a XXY stage is proposed for the first time in this paper. The stage characteristic signals are extracted and imported into the master and slave chaos error systems by signal filtering from the vibratory magnitude of the stage. The trajectory diagram is made from the chaos synchronization dynamic error signals E1 and E2. The distance between characteristic positive and negative centers of gravity, as well as the maximum and minimum distances of trajectory diagram, are captured as the characteristics of fault recognition by observing the variation in various signal trajectory diagrams. The matter-element model of normal status and collision status is built by an extension neural network. The correlation grade of various fault statuses of the XXY stage was calculated for diagnosis. The dSPACE is used for real-time analysis of stage fault status with an accelerometer sensor. Three stage fault statuses are detected in this study, including normal status, Y collision fault and X collision fault. It is shown that the scheme can have at least 75% diagnosis rate for collision faults of the XXY stage. As a result, the fault diagnosis system can be implemented using just one sensor, and consequently the hardware cost is significantly reduced. PMID:25405512

  5. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

    PubMed Central

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

  6. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    PubMed

    Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

  7. On-Time Diagnosis of Discrete Event Systems Aditya Mahajan and Demosthenis Teneketzis

    E-print Network

    Mahajan, Aditya

    is stopped when no fault had occurred, a false alarm penalty is incurred; on the other hand if a fault had a monitoring rule which min- imizes the worst case cost along all traces of the language de- scribing the discrete event system. An optimal diagnosis rule is determined using a dynamic programming algorithm. An ex

  8. Vibrational coupled cluster theory

    NASA Astrophysics Data System (ADS)

    Christiansen, Ove

    2004-02-01

    The theory and first implementation of a vibrational coupled cluster (VCC) method for calculations of the vibrational structure of molecules is presented. Different methods for introducing approximate VCC methods are discussed including truncation according to a maximum number of simultaneous mode excitations as well as an interaction space order concept is introduced. The theory is tested on calculation of anharmonic frequencies for a three-mode model system and a formaldehyde quartic force field. The VCC method is compared to vibrational self-consistent-field, vibrational Møller-Plesset perturbation theory, and vibrational configuration interaction (VCI). A VCC calculation typically gives higher accuracy than a corresponding VCI calculation with the same number of parameters and the same formal operation count.

  9. Vibration control in accelerators

    SciTech Connect

    Montag, C.

    2011-01-01

    In the vast majority of accelerator applications, ground vibration amplitudes are well below tolerable magnet jitter amplitudes. In these cases, it is necessary and sufficient to design a rigid magnet support structure that does not amplify ground vibration. Since accelerator beam lines are typically installed at an elevation of 1-2m above ground level, special care has to be taken in order to avoid designing a support structure that acts like an inverted pendulum with a low resonance frequency, resulting in untolerable lateral vibration amplitudes of the accelerator components when excited by either ambient ground motion or vibration sources within the accelerator itself, such as cooling water pumps or helium flow in superconducting magnets. In cases where ground motion amplitudes already exceed the required jiter tolerances, for instance in future linear colliders, passive vibration damping or active stabilization may be considered.

  10. ROBUS-2: A Fault-Tolerant Broadcast Communication System

    NASA Technical Reports Server (NTRS)

    Torres-Pomales, Wilfredo; Malekpour, Mahyar R.; Miner, Paul S.

    2005-01-01

    The Reliable Optical Bus (ROBUS) is the core communication system of the Scalable Processor-Independent Design for Enhanced Reliability (SPIDER), a general-purpose fault-tolerant integrated modular architecture currently under development at NASA Langley Research Center. The ROBUS is a time-division multiple access (TDMA) broadcast communication system with medium access control by means of time-indexed communication schedule. ROBUS-2 is a developmental version of the ROBUS providing guaranteed fault-tolerant services to the attached processing elements (PEs), in the presence of a bounded number of faults. These services include message broadcast (Byzantine Agreement), dynamic communication schedule update, clock synchronization, and distributed diagnosis (group membership). The ROBUS also features fault-tolerant startup and restart capabilities. ROBUS-2 is tolerant to internal as well as PE faults, and incorporates a dynamic self-reconfiguration capability driven by the internal diagnostic system. This version of the ROBUS is intended for laboratory experimentation and demonstrations of the capability to reintegrate failed nodes, dynamically update the communication schedule, and tolerate and recover from correlated transient faults.

  11. Fault compaction and overpressured faults: results from a 3-D model of a ductile fault zone

    NASA Astrophysics Data System (ADS)

    Fitzenz, D. D.; Miller, S. A.

    2003-10-01

    A model of a ductile fault zone is incorporated into a forward 3-D earthquake model to better constrain fault-zone hydraulics. The conceptual framework of the model fault zone was chosen such that two distinct parts are recognized. The fault core, characterized by a relatively low permeability, is composed of a coseismic fault surface embedded in a visco-elastic volume that can creep and compact. The fault core is surrounded by, and mostly sealed from, a high permeability damaged zone. The model fault properties correspond explicitly to those of the coseismic fault core. Porosity and pore pressure evolve to account for the viscous compaction of the fault core, while stresses evolve in response to the applied tectonic loading and to shear creep of the fault itself. A small diffusive leakage is allowed in and out of the fault zone. Coseismically, porosity is created to account for frictional dilatancy. We show in the case of a 3-D fault model with no in-plane flow and constant fluid compressibility, pore pressures do not drop to hydrostatic levels after a seismic rupture, leading to an overpressured weak fault. Since pore pressure plays a key role in the fault behaviour, we investigate coseismic hydraulic property changes. In the full 3-D model, pore pressures vary instantaneously by the poroelastic effect during the propagation of the rupture. Once the stress state stabilizes, pore pressures are incrementally redistributed in the failed patch. We show that the significant effect of pressure-dependent fluid compressibility in the no in-plane flow case becomes a secondary effect when the other spatial dimensions are considered because in-plane flow with a near-lithostatically pressured neighbourhood equilibrates at a pressure much higher than hydrostatic levels, forming persistent high-pressure fluid compartments. If the observed faults are not all overpressured and weak, other mechanisms, not included in this model, must be at work in nature, which need to be investigated. Significant leakage perpendicular to the fault strike (in the case of a young fault), or cracks hydraulically linking the fault core to the damaged zone (for a mature fault) are probable mechanisms for keeping the faults strong and might play a significant role in modulating fault pore pressures. Therefore, fault-normal hydraulic properties of fault zones should be a future focus of field and numerical experiments.

  12. Fault Location and Resolvable Set

    E-print Network

    Tonchev, Vladimir D.

    Fault Location and Resolvable Set Systems Charles J. Colbourn, colbourn@asu.ed with Bingli Fan (Beijing) and Daniel Horsley (Melbourne) Fault Location and Resolvable Set Systems Charles J. Colbourn, and Decision Systems Engineering Arizona State University ACA 2015 #12;Fault Location and Resolvable Set

  13. Decentralized diagnosis in a spacecraft attitude determination and control system

    NASA Astrophysics Data System (ADS)

    Pérez, C. G.; Travé-Massuyès, L.; Chanthery, E.; Sotomayor, J.

    2015-11-01

    In model-based diagnosis (MBD), structural models can provide useful information for fault diagnosis and fault-tolerant control design. In particular, they are known for supporting the design of analytical redundancy relations (ARRs) which are widely used to generate residuals for diagnosis. On the other hand, systems are increasingly complex whereby it is necessary to develop decentralized architectures to perform the diagnosis task. Decentralized diagnosis is of interest for on-board systems as a way to reduce computational costs or for large geographically distributed systems that require to minimizing data transfer. Decentralized solutions allow proper separation of industrial knowledge, provided that inputs and outputs are clearly defined. This paper builds on the results of [1] and proposes an optimized approach for decentralized fault-focused residual generation. It also introduce the concept of Fault-Driven Minimal Structurally-Overdetermined set (FMSO) ensuring minimal redundancy. The method decreases communication cost involved in decentralization with respect to the algorithm proposed in [1] while still maintaining the same isolation properties as the centralized approach as well as the isolation on request capability.

  14. On-line diagnosis of sequential systems, 2

    NASA Technical Reports Server (NTRS)

    Sundstrom, R. J.

    1974-01-01

    The theory and techniques applicable to the on-line diagnosis of sequential systems, were investigated. A complete model for the study of on-line diagnosis is developed. First an appropriate class of system models is formulated which can serve as a basis for a theoretical study of on-line diagnosis. Then notions of realization, fault, fault-tolerance and diagnosability are formalized which have meaningful interpretations in the the context of on-line diagnosis. The diagnosis of systems which are structurally decomposed and are represented as a network of smaller systems is studied. The fault set considered is the set of faults 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.

  15. Row fault detection system

    DOEpatents

    Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

    2012-02-07

    An apparatus, program product and method check for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

  16. Row fault detection system

    DOEpatents

    Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

    2008-10-14

    An apparatus, program product and method checks for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

  17. Row fault detection system

    DOEpatents

    Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

    2010-02-23

    An apparatus and program product check for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

  18. Fault Tolerant Quantum Filtering and Fault Detection for Quantum Systems

    E-print Network

    Qing Gao; Daoyi Dong; Ian R. Petersen

    2015-04-26

    This paper aims to determine the fault tolerant quantum filter and fault detection equation for a class of open quantum systems coupled to laser fields and subject to stochastic faults. In order to analyze open quantum systems where the system dynamics involve both classical and quantum random variables, a quantum-classical probability space model is developed. Using a reference probability approach, a fault tolerant quantum filter and a fault detection equation are simultaneously derived for this class of open quantum systems. An example of two-level open quantum systems subject to Poisson-type faults is presented to illustrate the proposed method. These results have the potential to lead to a new fault tolerant control theory for quantum systems.

  19. Fault Tolerant Quantum Filtering and Fault Detection for Quantum Systems

    E-print Network

    Qing Gao; Daoyi Dong; Ian R. Petersen

    2015-12-21

    This paper aims to determine the fault tolerant quantum filter and fault detection equation for a class of open quantum systems coupled to a laser field that is subject to stochastic faults. In order to analyze this class of open quantum systems, we propose a quantum-classical Bayesian inference method based on the definition of a so-called quantum-classical conditional expectation. It is shown that the proposed Bayesian inference approach provides a convenient tool to simultaneously derive the fault tolerant quantum filter and the fault detection equation for this class of open quantum systems. An example of two-level open quantum systems subject to Poisson-type faults is presented to illustrate the proposed method. These results have the potential to lead to a new fault tolerant control theory for quantum systems.

  20. Fault Scarp Offsets and Fault Population Analysis on Dione

    NASA Astrophysics Data System (ADS)

    Tarlow, S.; Collins, G. C.

    2010-12-01

    Cassini images of Dione show several fault zones cutting through the moon’s icy surface. We have measured the displacement and length of 271 faults, and estimated the strain occurring in 6 different fault zones. These measurements allow us to quantify the total amount of surface strain on Dione as well as constrain what processes might have caused these faults to form. Though we do not have detailed topography across fault scarps on Dione, we can use their projected size on the camera plane to estimate their heights, assuming a reasonable surface slope. Starting with high resolution images of Dione obtained by the Cassini ISS, we marked points at the top to the bottom of each fault scarp to measure the fault’s projected displacement and its orientation along strike. Line and sample information for the measurements were then processed through ISIS to derive latitude/longitude information and pixel dimensions. We then calculate the three dimensional orientation of a vector running from the bottom to the top of the fault scarp, assuming a 45 degree angle with respect to the surface, and project this vector onto the spacecraft camera plane. This projected vector gives us a correction factor to estimate the actual vertical displacement of the fault scarp. This process was repeated many times for each fault, to show variations of displacement along the length of the fault. To compare each fault to its neighbors and see how strain was accommodated across a population of faults, we divided the faults into fault zones, and created new coordinate systems oriented along the central axis of each fault zone. We could then quantify the amount of fault overlap and add the displacement of overlapping faults to estimate the amount of strain accommodated in each zone. Faults in the southern portion of Padua have a strain of 0.031(+/-) 0.0097, central Padua exhibits a strain of .032(+/-) 0.012, and faults in northern Padua have a strain of 0.025(+/-) 0.0080. The western faults of Eurotas have a strain of 0.031(+/-) 0.011, while the eastern faults have a strain of 0.037(+/-) 0.025. Lastly, Clusium has a strain of 0.10 (+/-) 0.029. We also calculated the ratio of maximum fault displacement vs. the length of the faults, and we found this ratio to be 0.019 when drawing a trend line through all the faults that were analyzed. D/L measurements performed on two faults on Europa using stereo topography showed a value of .021 (Nimmo and Schenk 2006), the only other icy satellite where this ratio has been measured. In contrast, faults on Earth has a D/L ratio of about .1 and Mars has a D/L Ratio of about .01 (Schultz et al. 2006).

  1. Fault-Related Sanctuaries

    NASA Astrophysics Data System (ADS)

    Piccardi, L.

    2001-12-01

    Beyond the study of historical surface faulting events, this work investigates the possibility, in specific cases, of identifying pre-historical events whose memory survives in myths and legends. The myths of many famous sacred places of the ancient world contain relevant telluric references: "sacred" earthquakes, openings to the Underworld and/or chthonic dragons. Given the strong correspondence with local geological evidence, these myths may be considered as describing natural phenomena. It has been possible in this way to shed light on the geologic origin of famous myths (Piccardi, 1999, 2000 and 2001). Interdisciplinary researches reveal that the origin of several ancient sanctuaries may be linked in particular to peculiar geological phenomena observed on local active faults (like ground shaking and coseismic surface ruptures, gas and flames emissions, strong underground rumours). In many of these sanctuaries the sacred area is laid directly above the active fault. In a few cases, faulting has affected also the archaeological relics, right through the main temple (e.g. Delphi, Cnidus, Hierapolis of Phrygia). As such, the arrangement of the cult site and content of relative myths suggest that specific points along the trace of active faults have been noticed in the past and worshiped as special `sacred' places, most likely interpreted as Hades' Doors. The mythological stratification of most of these sanctuaries dates back to prehistory, and points to a common derivation from the cult of the Mother Goddess (the Lady of the Doors), which was largely widespread since at least 25000 BC. The cult itself was later reconverted into various different divinities, while the `sacred doors' of the Great Goddess and/or the dragons (offspring of Mother Earth and generally regarded as Keepers of the Doors) persisted in more recent mythologies. Piccardi L., 1999: The "Footprints" of the Archangel: Evidence of Early-Medieval Surface Faulting at Monte Sant'Angelo (Gargano, Italy). European Union of Geophysics Congress, Strasbourg, March 1999. Piccardi L., 2000: Active faulting at Delphi (Greece): seismotectonic remarks and a hypothesis for the geological environment of a myth. Geology, 28, 651-654. Piccardi L., 2001: Seismotectonic Origin of the Monster of Loch Ness. Earth System Processes, Joint Meeting of G.S.A. and G.S.L., Edinburgh, June 2001.

  2. Electrorheological vibration system

    NASA Astrophysics Data System (ADS)

    Korobko, Evguenia V.; Shulman, Zinovy P.; Korobko, Yulia O.

    2001-07-01

    The present paper is devoted to de3velopment and testing of an active vibration system. The system is intended for providing efficient motion of a piston in a hydraulic channel for creation of shocks and periodic vibrations in a low frequency range by means of the ER-valves based on an electrosensitive working me dium, i.e. electrorheological fluids. The latter manifests the electrorheological (ER) effect, i.e. a reversible change in the rheological characteristics of weak-conducting disperse compositions in the presence of constant and alternating electric fields. As a result of the experimental study of the dependence of viscoelastic properties of the ER-fluid on the magnitude and type of an electric field, the optimum dimensions of the vibrator and the its valves characteristics of the optimal electrical signal are determined. For control of an ER- vibrator having several valves we have designed a special type of a high-voltage two-channel impulse generator. Experiments were conducted at the frequencies ranged from 1- 10 Hz. It has been shown, that a peak force made 70% of the static force exercised by the vibrator rod. A phase shift between the input voltage and the load acceleration was less than 45 degree(s)C which allowed servocontrol and use of the vibrator for attendant operations. It was noted that a response of the vibrator to a stepwise signal has a delay only of several milliseconds.

  3. Fault diagnosis via neural networks: The Boltzmann machine

    SciTech Connect

    Marseguerra, M.; Zio, E. . Dept. of Nuclear Engineering)

    1994-07-01

    The Boltzmann machine is a general-purpose artificial neural network that can be used as an associative memory as well as a mapping tool. The usual information entropy is introduced, and a network energy function is suitably defined. The network's training procedure is based on the simulated annealing during which a combination of energy minimization and entropy maximization is achieved. An application in the nuclear reactor field is presented in which the Boltzmann input-output machine is used to detect and diagnose a pipe break in a simulated auxiliary feedwater system feeding two coupled steam generators. The break may occur on either the hot or the cold leg of any of the two steam generators. The binary input data to the network encode only the trends of the thermohydraulic signals so that the network is actually a polarity device. The results indicate that the trained neural network is actually capable of performing its task. The method appears to be robust enough so that it may also be applied with success in the presence of substantial amounts of noise that cause the network to be fed with wrong signals.

  4. Diagnosis errors are a system problem, not just doctor's fault.

    PubMed

    2015-11-01

    The Institute of Medicine has issued a report calling on the medical community to more effectively address diagnostic errors. Reducing these errors will require a collaborative approach. Diagnostic errors are not typically caused by only a physician's error. Radiologists and pathologists should be more involved with diagnoses. Risk managers should treat diagnostic errors as a system problem. PMID:26565056

  5. Vehicle Fault Diagnose Based on Smart Sensor

    NASA Astrophysics Data System (ADS)

    Zhining, Li; Peng, Wang; Jianmin, Mei; Jianwei, Li; Fei, Teng

    In the vehicle's traditional fault diagnose system, we usually use a computer system with a A/D card and with many sensors connected to it. The disadvantage of this system is that these sensor can hardly be shared with control system and other systems, there are too many connect lines and the electro magnetic compatibility(EMC) will be affected. In this paper, smart speed sensor, smart acoustic press sensor, smart oil press sensor, smart acceleration sensor and smart order tracking sensor were designed to solve this problem. With the CAN BUS these smart sensors, fault diagnose computer and other computer could be connected together to establish a network system which can monitor and control the vehicle's diesel and other system without any duplicate sensor. The hard and soft ware of the smart sensor system was introduced, the oil press, vibration and acoustic signal are resampled by constant angle increment to eliminate the influence of the rotate speed. After the resample, the signal in every working cycle could be averaged in angle domain and do other analysis like order spectrum.

  6. Vibration Analysis and the Accelerometer

    ERIC Educational Resources Information Center

    Hammer, Paul

    2011-01-01

    Have you ever put your hand on an electric motor or motor-driven electric appliance and felt it vibrate? Ever wonder why it vibrates? What is there about the operation of the motor, or the object to which it is attached, that causes the vibrations? Is there anything "regular" about the vibrations, or are they the result of random causes? In this…

  7. Damping Vibration at an Impeller

    NASA Technical Reports Server (NTRS)

    Hager, J. A.; Rowan, B. F.

    1982-01-01

    Vibration of pump shaft is damped at impeller--where vibration-induced deflections are greatest--by shroud and seal. Damping reduces vibrational motion of shaft at bearings and load shaft places on them. Flow through clearance channel absorbs vibration energy.

  8. Qualitative model-based diagnosis using possibility theory

    NASA Technical Reports Server (NTRS)

    Joslyn, Cliff

    1994-01-01

    The potential for the use of possibility in the qualitative model-based diagnosis of spacecraft systems is described. The first sections of the paper briefly introduce the Model-Based Diagnostic (MBD) approach to spacecraft fault diagnosis; Qualitative Modeling (QM) methodologies; and the concepts of possibilistic modeling in the context of Generalized Information Theory (GIT). Then the necessary conditions for the applicability of possibilistic methods to qualitative MBD, and a number of potential directions for such an application, are described.

  9. [Clinical diagnosis].

    PubMed

    Furukawa, Katsutoshi; Kamada, Maki; Ishiki, Aiko; Tomita, Naoki; Arai, Hiroyuki

    2014-04-01

    The commonly followed definition of dementia is the one described by the International Statistical Classification of Diseases, 10th Revision (ICD-10, World Health Organization) or the Diagnostic and Statistical Manual of Mental Disorders (DSM-V, American Psychiatric Association). The most important aspect in the diagnosis of dementia is the assessment of overall mental and functions, including living environment, activities of daily living, cognition, mental status, and behavior. Physicians should diagnose dementia on the basis of not only cognitive test results or radiological findings but also other available information, including that obtained from the families or caregivers. Tests for the quantitative evaluation of cognitive function and dementia include the Mini-Mental State Examination (MMSE), Hasegawa Dementia Scale Revised (HDS-R), Clinical Dementia Rating (CDR), and Wechsler Memory Scale-Revised (WMS-R). PMID:24796095

  10. Holocene faulting on the Mission fault, northwest Montana

    SciTech Connect

    Ostenaa, D.A.; Klinger, R.E.; Levish, D.R. )

    1993-04-01

    South of Flathead Lake, fault scarps on late Quaternary surfaces are nearly continuous for 45 km along the western flank of the Mission Range. On late Pleistocene alpine lateral moraines, scarp heights reach a maximum of 17 m. Scarp heights on post glacial Lake Missoula surfaces range from 2.6--7.2 m and maximum scarp angles range from 10[degree]--24[degree]. The stratigraphy exposed in seven trenches across the fault demonstrates that the post glacial Lake Missoula scarps resulted from at least two surface-faulting events. Larger scarp heights on late Pleistocene moraines suggests a possible third event. This yields an estimated recurrence of 4--8 kyr. Analyses of scarp profiles show that the age of the most surface faulting is middle Holocene, consistent with stratigraphic evidence found in the trenches. Rupture length and displacement imply earthquake magnitudes of 7 to 7.5. Previous studies have not identified geologic evidence of late Quaternary surface faulting in the Rocky Mountain Trench or on faults north of the Lewis and Clark line despite abundant historic seismicity in the Flathead Lake area. In addition to the Mission fault, reconnaissance studies have located late Quaternary fault scarps along portions of faults bordering Jocko and Thompson Valleys. These are the first documented late Pleistocene/Holocene faults north of the Lewis and Clark line in Montana and should greatly revise estimates of earthquake hazards in this region.

  11. Methods for quantitatively determining fault slip using fault separation

    NASA Astrophysics Data System (ADS)

    Xu, S.-S.; Velasquillo-Martínez, L. G.; Grajales-Nishimura, J. M.; Murillo-Muñetón, G.; Nieto-Samaniego, A. F.

    2007-10-01

    Fault slip and fault separation are generally not equal to each other, however, they are geometrically related. The fault slip ( S) is a vector with a magnitude, a direction, and a sense of the movement. In this paper, a series of approaches are introduced to estimate quantitatively the magnitude and direction of the fault slip using fault separations. For calculation, the known factors are the pitch of slip lineations ( ?), the pitch of a cutoff ( ?), the dip separation ( Smd) or the strike separation ( Smh) for one marker. The two main purposes of this work include: (1) to analyze the relationship between fault slip and fault separation when slickenside lineations of a fault are known; (2) to estimate the slip direction when the parameters Smd or Smh, and ? for two non-parallel markers at a place (e.g., a point) are known. We tested the approaches using an example from a mainly strike-slip fault in East Quantoxhead, United Kingdom, and another example from the Jordan Field, Ector County, Texas. Also, we estimated the relative errors of apparent heave of the normal faults from the Sierra de San Miguelito, central Mexico.

  12. Dynamic modeling of injection-induced fault reactivation and ground motion and impact on surface structures and human perception

    SciTech Connect

    Rutqvist, Jonny; Cappa, Frederic; Rinaldi, Antonio P.; Godano, Maxime

    2014-12-31

    We summarize recent modeling studies of injection-induced fault 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 vibration criteria from mining and construction industries. We first simulated injection-induced fault reactivation, including dynamic fault slip, seismic source, wave propagation, and ground vibrations. From co-seismic average shear displacement and rupture area, we determined the moment magnitude to about Mw = 3 for an injection-induced fault reactivation at a depth of about 1000 m. We then analyzed the ground vibration results in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and frequency content, with comparison to the U.S. Bureau of Mines’ vibration criteria for cosmetic damage to buildings, as well as human-perception vibration limits. For the considered synthetic Mw = 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 CO2 leakage, but would certainly be felt by the local population.

  13. Dynamic modeling of injection-induced fault reactivation and ground motion and impact on surface structures and human perception

    DOE PAGESBeta

    Rutqvist, Jonny; Cappa, Frederic; Rinaldi, Antonio P.; Godano, Maxime

    2014-12-31

    We summarize recent modeling studies of injection-induced fault 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 vibration criteria from mining and construction industries. We first simulated injection-induced fault reactivation, including dynamic fault slip, seismic source, wave propagation, and ground vibrations. From co-seismic average shear displacement and rupture area, we determined the moment magnitude to about Mw = 3 for an injection-induced fault reactivation at a depth of about 1000 m. We then analyzed themore »ground vibration results in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and frequency content, with comparison to the U.S. Bureau of Mines’ vibration criteria for cosmetic damage to buildings, as well as human-perception vibration limits. For the considered synthetic Mw = 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 CO2 leakage, but would certainly be felt by the local population.« less

  14. Chiari Malformation: Diagnosis

    MedlinePLUS

    ... light touch, a tuning fork to test for vibration, and pin pricks. The exam will test sensation ... resonance imaging process involves M agnets, R esonance (vibration), and I maging (creating pictures). What to expect: ...

  15. Monitor based fault management in a distributed environment

    SciTech Connect

    Lim, Hyung-Taek; Yang, Seung-Min; Kim, Moon Hae

    1996-12-31

    The use of computers and communication networks has been rapidly increasing in distributed applications. Most distributed applications require highly dependable (or reliable) system operations. In order to guarantee highly dependable operations of the system in a distributed environment, system-level diagnosis and run-time error (or fault) recovery mechanisms should be provided in the system. In order to support the system-level diagnosis capability, we have proposed a mechanism, call the monitor, to detect faults in a system and to diagnose them. In this paper, we present the design and implementation of the monitor. Also, we show the effectiveness of our monitor approach by applying it to an intelligent vehicle system (IVS).

  16. Dynamic faulting on a conjugate fault system detected by near-fault tilt measurements

    NASA Astrophysics Data System (ADS)

    Fukuyama, Eiichi

    2015-12-01

    There have been reports of conjugate faults that have ruptured during earthquakes. However, it is still unclear whether or not these conjugate faults ruptured coseismically during earthquakes. In this paper, we investigated near-fault ground tilt motions observed at the IWTH25 station during the 2008 Iwate-Miyagi Nairiku earthquake ( M w 6.9). Since near-fault tilt motion is very sensitive to the fault geometry on which the slip occurs during an earthquake, these data make it possible to distinguish between the main fault rupture and a rupture on the conjugate fault. We examined several fault models that have already been proposed and confirmed that only the models with a conjugated fault could explain the tilt data observed at IWTH25. The results support the existence of simultaneous conjugate faulting during the main rupture. This will contribute to the understanding of earthquake rupture dynamics because the conjugate rupture releases the same shear strain as that released on the main fault, and thus it has been considered quite difficult for both ruptures to accelerate simultaneously.

  17. An online outlier identification and removal scheme for improving fault detection performance.

    PubMed

    Ferdowsi, Hasan; Jagannathan, Sarangapani; Zawodniok, Maciej

    2014-05-01

    Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be used to remove the outliers. The OIR scheme uses a neural network (NN) to estimate the actual system states from measured system states involving outliers. With this method, the outlier detection is performed online at each time instant by finding the difference between the estimated and the measured states and comparing its median with its standard deviation over a moving time window. The NN weight update law in OIR is designed such that the detected outliers will have no effect on the state estimation, which is subsequently used for model-based fault diagnosis. In addition, since the OIR estimator cannot distinguish between the faulty or healthy operating conditions, a separate model-based observer is designed for fault diagnosis, which uses the OIR scheme as a preprocessing unit to improve the FD performance. The stability analysis of both OIR and fault diagnosis schemes are introduced. Finally, a three-tank benchmarking system and a simple linear system are used to verify the proposed scheme in simulations, and then the scheme is applied on an axial piston pump testbed. The scheme can be applied to nonlinear systems whose dynamics and underlying distribution of states are subjected to change due to both unknown faults and operating conditions. PMID:24808037

  18. 2008 Vibrational Spectroscopy

    SciTech Connect

    Philip J. Reid

    2009-09-21

    The conference focuses on using vibrational spectroscopy to probe structure and dynamics of molecules in gases, liquids, and interfaces. The goal is to bring together a collection of researchers who share common interests and who will gain from discussing work at the forefront of several connected areas. The intent is to emphasize the insights and understanding that studies of vibrations provide about a variety of systems.

  19. Managing Fault Management Development

    NASA Technical Reports Server (NTRS)

    McDougal, John M.

    2010-01-01

    As the complexity of space missions grows, development of Fault Management (FM) capabilities is an increasingly common driver for significant cost overruns late in the development cycle. FM issues and the resulting cost overruns are rarely caused by a lack of technology, but rather by a lack of planning and emphasis by project management. A recent NASA FM Workshop brought together FM practitioners from a broad spectrum of institutions, mission types, and functional roles to identify the drivers underlying FM overruns and recommend solutions. They identified a number of areas in which increased program and project management focus can be used to control FM development cost growth. These include up-front planning for FM as a distinct engineering discipline; managing different, conflicting, and changing institutional goals and risk postures; ensuring the necessary resources for a disciplined, coordinated approach to end-to-end fault management engineering; and monitoring FM coordination across all mission systems.

  20. Randomness fault detection system

    NASA Technical Reports Server (NTRS)

    Russell, B. Don (Inventor); Aucoin, B. Michael (Inventor); Benner, Carl L. (Inventor)

    1996-01-01

    A method and apparatus are provided for detecting a fault on a power line carrying a line parameter such as a load current. The apparatus monitors and analyzes the load current to obtain an energy value. The energy value is compared to a threshold value stored in a buffer. If the energy value is greater than the threshold value a counter is incremented. If the energy value is greater than a high value threshold or less than a low value threshold then a second counter is incremented. If the difference between two subsequent energy values is greater than a constant then a third counter is incremented. A fault signal is issued if the counter is greater than a counter limit value and either the second counter is greater than a second limit value or the third counter is greater than a third limit value.

  1. Vibration condition monitoring of planetary gearbox under varying external load

    NASA Astrophysics Data System (ADS)

    Bartelmus, W.; Zimroz, R.

    2009-01-01

    The paper shows that for condition monitoring of planetary gearboxes it is important to identify the external varying load condition. In the paper, systematic consideration has been taken of the influence of many factors on the vibration signals generated by a system in which a planetary gearbox is included. These considerations give the basis for vibration signal interpretation, development of the means of condition monitoring, and for the scenario of the degradation of the planetary gearbox. Real measured vibration signals obtained in the industrial environment are processed. The signals are recorded during normal operation of the diagnosed objects, namely planetary gearboxes, which are a part of the driving system used in a bucket wheel excavator, used in lignite mines. It is found that a planetary gearbox in bad condition is more susceptible to load than a gearbox in good condition. The estimated load time traces obtained by a demodulation process of the vibration acceleration signal for a planetary gearbox in good and bad conditions are given. It has been found that the most important factor of the proper planetary gearbox condition is connected with perturbation of arm rotation, where an arm rotation gives rise to a specific vibration signal whose properties are depicted by a short-time Fourier transform (STFT) and Wigner-Ville distribution presented as a time frequency map. The paper gives evidence that there are two dominant low-frequency causes that influence vibration signal modulation, i.e. the varying load, which comes from the nature of the bucket wheel digging process, and the arm/carrier rotation. These two causes determine the condition of the planetary gearboxes considered. Typical local faults such as cracking or breakage of a gear tooth, or local faults in rolling element bearings, have not been found in the cases considered. In real practice, local faults of planetary gearboxes have not occurred, but heavy destruction of planetary gearboxes have been noticed, which are caused by a prolonged run of a planetary gearbox at the condition of the arm run perturbation. It may be stated that the paper gives a new approach to the condition monitoring of planetary gearboxes. It has been shown that only a root cause analysis based on factors having an influence on the vibration solves the problem of planetary gearbox condition monitoring.

  2. Fault tolerant control laws

    NASA Technical Reports Server (NTRS)

    Ly, U. L.; Ho, J. K.

    1986-01-01

    A systematic procedure for the synthesis of fault tolerant control laws to actuator failure has been presented. Two design methods were used to synthesize fault tolerant controllers: the conventional LQ design method and a direct feedback controller design method SANDY. The latter method is used primarily to streamline the full-state Q feedback design into a practical implementable output feedback controller structure. To achieve robustness to control actuator failure, the redundant surfaces are properly balanced according to their control effectiveness. A simple gain schedule based on the landing gear up/down logic involving only three gains was developed to handle three design flight conditions: Mach .25 and Mach .60 at 5000 ft and Mach .90 at 20,000 ft. The fault tolerant control law developed in this study provides good stability augmentation and performance for the relaxed static stability aircraft. The augmented aircraft responses are found to be invariant to the presence of a failure. Furthermore, single-loop stability margins of +6 dB in gain and +30 deg in phase were achieved along with -40 dB/decade rolloff at high frequency.

  3. This paper compares two fault injection techniques: scan chain implemented fault injection (SCIFI), i.e. fault

    E-print Network

    Karlsson, Johan

    Abstract This paper compares two fault injection techniques: scan chain implemented fault injection (SCIFI), i.e. fault injection in a physical system using built in test logic, and fault injection in a VHDL software simulation model of a system. The fault injections were used to evaluate the error

  4. Force Limited Vibration Testing

    NASA Technical Reports Server (NTRS)

    Scharton, Terry; Chang, Kurng Y.

    2005-01-01

    This slide presentation reviews the concept and applications of Force Limited Vibration Testing. The goal of vibration testing of aerospace hardware is to identify problems that would result in flight failures. The commonly used aerospace vibration tests uses artificially high shaker forces and responses at the resonance frequencies of the test item. It has become common to limit the acceleration responses in the test to those predicted for the flight. This requires an analysis of the acceleration response, and requires placing accelerometers on the test item. With the advent of piezoelectric gages it has become possible to improve vibration testing. The basic equations have are reviewed. Force limits are analogous and complementary to the acceleration specifications used in conventional vibration testing. Just as the acceleration specification is the frequency spectrum envelope of the in-flight acceleration at the interface between the test item and flight mounting structure, the force limit is the envelope of the in-flight force at the interface . In force limited vibration tests, both the acceleration and force specifications are needed, and the force specification is generally based on and proportional to the acceleration specification. Therefore, force limiting does not compensate for errors in the development of the acceleration specification, e.g., too much conservatism or the lack thereof. These errors will carry over into the force specification. Since in-flight vibratory force data are scarce, force limits are often derived from coupled system analyses and impedance information obtained from measurements or finite element models (FEM). Fortunately, data on the interface forces between systems and components are now available from system acoustic and vibration tests of development test models and from a few flight experiments. Semi-empirical methods of predicting force limits are currently being developed on the basis of the limited flight and system test data. A simple two degree of freedom system is shown and the governing equations for basic force limiting results for this system are reviewed. The design and results of the shuttle vibration forces (SVF) experiments are reviewed. The Advanced Composition Explorer (ACE) also was used to validate force limiting. Test instrumentation and supporting equipment are reviewed including piezo-electric force transducers, signal processing and conditioning systems, test fixtures, and vibration controller systems. Several examples of force limited vibration testing are presented with some results.

  5. An Event-Based Approach to Distributed Diagnosis of Continuous Systems

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

    2010-01-01

    Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

  6. Evaluating Fault Management Operations Concepts for Next-Generation Spacecraft: What Eye Movements Tell Us

    NASA Technical Reports Server (NTRS)

    Hayashi, Miwa; Ravinder, Ujwala; McCann, Robert S.; Beutter, Brent; Spirkovska, Lily

    2009-01-01

    Performance enhancements associated with selected forms of automation were quantified in a recent human-in-the-loop evaluation of two candidate operational concepts for fault management on next-generation spacecraft. The baseline concept, called Elsie, featured a full-suite of "soft" fault management interfaces. However, operators were forced to diagnose malfunctions with minimal assistance from the standalone caution and warning system. The other concept, called Besi, incorporated a more capable C&W system with an automated fault diagnosis capability. Results from analyses of participants' eye movements indicate that the greatest empirical benefit of the automation stemmed from eliminating the need for text processing on cluttered, text-rich displays.

  7. Self-calibrating models for dynamic monitoring and diagnosis

    NASA Technical Reports Server (NTRS)

    Kuipers, Benjamin

    1996-01-01

    A method for automatically building qualitative and semi-quantitative models of dynamic systems, and using them for monitoring and fault diagnosis, is developed and demonstrated. The qualitative approach and semi-quantitative method are applied to monitoring observation streams, and to design of non-linear control systems.

  8. 20 CFR 410.561b - Fault.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 20 Employees' Benefits 2 2011-04-01 2011-04-01 false Fault. 410.561b Section 410.561b Employees... LUNG BENEFITS (1969- ) Payment of Benefits § 410.561b Fault. Fault as used in without fault (see § 410.561a) applies only to the individual. Although the Administration may have been at fault in making...

  9. 20 CFR 404.507 - Fault.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... Officer § 404.507 Fault. Fault as used in without fault (see § 404.506 and 42 CFR 405.355) applies only to the individual. Although the Administration may have been at fault in making the overpayment, that... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Fault. 404.507 Section 404.507...

  10. 20 CFR 404.507 - Fault.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... Officer § 404.507 Fault. Fault as used in without fault (see § 404.506 and 42 CFR 405.355) applies only to the individual. Although the Administration may have been at fault in making the overpayment, that... 20 Employees' Benefits 2 2011-04-01 2011-04-01 false Fault. 404.507 Section 404.507...

  11. 20 CFR 410.561b - Fault.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Fault. 410.561b Section 410.561b Employees... LUNG BENEFITS (1969- ) Payment of Benefits § 410.561b Fault. Fault as used in without fault (see § 410.561a) applies only to the individual. Although the Administration may have been at fault in making...

  12. 20 CFR 404.507 - Fault.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... Officer § 404.507 Fault. Fault as used in without fault (see § 404.506 and 42 CFR 405.355) applies only to the individual. Although the Administration may have been at fault in making the overpayment, that... 20 Employees' Benefits 2 2014-04-01 2014-04-01 false Fault. 404.507 Section 404.507...

  13. 20 CFR 404.507 - Fault.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... Officer § 404.507 Fault. Fault as used in without fault (see § 404.506 and 42 CFR 405.355) applies only to the individual. Although the Administration may have been at fault in making the overpayment, that... 20 Employees' Benefits 2 2012-04-01 2012-04-01 false Fault. 404.507 Section 404.507...

  14. 20 CFR 404.507 - Fault.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... Officer § 404.507 Fault. Fault as used in without fault (see § 404.506 and 42 CFR 405.355) applies only to the individual. Although the Administration may have been at fault in making the overpayment, that... 20 Employees' Benefits 2 2013-04-01 2013-04-01 false Fault. 404.507 Section 404.507...

  15. Web-Centric Diagnosis and Prediction System for Global Manufacturing Mihaela Ulieru

    E-print Network

    Ulieru, Mihaela

    Web-Centric Diagnosis and Prediction System for Global Manufacturing Mihaela Ulieru Electrical://isg.enme.ucalgary.ca/ Abstract. A FIPA-enabled diagnosis and prediction system is proposed for dealing with disturbances schedule according to the fault predictions it perform while evaluating the evolution in time

  16. A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

    NASA Technical Reports Server (NTRS)

    Tejada, Arturo

    2009-01-01

    An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault 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 vibrational techniques to detect faults 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 vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault 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.

  17. Construction of customized redundant multiwavelet via increasing multiplicity for fault detection of rotating machinery

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Zuo, Ming J.; Zi, Yanyang; He, Zhengjia

    2014-01-01

    Fault detection from the vibration measurement data of rotating machinery is significant for avoiding serious accidents. However, non-stationary vibration signal with a large amount of noise makes this task challenging. Multiwavelet not only owns the advantage on multi-resolution analysis but also can offer multiple wavelet basis functions. So it has the possibility of detecting various fault features preferably. However, the fixed basis functions which are not related to the given signal may lower the accuracy of fault detection. Moreover, another major intrinsic deficiency of multiwavelet lies in its critically sampled filter-bank, which causes shift-variance and is harmful to extract the feature of periodical impulses. To overcome these deficiencies, a new method called customized redundant multiwavelet (CRM) is constructed via increasing multiplicity (IM). IM is a simple method to design a series of changeable multiwavelet which are available for the subsequent optimization process. By the rule of the envelope spectrum entropy minimum principle, optimal multiwavelet is searched for. Based on the customized multiwavelet filters, the filters of CRM can be calculated by inserting zeros. The proposed method is applied to analyze the simulation, gearbox and rolling element bearing vibration signals. Compared with some other conventional methods, the results demonstrate that the proposed method possesses robust performance in detecting fault features of rotating machinery.

  18. Plague Diagnosis and Treatment

    MedlinePLUS

    ... Officials Veterinarians Prevention History of Plague Resources FAQ Diagnosis and Treatment Recommend on Facebook Tweet Share Compartir Detailed Diagnosis and Treatment Recommendations Diagnosis Doctors examining a bubo caused by plague. Plague ...

  19. Fault interaction near Hollister, California

    NASA Astrophysics Data System (ADS)

    Mavko, Gerald M.

    1982-09-01

    A numerical model is used to study fault stress and slip near Hollister, California. The geometrically complex system of interacting faults, including the San Andreas, Calaveras, Sargent, and Busch faults, is approximated with a two-dimensional distribution of short planar fault segments in an elastic medium. The steady stress and slip rate are simulated by specifying frictional strength and stepping the remote stress ahead in time. The resulting computed fault stress is roughly proportional to the observed spatial density of small earthquakes, suggesting that the distinction between segments characterized by earthquakes and those with aseismic creep results, in part, from geometry. A nosteady simulation is made by introducing, in addition, stress drops for individual moderate earthquakes. A close fit of observed creep with calculated slip on the Calaveras and San Andreas faults suggests that many changes in creep rate (averaged over several months) are caused by local moderate earthquakes. In particular, a 3-year creep lag preceding the August 6, 1979, Coyote Lake earthquake on the Calaveras fault seems to have been a direct result of the November 28, 1974, Thanksgiving Day earthquake on the Busch fault. Computed lags in slip rate preceding some other moderate earthquakes in the area are also due to earlier earthquakes. Although the response of the upper 1 km of the fault zone may cause some individual creep events and introduce delays in others, the long-term rate appears to reflect deep slip.

  20. Anisotropic origin of transform faults.

    PubMed

    Freund, R; Merzer, A M

    1976-04-01

    Transform faults appear in the process of stretching during freezing of the surface films on liquid wax. These films are composed of a warp yarn of wax fibers with optical anisotropy. This fabric is absent in materials that fail to produce transform faults. The mechanical anisotropy of these wax films (with high tensile strength and low shear strength in the direction of spreading) is responsible for the initiation of the transform faults. It is suggested that the anisotropy of the ocean upper mantle recorded seismically may likewise be responsible for the creation of the ridge-ridge transform faults in the oceans. PMID:17792444

  1. Vibration modelling and verifications for whole aero-engine

    NASA Astrophysics Data System (ADS)

    Chen, G.

    2015-08-01

    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 faults 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-? method and the improved Newmark-? 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) fault simulations for a certain type of missile turbofan aero-engine vibration. The results show that the proposed model can not only simulate the natural vibration characteristics of the whole aero-engine but also effectively perform nonlinear dynamic simulations of a whole aero-engine with faults.

  2. Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Zi, Yanyang; He, Zhengjia; Yuan, Jing

    2013-07-01

    Due to the character of diversity and complexity, the compound faults detection of rotating machinery under non-stationary operation turns into a challenging task. Multiwavelet with two or more base functions and many excellent properties provides a possibility to detect and extract all the features of compound faults at one time. However, the fixed basis functions independent of the vibration signal may decrease the accuracy of fault detection. Moreover, the decomposition result of discrete multiwavelet transform does not possess time invariance, which is harmful to extract the feature of periodical impulses. To overcome these deficiencies, based on the Hermite splines interpolation, taking the minimum envelope spectrum entropy as the optimization objective, adaptive redundant lifting multiwavelet is developed. Additionally, in order to eliminate error propagation of decomposition results, adaptive redundant lifting multiwavelet is improved by adding the normalization factors. As an effective method, Hilbert transform demodulation analysis is used to extract the fault feature from the high frequency modulation signal. The proposed method incorporating improved adaptive redundant lifting multiwavelet (IARLM) with Hilbert transform demodulation analysis is applied to compound faults detection for the simulation experiment, rolling element bearing test bench and traveling unit of electric locomotive. Compared with some other fault detection methods, the results show the superior effectiveness and reliability on the compound faults detection.

  3. Fault Branching and Rupture Directivity

    NASA Astrophysics Data System (ADS)

    Dmowska, R.; Rice, J. R.; Kame, N.

    2002-12-01

    Can the rupture directivity of past earthquakes be inferred from fault geometry? Nakata et al. [J. Geogr., 1998] propose to relate the observed surface branching of fault systems with directivity. Their work assumes that all branches are through acute angles in the direction of rupture propagation. However, in some observed cases rupture paths seem to branch through highly obtuse angles, as if to propagate ``backwards". Field examples of that are as follows: (1) Landers 1992. When crossing from the Johnson Valley to the Homestead Valley (HV) fault via the Kickapoo (Kp) fault, the rupture from Kp progressed not just forward onto the northern stretch of the HV fault, but also backwards, i.e., SSE along the HV [Sowers et al., 1994, Spotila and Sieh, 1995, Zachariasen and Sieh, 1995, Rockwell et al., 2000]. Measurements of surface slip along that backward branch, a prominent feature of 4 km length, show right-lateral slip, decreasing towards the SSE. (2) At a similar crossing from the HV to the Emerson (Em) fault, the rupture progressed backwards along different SSE splays of the Em fault [Zachariasen and Sieh, 1995]. (3). In crossing from the Em to Camp Rock (CR) fault, again, rupture went SSE on the CR fault. (4). Hector Mine 1999. The rupture originated on a buried fault without surface trace [Li et al., 2002; Hauksson et al., 2002] and progressed bilaterally south and north. In the south it met the Lavic Lake (LL) fault and progressed south on it, but also progressed backward, i.e. NNW, along the northern stretch of the LL fault. The angle between the buried fault and the northern LL fault is around -160o, and that NNW stretch extends around 15 km. The field examples with highly obtuse branch angles suggest that there may be no simple correlation between fault geometry and rupture directivity. We propose that an important distinction is whether those obtuse branches actually involved a rupture path which directly turned through the obtuse angle (while continuing also on the main fault), or rather involved arrest by a barrier on the original fault and jumping [Harris and Day, JGR, 1993] to a neighboring fault on which rupture propagated bilaterally to form what appears as a backward-branched structure. Our studies [Poliakov et al., JGR in press, 2002; Kame et al, EOS, 2002] of stress fields around a dynamically moving mode II crack tip show a clear tendency to branch from the straight path at high rupture speeds, but the stress fields never allow the rupture path to directly turn through highly obtuse angles, and hence that mechanism is unlikely. In contrast, study of fault maps in the vicinity of the Kp to HV fault transition [Sowers et al., 1994], discussed as case (1) above, strongly suggest that the large-angle branching occurred as a jump, which we propose as the likely general mechanism. Implications for the Nakata et al. [1998] aim of inferring rupture directivity from branch geometry is that this will be possible only when rather detailed characterization (by surface geology, seismic relocation, trapped waves) of fault connectivity can be carried out in the vicinity of the branching junction, to ascertain whether direct turning of the rupture path through an angle, or jumping and then propagating bilaterally, were involved in prior events. They have opposite implications for how we would associate past directivity with a (nominally) branched fault geometry.

  4. Shallow faults mapped with seismic reflections: Lost River Fault, Idaho

    E-print Network

    Mubarik, Ali; Miller, Richard D.; Steeples, Don W.

    1991-09-01

    A high-resolution seismic-reflection survey, conducted at the intersection of Arentson Gulch road and the western splay of the Lost River fault scarp in central Idaho, defines a bedrock surface about 80 m deep which is segmented by several faults...

  5. DRAM Fault Analysis and Test DRAM Fault Analysis and Test

    E-print Network

    of the personal computer (PC). These memories are elaborately tested by their manufacturers to ensure a high-up stage where products are shipped to the customer, and ending with the test adaptation stage, basedDRAM Fault Analysis and Test Generation #12;#12;DRAM Fault Analysis and Test Generation

  6. 5 CFR 831.1402 - Fault.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ...2011-01-01 false Fault. 831.1402 Section... OFFICE OF PERSONNEL MANAGEMENT (CONTINUED) CIVIL...Overpayments § 831.1402 Fault. A recipient of...Office of Personnel Management may have been at fault in initiating an...

  7. 5 CFR 831.1402 - Fault.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ...2010-01-01 false Fault. 831.1402 Section... OFFICE OF PERSONNEL MANAGEMENT (CONTINUED) CIVIL...Overpayments § 831.1402 Fault. A recipient of...Office of Personnel Management may have been at fault in initiating an...

  8. Perspective View, Garlock Fault

    NASA Technical Reports Server (NTRS)

    2000-01-01

    California's Garlock Fault, marking the northwestern boundary of the Mojave Desert, lies at the foot of the mountains, running from the lower right to the top center of this image, which was created with data from NASA's shuttle Radar Topography Mission (SRTM), flown in February 2000. The data will be used by geologists studying fault dynamics and landforms resulting from active tectonics. These mountains are the southern end of the Sierra Nevada and the prominent canyon emerging at the lower right is Lone Tree canyon. In the distance, the San Gabriel Mountains cut across from the leftside of the image. At their base lies the San Andreas Fault which meets the Garlock Fault near the left edge at Tejon Pass. The dark linear feature running from lower right to upper left is State Highway 14 leading from the town of Mojave in the distance to Inyokern and the Owens Valley in the north. The lighter parallel lines are dirt roads related to power lines and the Los Angeles Aqueduct which run along the base of the mountains.

    This type of display adds the important dimension of elevation to the study of land use and environmental processes as observed in satellite images. The perspective view was created by draping a Landsat satellite image over an SRTM elevation model. Topography is exaggerated 1.5 times vertically. The Landsat image was provided by the United States Geological Survey's Earth Resources Observations Systems (EROS) Data Center, Sioux Falls, South Dakota.

    Elevation data used in this image was acquired by the Shuttle Radar Topography Mission (SRTM) aboard the Space Shuttle Endeavour, launched on February 11,2000. SRTM used the same radar instrument that comprised the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) that flew twice on the Space Shuttle Endeavour in 1994. SRTM was designed to collect three-dimensional measurements of the Earth's surface. To collect the 3-D data, engineers added a 60-meter-long (200-foot) mast, installed additional C-band and X-band antennas, and improved tracking and navigation devices. The mission is a cooperative project between the National Aeronautics and Space Administration (NASA), the National Imagery and Mapping Agency (NIMA) of the U.S. Department of Defense (DoD), and the German and Italian space agencies. It is managed by NASA's Jet Propulsion Laboratory, Pasadena, CA, for NASA's Earth Science Enterprise,Washington, DC.

    Size: Varies in a perspective view Location: 35.25 deg. North lat., 118.05 deg. West lon. Orientation: Looking southwest Original Data Resolution: SRTM and Landsat: 30 meters (99 feet) Date Acquired: February 16, 2000

  9. Fault current limiter

    SciTech Connect

    Darmann, Francis Anthony

    2013-10-08

    A fault current limiter (FCL) includes a series of high permeability posts for collectively define a core for the FCL. A DC coil, for the purposes of saturating a portion of the high permeability posts, surrounds the complete structure outside of an enclosure in the form of a vessel. The vessel contains a dielectric insulation medium. AC coils, for transporting AC current, are wound on insulating formers and electrically interconnected to each other in a manner such that the senses of the magnetic field produced by each AC coil in the corresponding high permeability core are opposing. There are insulation barriers between phases to improve dielectric withstand properties of the dielectric medium.

  10. Digital vibration control techniques

    NASA Technical Reports Server (NTRS)

    Chapman, P.; Kim, B. K.; Boctor, W.

    1974-01-01

    Analog vibration control techniques are reviewed and are compared with digital techniques. The advantages of the digital methods over the analog methods are demonstrated. The following topics are covered: (1) methods of computer-controlled random vibration and reverberation acoustic testing; (2) methods of computer-controlled sinewave vibration testing; and (3) methods of computer-controlled shock testing. Basic concepts are stressed rather than specific techniques or equipment. General algorithms are described in the form of block diagrams and flow diagrams. Specific problems and potential problems are discussed. The material is computer sciences oriented but is kept at a level that facilitates an understanding of the basic concepts of computer-controlled induced environmental test systems.

  11. Fiber optic vibration sensor

    DOEpatents

    Dooley, J.B.; Muhs, J.D.; Tobin, K.W.

    1995-01-10

    A fiber optic vibration sensor utilizes two single mode optical fibers supported by a housing with one optical fiber fixedly secured to the housing and providing a reference signal and the other optical fiber having a free span length subject to vibrational displacement thereof with respect to the housing and the first optical fiber for providing a signal indicative of a measurement of any perturbation of the sensor. Damping or tailoring of the sensor to be responsive to selected levels of perturbation is provided by altering the diameter of optical fibers or by immersing at least a portion of the free span length of the vibration sensing optical fiber into a liquid of a selected viscosity. 2 figures.

  12. Fiber optic vibration sensor

    DOEpatents

    Dooley, Joseph B. (Harriman, TN); Muhs, Jeffrey D. (Lenoir City, TN); Tobin, Kenneth W. (Harriman, TN)

    1995-01-01

    A fiber optic vibration sensor utilizes two single mode optical fibers supported by a housing with one optical fiber fixedly secured to the housing and providing a reference signal and the other optical fiber having a free span length subject to vibrational displacement thereof with respect to the housing and the first optical fiber for providing a signal indicative of a measurement of any perturbation of the sensor. Damping or tailoring of the sensor to be responsive to selected levels of perturbation is provided by altering the diameter of optical fibers or by immersing at least a portion of the free span length of the vibration sensing optical fiber into a liquid of a selected viscosity.

  13. Externally tuned vibration absorber

    DOEpatents

    Vincent, Ronald J. (Latham, NY)

    1987-09-22

    A vibration absorber unit or units are mounted on the exterior housing of a hydraulic drive system of the type that is powered from a pressure wave generated, e.g., by a Stirling engine. The hydraulic drive system employs a piston which is hydraulically driven to oscillate in a direction perpendicular to the axis of the hydraulic drive system. The vibration absorbers each include a spring or other resilient member having one side affixed to the housing and another side to which an absorber mass is affixed. In a preferred embodiment, a pair of vibration absorbers is employed, each absorber being formed of a pair of leaf spring assemblies, between which the absorber mass is suspended.

  14. Central Asia Active Fault Database

    NASA Astrophysics Data System (ADS)

    Mohadjer, Solmaz; Ehlers, Todd A.; Kakar, Najibullah

    2014-05-01

    The ongoing collision of the Indian subcontinent with Asia controls active tectonics and seismicity in Central Asia. This motion is accommodated by faults that have historically caused devastating earthquakes and continue to pose serious threats to the population at risk. Despite international and regional efforts to assess seismic hazards in Central Asia, little attention has been given to development of a comprehensive database for active faults in the region. To address this issue and to better understand the distribution and level of seismic hazard in Central Asia, we are developing a publically available database for active faults of Central Asia (including but not limited to Afghanistan, Tajikistan, Kyrgyzstan, northern Pakistan and western China) using ArcGIS. The database is designed to allow users to store, map and query important fault parameters such as fault location, displacement history, rate of movement, and other data relevant to seismic hazard studies including fault trench locations, geochronology constraints, and seismic studies. Data sources integrated into the database include previously published maps and scientific investigations as well as strain rate measurements and historic and recent seismicity. In addition, high resolution Quickbird, Spot, and Aster imagery are used for selected features to locate and measure offset of landforms associated with Quaternary faulting. These features are individually digitized and linked to attribute tables that provide a description for each feature. Preliminary observations include inconsistent and sometimes inaccurate information for faults documented in different studies. For example, the Darvaz-Karakul fault which roughly defines the western margin of the Pamir, has been mapped with differences in location of up to 12 kilometers. The sense of motion for this fault ranges from unknown to thrust and strike-slip in three different studies despite documented left-lateral displacements of Holocene and late Pleistocene landforms observed near the fault trace.

  15. Vibration by relativistic effects

    E-print Network

    Enrique Oradaz Romay

    2005-12-27

    Relativity, time reversal invariance in mechanics and principle of causality can be in the bases of a type of vibration of the extensive objects. It is because, the detailed analysis of the relativistic movement of an extensive body entail that all the objects must have inherent a vibratory movement to their own size. Such effect does not happen when it works with point particles thus is not stranger who happens unnoticed in the traditional studies. Also we can find relation between the form of vibration of the extensive objects and the energy that calculates by quantum considerations.

  16. Fault feature extraction of rolling bearing based on an improved cyclical spectrum density method

    NASA Astrophysics Data System (ADS)

    Li, Min; Yang, Jianhong; Wang, Xiaojing

    2015-07-01

    The traditional cyclical spectrum density(CSD) method is widely used to analyze the fault signals of rolling bearing. All modulation frequencies are demodulated in the cyclic frequency spectrum. Consequently, recognizing bearing fault type is difficult. Therefore, a new CSD method based on kurtosis(CSDK) is proposed. The kurtosis value of each cyclic frequency is used to measure the modulation capability of cyclic frequency. When the kurtosis value is large, the modulation capability is strong. Thus, the kurtosis value is regarded as the weight coefficient to accumulate all cyclic frequencies to extract fault features. Compared with the traditional method, CSDK can reduce the interference of harmonic frequency in fault frequency, which makes fault characteristics distinct from background noise. To validate the effectiveness of the method, experiments are performed on the simulation signal, the fault signal of the bearing outer race in the test bed, and the signal gathered from the bearing of the blast furnace belt cylinder. Experimental results show that the CSDK is better than the resonance demodulation method and the CSD in extracting fault features and recognizing degradation trends. The proposed method provides a new solution to fault diagnosis in bearings.

  17. Colorado Regional Faults

    SciTech Connect

    Hussein, Khalid

    2012-02-01

    Citation Information: Originator: Earth Science &Observation Center (ESOC), CIRES, University of Colorado at Boulder Originator: Colorado Geological Survey (CGS) Publication Date: 2012 Title: Regional Faults Edition: First Publication Information: Publication Place: Earth Science & Observation Center, Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder Publisher: Earth Science &Observation Center (ESOC), CIRES, University of Colorado at Boulder Description: This layer contains the regional faults of Colorado Spatial Domain: Extent: Top: 4543192.100000 m Left: 144385.020000 m Right: 754585.020000 m Bottom: 4094592.100000 m Contact Information: Contact Organization: Earth Science &Observation Center (ESOC), CIRES, University of Colorado at Boulder Contact Person: Khalid Hussein Address: CIRES, Ekeley Building Earth Science & Observation Center (ESOC) 216 UCB City: Boulder State: CO Postal Code: 80309-0216 Country: USA Contact Telephone: 303-492-6782 Spatial Reference Information: Coordinate System: Universal Transverse Mercator (UTM) WGS’1984 Zone 13N False Easting: 500000.00000000 False Northing: 0.00000000 Central Meridian: -105.00000000 Scale Factor: 0.99960000 Latitude of Origin: 0.00000000 Linear Unit: Meter Datum: World Geodetic System 1984 (WGS ’984) Prime Meridian: Greenwich Angular Unit: Degree Digital Form: Format Name: Shape file

  18. SFT: Scalable Fault Tolerance

    SciTech Connect

    Petrini, Fabrizio; Nieplocha, Jarek; Tipparaju, Vinod

    2006-04-15

    In this paper we will present a new technology that we are currently developing within the SFT: Scalable Fault Tolerance FastOS project which seeks to implement fault tolerance at the operating system level. Major design goals include dynamic reallocation of resources to allow continuing execution in the presence of hardware failures, very high scalability, high efficiency (low overhead), and transparency—requiring no changes to user applications. Our technology is based on a global coordination mechanism, that enforces transparent recovery lines in the system, and TICK, a lightweight, incremental checkpointing software architecture implemented as a Linux kernel module. TICK is completely user-transparent and does not require any changes to user code or system libraries; it is highly responsive: an interrupt, such as a timer interrupt, can trigger a checkpoint in as little as 2.5?s; and it supports incremental and full checkpoints with minimal overhead—less than 6% with full checkpointing to disk performed as frequently as once per minute.

  19. Optimized Fault Location Final Project Report

    E-print Network

    Optimized Fault Location Final Project Report Power Systems Engineering Research Center A National Engineering Research Center Optimized Fault Location Concurrent Technologies Corporation Final Project Report

  20. Impulsive radon emanation on a creeping segment of the San Andreas fault, California

    USGS Publications Warehouse

    King, C.-Y.

    1985-01-01

    Radon emanation was continuously monitored for several months at two locations along a creeping segment of the San Andreas fault in central California. The recorded emanations showed several impulsive increases that lasted as much as five hours with amplitudes considerably larger than meteorologically induced diurnal variations. Some of the radon increases were accompanied or followed by earthquakes or fault-creep events. They were possibly the result of some sudden outbursts of relatively radon-rich ground gas, sometimes triggered by crustal deformation or vibration. ?? 1985 Birkha??user Verlag.

  1. Effect of boundary vibration on the frictional behavior of a dense sheared granular layer

    E-print Network

    B. Ferdowsi; M. Griffa; R. A. Guyer; P. A. Johnson; J. Carmeliet

    2014-01-24

    We report results of 3D Discrete Element Method (DEM) simulations aiming at investigating the role of the boundary vibration in inducing frictional weakening in sheared granular layers. We study the role of different vibration amplitudes applied at various shear stress levels, for a granular layer in the stick-slip regime and in the steady-sliding regime. Results are reported in terms of friction drops and kinetic energy release associated with frictional weakening events. We find that larger vibration amplitude induces larger frictional weakening events. The results show evidence of a threshold below which no induced frictional weakening takes place. Friction drop size is found to be dependent on the shear stress at the time of vibration. A significant increase in the ratio between the number of slipping contacts to the number of sticking contacts in the granular layer is observed for large vibration amplitudes. These vibration-induced contact rearrangements enhance particle mobilization and induces a friction drop and kinetic energy release. This observation provides some insight into the grain-scale mechanisms of frictional weakening by boundary vibration in a dense sheared granular layer. In addition to characterizing the basic physics of vibration induced shear weakening, we are attempting to understand how a fault fails in the earth under seismic wave forcing. This is the well know phenomenon of dynamic earthquake triggering. We believe that the granular physics are key to this understanding.

  2. Award ER25750: Coordinated Infrastructure for Fault Tolerance Systems Indiana University Final Report

    SciTech Connect

    Lumsdaine, Andrew

    2013-03-08

    The main purpose of the Coordinated Infrastructure for Fault Tolerance in Systems initiative has been to conduct research with a goal of providing end-to-end fault tolerance on a systemwide basis for applications and other system software. While fault tolerance has been an integral part of most high-performance computing (HPC) system software developed over the past decade, it has been treated mostly as a collection of isolated stovepipes. Visibility and response to faults has typically been limited to the particular hardware and software subsystems in which they are initially observed. Little fault information is shared across subsystems, allowing little flexibility or control on a system-wide basis, making it practically impossible to provide cohesive end-to-end fault tolerance in support of scientific applications. As an example, consider faults such as communication link failures that can be seen by a network library but are not directly visible to the job scheduler, or consider faults related to node failures that can be detected by system monitoring software but are not inherently visible to the resource manager. If information about such faults could be shared by the network libraries or monitoring software, then other system software, such as a resource manager or job scheduler, could ensure that failed nodes or failed network links were excluded from further job allocations and that further diagnosis could be performed. As a founding member and one of the lead developers of the Open MPI project, our efforts over the course of this project have been focused on making Open MPI more robust to failures by supporting various fault tolerance techniques, and using fault information exchange and coordination between MPI and the HPC system software stack?from the application, numeric libraries, and programming language runtime to other common system components such as jobs schedulers, resource managers, and monitoring tools.

  3. Observer-based fault detection for nuclear reactors

    E-print Network

    Li, Qing, 1972-

    2001-01-01

    This is a study of fault detection for nuclear reactor systems. Basic concepts are derived from fundamental theories on system observers. Different types of fault- actuator fault, sensor fault, and system dynamics fault ...

  4. Vibration of Shells

    NASA Technical Reports Server (NTRS)

    Leissa, A. W.

    1973-01-01

    The vibrational characteristics and mechanical properties of shell structures are discussed. The subjects presented are: (1) fundamental equations of thin shell theory, (2) characteristics of thin circular cylindrical shells, (3) complicating effects in circular cylindrical shells, (4) noncircular cylindrical shell properties, (5) characteristics of spherical shells, and (6) solution of three-dimensional equations of motion for cylinders.

  5. Blade Vibration Measurement System

    NASA Technical Reports Server (NTRS)

    Platt, Michael J.

    2014-01-01

    The Phase I project successfully demonstrated that an advanced noncontacting stress measurement system (NSMS) could improve classification of blade vibration response in terms of mistuning and closely spaced modes. The Phase II work confirmed the microwave sensor design process, modified the sensor so it is compatible as an upgrade to existing NSMS, and improved and finalized the NSMS software. The result will be stand-alone radar/tip timing radar signal conditioning for current conventional NSMS users (as an upgrade) and new users. The hybrid system will use frequency data and relative mode vibration levels from the radar sensor to provide substantially superior capabilities over current blade-vibration measurement technology. This frequency data, coupled with a reduced number of tip timing probes, will result in a system capable of detecting complex blade vibrations that would confound traditional NSMS systems. The hardware and software package was validated on a compressor rig at Mechanical Solutions, Inc. (MSI). Finally, the hybrid radar/tip timing NSMS software package and associated sensor hardware will be installed for use in the NASA Glenn spin pit test facility.

  6. Compact Vibration Damper

    NASA Technical Reports Server (NTRS)

    Ivanco, Thomas G. (Inventor)

    2014-01-01

    A vibration damper includes a rigid base with a mass coupled thereto for linear movement thereon. Springs coupled to the mass compress in response to the linear movement along either of two opposing directions. A converter coupled to the mass converts the linear movement to a corresponding rotational movement. A rotary damper coupled to the converter damps the rotational movement.

  7. Polyatomic molecule vibrations

    NASA Technical Reports Server (NTRS)

    1976-01-01

    Polyatomic molecule vibrations are analyzed as harmonic vibrations along normal coordinates. The energy eigenvalues are found for linear and nonlinear symmetric triatomic molecules for valence bond models of the potential function with arbitrary coupling coefficients; such models can usually be fitted to observed energy levels with reasonably good accuracy. Approximate normal coordinates for the H2O molecule are discussed. Degenerate vibrational modes such as occur in CO2 are analyzed and expressions for Fermi resonance between close-lying states of the same symmetry are developed. The bending modes of linear triatomic molecules are expressed in terms of Laguerre polynomials in cylindrical coordinates as well as in terms of Hermite polynomials in Cartesian coordinates. The effects of large-amplitude bending such as occur in the C3 molecule are analyzed, along with anharmonic effects, which split the usually degenerate bending mode energy levels. Finally, the vibrational frequencies, degeneracies, and symmetry properties of XY3, X2Y2, and XY4 type molecules are discussed.

  8. Nonlinear vibrational microscopy

    DOEpatents

    Holtom, Gary R. (Richland, WA); Xie, Xiaoliang Sunney (Richland, WA); Zumbusch, Andreas (Munchen, DE)

    2000-01-01

    The present invention is a method and apparatus for microscopic vibrational imaging using coherent Anti-Stokes Raman Scattering or Sum Frequency Generation. Microscopic imaging with a vibrational spectroscopic contrast is achieved by generating signals in a nonlinear optical process and spatially resolved detection of the signals. The spatial resolution is attained by minimizing the spot size of the optical interrogation beams on the sample. Minimizing the spot size relies upon a. directing at least two substantially co-axial laser beams (interrogation beams) through a microscope objective providing a focal spot on the sample; b. collecting a signal beam together with a residual beam from the at least two co-axial laser beams after passing through the sample; c. removing the residual beam; and d. detecting the signal beam thereby creating said pixel. The method has significantly higher spatial resolution then IR microscopy and higher sensitivity than spontaneous Raman microscopy with much lower average excitation powers. CARS and SFG microscopy does not rely on the presence of fluorophores, but retains the resolution and three-dimensional sectioning capability of confocal and two-photon fluorescence microscopy. Complementary to these techniques, CARS and SFG microscopy provides a contrast mechanism based on vibrational spectroscopy. This vibrational contrast mechanism, combined with an unprecedented high sensitivity at a tolerable laser power level, provides a new approach for microscopic investigations of chemical and biological samples.

  9. Vibrational Spectroscopy and Astrobiology

    NASA Technical Reports Server (NTRS)

    Chaban, Galina M.; Kwak, D. (Technical Monitor)

    2001-01-01

    Role of vibrational spectroscopy in solving problems related to astrobiology will be discussed. Vibrational (infrared) spectroscopy is a very sensitive tool for identifying molecules. Theoretical approach used in this work is based on direct computation of anharmonic vibrational frequencies and intensities from electronic structure codes. One of the applications of this computational technique is possible identification of biological building blocks (amino acids, small peptides, DNA bases) in the interstellar medium (ISM). Identifying small biological molecules in the ISM is very important from the point of view of origin of life. Hybrid (quantum mechanics/molecular mechanics) theoretical techniques will be discussed that may allow to obtain accurate vibrational spectra of biomolecular building blocks and to create a database of spectroscopic signatures that can assist observations of these molecules in space. Another application of the direct computational spectroscopy technique is to help to design and analyze experimental observations of ice surfaces of one of the Jupiter's moons, Europa, that possibly contains hydrated salts. The presence of hydrated salts on the surface can be an indication of a subsurface ocean and the possible existence of life forms inhabiting such an ocean.

  10. Vibrational Conical Intersections: Implications for Ultrafast Vibrational Dynamics

    NASA Astrophysics Data System (ADS)

    Dawadi, Mahesh; Prasad Thapaliya, Bishnu; Bhatta, Ram; Perry, David

    2015-03-01

    The presence of conical intersections (CIs) between electronic potential energy surfaces is known to play a key role in ultrafast electronic relaxation in diverse circumstances. Recent reports have documented the existence of vibrational CIs connecting vibrationally adiabatic surfaces. Just as electronic CIs are now appreciated to be ubiquitous, controlling the rates of many photochemical processes, the present work on methanol and methyl mercaptan suggests that vibrational CIs may also be widespread, possibly controlling the outcome of some high-energy processes where vibrationally excited species are present. Other examples of vibrational CIs include the vibrational Jahn-Teller effect in C3V organic molecules and transition metal complexes. While the present work addresses only the couplings within bound molecules, the concept of vibrational CIs providing pathways for ultrafast relaxation also applies to molecular collisions. This work is supported by DOE (DEFG02-90ER14151).

  11. Comparison of Cenozoic Faulting at the Savannah River Site to Fault Characteristics of the Atlantic Coast Fault Province: Implications for Fault Capability

    SciTech Connect

    Cumbest, R.J.

    2000-11-14

    This study compares the faulting observed on the Savannah River Site and vicinity with the faults of the Atlantic Coastal Fault Province and concludes that both sets of faults exhibit the same general characteristics and are closely associated. Based on the strength of this association it is concluded that the faults observed on the Savannah River Site and vicinity are in fact part of the Atlantic Coastal Fault Province. Inclusion in this group means that the historical precedent established by decades of previous studies on the seismic hazard potential for the Atlantic Coastal Fault Province is relevant to faulting at the Savannah River Site. That is, since these faults are genetically related the conclusion of ''not capable'' reached in past evaluations applies.In addition, this study establishes a set of criteria by which individual faults may be evaluated in order to assess their inclusion in the Atlantic Coast Fault Province and the related association of the ''not capable'' conclusion.

  12. Fault-Tolerant Quantum Computation for Local Leakage Faults

    E-print Network

    Panos Aliferis; Barbara M. Terhal

    2006-05-26

    We provide a rigorous analysis of fault-tolerant quantum computation in the presence of local leakage faults. We show that one can systematically deal with leakage by using appropriate leakage-reduction units such as quantum teleportation. The leakage noise is described by a Hamiltonian and the noise is treated coherently, similar to general non-Markovian noise analyzed in Refs. quant-ph/0402104 and quant-ph/0504218. We describe ways to limit the use of leakage-reduction units while keeping the quantum circuits fault-tolerant and we also discuss how leakage reduction by teleportation is naturally achieved in measurement-based computation.

  13. The effects of vibration-reducing gloves on finger vibration

    PubMed Central

    Welcome, Daniel E.; Dong, Ren G.; Xu, Xueyan S.; Warren, Christopher; McDowell, Thomas W.

    2015-01-01

    Vibration-reducing (VR) gloves have been used to reduce the hand-transmitted vibration exposures from machines and powered hand tools but their effectiveness remains unclear, especially for finger protection. The objectives of this study are to determine whether VR gloves can attenuate the vibration transmitted to the fingers and to enhance the understanding of the mechanisms of how these gloves work. Seven adult male subjects participated in the experiment. The fixed factors evaluated include hand force (four levels), glove condition (gel-filled, air bladder, no gloves), and location of the finger vibration measurement. A 3-D laser vibrometer was used to measure the vibrations on the fingers with and without wearing a glove on a 3-D hand-arm vibration test system. This study finds that the effect of VR gloves on the finger vibration depends on not only the gloves but also their influence on the distribution of the finger contact stiffness and the grip effort. As a result, the gloves increase the vibration in the fingertip area but marginally reduce the vibration in the proximal area at some frequencies below 100 Hz. On average, the gloves reduce the vibration of the entire fingers by less than 3% at frequencies below 80 Hz but increase at frequencies from 80 to 400 Hz. At higher frequencies, the gel-filled glove is more effective at reducing the finger vibration than the air bladder-filled glove. The implications of these findings are discussed. Relevance to industry Prolonged, intensive exposure to hand-transmitted vibration can cause hand-arm vibration syndrome. Vibration-reducing gloves have been used as an alternative approach to reduce the vibration exposure. However, their effectiveness for reducing finger-transmitted vibrations remains unclear. This study enhanced the understanding of the glove effects on finger vibration and provided useful information on the effectiveness of typical VR gloves at reducing the vibration transmitted to the fingers. The new results and knowledge can be used to help select appropriate gloves for the operations of powered hand tools, to help perform risk assessment of the vibration exposure, and to help design better VR gloves. PMID:26543297

  14. Vibration sensing method and apparatus

    DOEpatents

    Barna, Basil A. (Idaho Falls, ID)

    1989-04-25

    A method and apparatus for nondestructive evaluation of a structure is disclosed. Resonant audio frequency vibrations are excited in the structure to be evaluated and the vibrations are measured and characterized to obtain information about the structure. The vibrations are measured and characterized by reflecting a laser beam from the vibrating structure and directing a substantial portion of the reflected beam back into the laser device used to produce the beam which device is capable of producing an electric signal containing information about the vibration.

  15. Vibration sensing method and apparatus

    DOEpatents

    Barna, B.A.

    1989-04-25

    A method and apparatus for nondestructive evaluation of a structure are disclosed. Resonant audio frequency vibrations are excited in the structure to be evaluated and the vibrations are measured and characterized to obtain information about the structure. The vibrations are measured and characterized by reflecting a laser beam from the vibrating structure and directing a substantial portion of the reflected beam back into the laser device used to produce the beam which device is capable of producing an electric signal containing information about the vibration. 4 figs.

  16. Vibration sensing method and apparatus

    DOEpatents

    Barna, B.A.

    1987-07-07

    A method and apparatus for nondestructive evaluation of a structure is disclosed. Resonant audio frequency vibrations are excited in the structure to be evaluated and the vibrations are measured and characterized to obtain information about the structure. The vibrations are measured and characterized by reflecting a laser beam from the vibrating structure and directing a substantial portion of the reflected beam back into the laser device used to produce the beam which device is capable of producing an electric signal containing information about the vibration. 4 figs.

  17. Arc fault detection system

    DOEpatents

    Jha, K.N.

    1999-05-18

    An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard. 1 fig.

  18. Arc fault detection system

    DOEpatents

    Jha, Kamal N. (Bethel Park, PA)

    1999-01-01

    An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard.

  19. Faulted Sedimentary Rocks

    NASA Technical Reports Server (NTRS)

    2004-01-01

    27 June 2004 This Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC) image shows some of the layered, sedimentary rock outcrops that occur in a crater located at 8oN, 7oW, in western Arabia Terra. Dark layers and dark sand have enhanced the contrast of this scene. In the upper half of the image, one can see numerous lines that off-set the layers. These lines are faults along which the rocks have broken and moved. The regularity of layer thickness and erosional expression are taken as evidence that the crater in which these rocks occur might once have been a lake. The image covers an area about 1.9 km (1.2 mi) wide. Sunlight illuminates the scene from the lower left.

  20. Fault Tolerant State Machines

    NASA Technical Reports Server (NTRS)

    Burke, Gary R.; Taft, Stephanie

    2004-01-01

    State machines are commonly used to control sequential logic in FPGAs and ASKS. An errant state machine can cause considerable damage to the device it is controlling. For example in space applications, the FPGA might be controlling Pyros, which when fired at the wrong time will cause a mission failure. Even a well designed state machine can be subject to random errors us a result of SEUs from the radiation environment in space. There are various ways to encode the states of a state machine, and the type of encoding makes a large difference in the susceptibility of the state machine to radiation. In this paper we compare 4 methods of state machine encoding and find which method gives the best fault tolerance, as well as determining the resources needed for each method.