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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; Juričić, Đani

    2016-01-01

    Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

  2. Bearing fault diagnosis based on spectrum images of vibration signals

    NASA Astrophysics Data System (ADS)

    Li, Wei; Qiu, Mingquan; Zhu, Zhencai; Wu, Bo; Zhou, Gongbo

    2016-03-01

    Bearing fault diagnosis has been a challenge in the monitoring activities of rotating machinery, and it’s receiving more and more attention. The conventional fault diagnosis methods usually extract features from the waveforms or spectrums of vibration signals in order to correctly classify faults. In this paper, a novel feature in the form of images is presented, namely analysis of the spectrum images of vibration signals. The spectrum images are simply obtained by doing fast Fourier transformation. Such images are processed with two-dimensional principal component analysis (2DPCA) to reduce the dimensions, and then a minimum distance method is applied to classify the faults of bearings. The effectiveness of the proposed method is verified with experimental data.

  3. 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.

  4. 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.

  5. Fault Diagnosis of Diesel Engine Using Vibration Signals

    NASA Astrophysics Data System (ADS)

    Wang, Fengli; Duan, Shulin

    Aiming at the characteristics of the surface vibration signals measured from the diesel engine, a novel method combining local wave decomposition (LWD) and lifting wavelet denoising is proposed, and is used for feature extraction and condition evaluation of diesel engine vibration signals. Firstly, the original data is preprocessed using the lifting wavelet transformation to suppress abnormal interference of noise, and avoid the pseudo mode functions from LWD. Obtaining intrinsic mode functions(IMFs) by using LWD, the instantaneous frequency and amplitude can be calculated by Hilbert transform. Hilbert marginal spectrum can exactly provide the energy distribution of the signal with the change of instantaneous frequency. The vibration signals of diesel engine piston-liner wear are analyzed and the results show that the method is feasible and effective in feature extraction and condition evaluation of diesel engine faults.

  6. Unsupervised Pattern Classifier for Abnormality-Scaling of Vibration Features for Helicopter Gearbox Fault Diagnosis

    NASA Technical Reports Server (NTRS)

    Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

    1996-01-01

    A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.

  7. 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

  8. 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].

  9. Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis

    NASA Astrophysics Data System (ADS)

    Cong, Feiyun; Chen, Jin; Dong, Guangming; Pecht, Michael

    2013-04-01

    Rolling element bearing faults are among the main causes of breakdown in rotating machines. In this paper, a rolling bearing fault model is proposed based on the dynamic load analysis of a rotor-bearing system. The rotor impact factor is taken into consideration in the rolling bearing fault signal model. The defect load on the surface of the bearing is divided into two parts, the alternate load and the determinate load. The vibration response of the proposed fault signal model is investigated and the fault signal calculating equation is derived through dynamic and kinematic analysis. Outer race and inner race fault simulations are realized in the paper. The simulation process includes consideration of several parameters, such as the gravity of the rotor-bearing system, the imbalance of the rotor, and the location of the defect on the surface. The simulation results show that different amplitude contributions of the alternate load and determinate load will cause different envelope spectrum expressions. The rotating frequency sidebands will occur in the envelope spectrum in addition to the fault characteristic frequency. This appearance of sidebands will increase the difficulty of fault recognition in intelligent fault diagnosis. The experiments given in the paper have successfully verified the proposed signal model simulation results. The test rig design of the rotor bearing system simulated several operating conditions: (1) rotor bearing only; (2) rotor bearing with loader added; (3) rotor bearing with loader and rotor disk; and (4) bearing fault simulation without rotor influence. The results of the experiments have verified that the proposed rolling bearing signal model is important to the rolling bearing fault diagnosis of rotor-bearing systems.

  10. 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.

  11. Theoretical and experimental analysis of bispectrum of vibration signals for fault diagnosis of gears

    NASA Astrophysics Data System (ADS)

    Guoji, Shen; McLaughlin, Stephen; Yongcheng, Xu; White, Paul

    2014-02-01

    Condition monitoring and fault diagnosis is an important issue for gearbox maintenance and safety. The critical process involved in such activities is to extract reliable features representative of the condition of the gears or gearbox. In this paper a framework is presented for the application of bispectrum to the analysis of gearbox vibration. The bispectrum of a composite signal consisting of multiple periodic components has peaks at the bifrequencies that correspond to closely related components which can be produced by any nonlinearity. As a result, biphase verification is necessary to decrease false-alarming for any bispectrum-based method. A model based on modulated signals is adopted to reveal the bispectrum characteristics for the vibration of a faulty gear, and the corresponding amplitude and phase of the bispectrum expression are deduced. Therefore, a diagnostic approach based on the theoretical result is derived and verified by the analysis of a set of vibration signals from a helicopter gearbox.

  12. 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

  13. Vibration sensor-based bearing fault diagnosis using ellipsoid-ARTMAP and differential evolution algorithms.

    PubMed

    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

  14. 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.

  15. 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

  16. 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

  17. 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

  18. Multi-component machine monitoring and fault diagnosis using blind source separation and advanced vibration analysis

    NASA Astrophysics Data System (ADS)

    Mahvash Mohammadi, Ali

    In this dissertation, two approaches are studied for the case of bearing anomaly detection. One approach is to regard it as a blind source separation (cocktail party) problem and take advantage of statistical and mathematical methods developed for this purpose, primarily independent component analysis (ICA), to separate signals coming from different sources. The other approach is to avoid making the effort to 'separate' the signals and relate them to different components (sources) and instead make use of the specification and characteristics of vibration signals produced by the different components in normal and faulty conditions. In the first approach, a common difficulty with applying blind source separation techniques (or, in general any mathematical methods) to separation of vibration sources is that no standard measure exists to assess the quality of separation and validate the results. In fact, for an ideal assessment the true original signals produced by each component must be available as a prerequisite. This requires gathering signals from each component in strict isolation during operation in a lab environment which, if not impossible, is very costly and difficult. To alleviate this difficulty, a novel method is developed that presents the distribution of vibration energy with regard to the respective locations of vibration sources and sensors, and takes into consideration the mechanical attributes of the structure. This method uses some key concepts from statistical energy analysis (SEA) to support the fact that each sensor collects a different version of the oscillations produced in the system with respect to its location in the system. Therefore, by comparing the spectral signature of the vibration signals and making use of a priori knowledge of the spatial distribution of sensors and components, a schematic representation of the spectral signature of the vibration sources are obtained. This method is verified using a series of experiments with synthetic and real data. If a standard evaluation metric is available, more rigorous evaluation of blind source separation techniques can be achieved. The foremost existing solution to blind source separation is Independent Component Analysis (ICA). In ICA it is assumed that the source signals are statistically independent from one another and can therefore be recovered by formulating the independence. There are, however, two dominant ambiguities and indeterminacies associated with ICA results. One ambiguity is that the original index or permutation of the recovered source signals is unknown. The other ambiguity is that the actual scale of the source signals cannot be determined. ICA can be applied in both time and frequency domains. In this dissertation, a new technique is proposed based mainly on the mechanical attributes of the system rather than unrealistic mathematical or statistical assumptions. This technique is developed based on the presumption that the mixing mechanism for neighboring frequency bins varies only slightly from one bin to another. Therefore, by numerically tying and relating the mixing matrices of contiguous frequency bins, local permutation and scale indeterminacy problems are resolved. This method is studied experimentally using laboratory data and the results are also compared with the evaluation metric presented in the previous study. Accordance between the results confirmed the efficacy of the proposed method. In the second approach, the effectiveness of cyclic spectral analysis is assessed for detecting bearing faults in complex machinery. Bearing faults are known to produce vibration with recurring impulsiveness in the energy which is referred to as cyclostationarity. Cyclic spectral analysis is a powerful tool to measure the cyclostationarity of a signal in different frequency ranges. For this tool to be effective in applications related to complex machinery, two requirements are identified. One requirement is that the tool must be capable of detecting defects from a weak signal as it passes and attenuates through its transmission path. The other requirement is that it must allow robust, attainable and consistent trending. Also the feature being tracked must be consistent in the sense that its value bears some correspondence to the severity of the faults. In this thesis, cyclostationarity is examined for these requirements through two sets of experimental tests. The experimental results show that cyclic spectral analysis is indeed capable of detecting bearing faults from faint signals. Also, it can be utilized as a reliable monitoring tool, even though the correspondence between the feature value and the severity of the bearing faults may not be robustly established. (Abstract shortened by UMI.)

  19. Isolability of faults in sensor fault diagnosis

    NASA Astrophysics Data System (ADS)

    Sharifi, Reza; Langari, Reza

    2011-10-01

    A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor fault diagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor fault diagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach.

  20. Fault diagnosis of analog circuits

    SciTech Connect

    Bandler, J.W.; Salama, A.E.

    1985-08-01

    In this paper, various fault location techniques in analog networks are described and compared. The emphasis is on the more recent developments in the subject. Four main approaches for fault location are addressed, examined, and illustrated using simple network examples. In particular, we consider the fault dictionary approach, the parameter identification approach, the fault verification approach, and the approximation approach. Theory and algorithms that are associated with these approaches are reviewed and problems of their practical application are identified. Associated with the fault dictionary approach we consider fault dictionary construction techniques, methods of optimum measurement selection, different fault isolation criteria, and efficient fault simulation techniques. Parameter identification techniques that either utilize linear or nonlinear systems of equations to identify all network elements are examined very thoroughly. Under fault verification techniques we discuss node-fault diagnosis, branch-fault diagnosis, subnetwork testability conditions as well as combinatorial techniques, the failure bound technique, and the network decomposition technique. For the approximation approach we consider probabilistic methods and optimization-based methods. The artificial intelligence technique and the different measures of testability are also considered. The main features of the techniques considered are summarized in a comparative table. An extensive, but not exhaustive, bibliography is provided.

  1. 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.

  2. Fault diagnosis of power systems

    SciTech Connect

    Sekine, Y. ); Akimoto, Y. ); Kunugi, M. )

    1992-05-01

    Fault diagnosis of power systems plays a crucial role in power system monitoring and control that ensures stable supply of electrical power to consumers. In the case of multiple faults or incorrect operation of protective devices, fault diagnosis requires judgment of complex conditions at various levels. For this reason, research into application of knowledge-based systems go an early start and reports of such systems have appeared in may papers. In this paper, these systems are classified by the method of inference utilized in the knowledge-based systems for fault diagnosis of power systems. The characteristics of each class and corresponding issues as well as the state-of-the-art techniques for improving their performance are presented. Additional topics covered are user interfaces, interfaces with energy management systems (EMS's), and expert system development tools for fault diagnosis. Results and evaluation of actual operation in the field are also discussed. Knowledge-based fault diagnosis of power systems will continue to disseminate.

  3. 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.

  4. A new intelligent hierarchical fault diagnosis system

    SciTech Connect

    Huang, Y.C.; Huang, C.L.; Yang, H.T.

    1997-02-01

    As a part of a substation-level decision support system, a new intelligent Hierarchical Fault Diagnosis System for on-line fault diagnosis is presented in this paper. The proposed diagnosis system divides the fault diagnosis process into two phases. Using time-stamped information of relays and breakers, phase 1 identifies the possible fault sections through the Group Method of Data Handling (GMDH) networks, and phase 2 recognizes the types and detailed situations of the faults identified in phase 1 by using a fast bit-operation logical inference mechanism. The diagnosis system has been practically verified by testing on a typical Taiwan power secondary transmission system. Test results show that rapid and accurate diagnosis can be obtained with flexibility and portability for fault diagnosis purpose of diverse substations.

  5. Layered clustering multi-fault diagnosis for hydraulic piston pump

    NASA Astrophysics Data System (ADS)

    Du, Jun; Wang, Shaoping; Zhang, Haiyan

    2013-04-01

    Efficient diagnosis is very important for improving reliability and performance of aircraft hydraulic piston pump, and it is one of the key technologies in prognostic and health management system. In practice, due to harsh working environment and heavy working loads, multiple faults of an aircraft hydraulic pump may occur simultaneously after long time operations. However, most existing diagnosis methods can only distinguish pump faults that occur individually. Therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic pump. In this paper, a new method based on the layered clustering algorithm is proposed to diagnose multiple faults of an aircraft hydraulic pump that occur simultaneously. The intensive failure mechanism analyses of the five main types of faults are carried out, and based on these analyses the optimal combination and layout of diagnostic sensors is attained. The three layered diagnosis reasoning engine is designed according to the faults' risk priority number and the characteristics of different fault feature extraction methods. The most serious failures are first distinguished with the individual signal processing. To the desultory faults, i.e., swash plate eccentricity and incremental clearance increases between piston and slipper, the clustering diagnosis algorithm based on the statistical average relative power difference (ARPD) is proposed. By effectively enhancing the fault features of these two faults, the ARPDs calculated from vibration signals are employed to complete the hypothesis testing. The ARPDs of the different faults follow different probability distributions. Compared with the classical fast Fourier transform-based spectrum diagnosis method, the experimental results demonstrate that the proposed algorithm can diagnose the multiple faults, which occur synchronously, with higher precision and reliability.

  6. 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

  7. 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

  8. Roller element bearing fault diagnosis using singular spectrum analysis

    NASA Astrophysics Data System (ADS)

    Muruganatham, Bubathi; Sanjith, M. A.; Krishnakumar, B.; Satya Murty, S. A. V.

    2013-02-01

    Most of the existing time series methods of feature extraction involve complex algorithm and the extracted features are affected by sample size and noise. In this paper, a simple time series method for bearing fault feature extraction using singular spectrum analysis (SSA) of the vibration signal is proposed. The method is easy to implement and fault feature is noise immune. SSA is used for the decomposition of the acquired signals into an additive set of principal components. A new approach for the selection of the principal components is also presented. Two methods of feature extraction based on SSA are implemented. In first method, the singular values (SV) of the selected SV number are adopted as the fault features, and in second method, the energy of the principal components corresponding to the selected SV numbers are used as features. An artificial neural network (ANN) is used for fault diagnosis. The algorithms were evaluated using two experimental datasets—one from a motor bearing subjected to different fault severity levels at various loads, with and without noise, and the other with bearing vibration data obtained in the presence of a gearbox. The effect of sample size, fault size and load on the fault feature is studied. The advantages of the proposed method over the exiting time series method are discussed. The experimental results demonstrate that the proposed bearing fault diagnosis method is simple, noise tolerant and efficient.

  9. 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.

  10. 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.

  11. Training for Skill in Fault Diagnosis

    ERIC Educational Resources Information Center

    Turner, J. D.

    1974-01-01

    The Knitting, Lace and Net Industry Training Board has developed a training innovation called fault diagnosis training. The entire training process concentrates on teaching based on the experiences of troubleshooters or any other employees whose main tasks involve fault diagnosis and rectification. (Author/DS)

  12. Automatic fault diagnosis of a switching regulator

    NASA Astrophysics Data System (ADS)

    Nienhaus, H. A.; Palmer, D. E.

    This paper describes a microprocessor-based system for the automatic fault diagnosis of a switching regulator. It covers the system from a test philosophy to a working breadboard that correctly identifies single simulated faults in the switching regulator. In addition to open circuit, short circuit, and stuck at faults, the system is capable of diagnosing faults due to excessive leakage, drift in critical components, and system instability.

  13. 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.

  14. 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.

  15. Measurement selection for parametric IC fault diagnosis

    NASA Technical Reports Server (NTRS)

    Wu, A.; Meador, J.

    1991-01-01

    Experimental results obtained with the use of measurement reduction for statistical IC fault diagnosis are described. The reduction method used involves data pre-processing in a fashion consistent with a specific definition of parametric faults. The effects of this preprocessing are examined.

  16. On-line diagnosis of unrestricted faults

    NASA Technical Reports Server (NTRS)

    Meyer, J. F.; Sundstrom, R. J.

    1974-01-01

    A formal model for the study of on-line diagnosis is introduced and used to investigate the diagnosis of unrestricted faults. A fault of a system S is considered to be a transformation of S into another system S' at some time tau. The resulting faulty system is taken to be the system which looks like S up to time tau, and like S' thereafter. Notions of fault tolerance error are defined in terms of the resulting system being able to mimic some desired behavior as specified by a system similar to S. A notion of on-line diagnosis is formulated which involves an external detector and a maximum time delay within which every error caused by a fault in a prescribed set must be detected. It is shown that if a system is on-line diagnosable for the unrestricted set of faults then the detector is at least as complex, in terms of state set size, as the specification. The use of inverse systems for the diagnosis of unrestricted faults is considered. A partial characterization of those inverses which can be used for unrestricted fault diagnosis is obtained.

  17. 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.

  18. Autoregressive modelling for rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Al-Bugharbee, H.; Trendafilova, I.

    2015-07-01

    In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition.

  19. Efficient fault diagnosis of helicopter gearboxes

    NASA Technical Reports Server (NTRS)

    Chin, H.; Danai, K.; Lewicki, D. G.

    1993-01-01

    Application of a diagnostic system to a helicopter gearbox is presented. The diagnostic system is a nonparametric pattern classifier that uses a multi-valued influence matrix (MVIM) as its diagnostic model and benefits from a fast learning algorithm that enables it to estimate its diagnostic model from a small number of measurement-fault data. To test this diagnostic system, vibration measurements were collected from a helicopter gearbox test stand during accelerated fatigue tests and at various fault instances. The diagnostic results indicate that the MVIM system can accurately detect and diagnose various gearbox faults so long as they are included in training.

  20. Cooperative human-machine fault diagnosis

    NASA Technical Reports Server (NTRS)

    Remington, Roger; Palmer, Everett

    1987-01-01

    Current expert system technology does not permit complete automatic fault diagnosis; significant levels of human intervention are still required. This requirement dictates a need for a division of labor that recognizes the strengths and weaknesses of both human and machine diagnostic skills. Relevant findings from the literature on human cognition are combined with the results of reviews of aircrew performance with highly automated systems to suggest how the interface of a fault diagnostic expert system can be designed to assist human operators in verifying machine diagnoses and guiding interactive fault diagnosis. It is argued that the needs of the human operator should play an important role in the design of the knowledge base.

  1. On-line diagnosis of unrestricted faults

    NASA Technical Reports Server (NTRS)

    Meyer, J. F.; Sundstrom, R. J.

    1975-01-01

    Attention is given to the formal development of the notion of a discrete-time system and the associated concepts of fault, result of a fault, and error. The considered concept of on-line diagnosis is formalized and a diagnosis using inverse machines is discussed. The case of an inverse which is lossless is investigated. It is found that in such a case the class of unrestricted faults can be diagnosed with a delay equal to the delay of losslessness of the inverse system.

  2. Adaptive fault diagnosis in rotating machines using indicators selection

    NASA Astrophysics Data System (ADS)

    Khelf, Ilyes; Laouar, Lakhdar; Bouchelaghem, Abdelaziz M.; Rémond, Didier; Saad, Salah

    2013-11-01

    Over the past two decades, condition monitoring and faults diagnosis in rotating machinery have been widely studied and reported. In the present paper an algorithm for fault diagnosis in industrial rotating machines facing new operating conditions emergence is developed on the basis of input indicators, extracted from vibrations spectrums. Indicators selection is used to improve diagnosis performances by the help of a hybrid approach using several selection criteria and different classifiers. To validate the performances of this algorithm, experimental tests were conducted on two industrial systems with various operating conditions. The results have proved the effectiveness of the developed algorithm compared to the "J48 decision tree" and also reveal the need to re-select the indicators for reliable monitoring of working conditions.

  3. System-level fault diagnosis and reconfiguration

    SciTech Connect

    Gupta, R.

    1987-01-01

    The classical fault-diagnosis model assumes that faults are permanent and each test, administered by a unit, is complete for the unit being tested. These two assumptions may restrict the applicability of the model. The author introduces a new deterministic fault model for system-level fault diagnosis. Unlike earlier attempts, his model intermittent faults, incomplete testing by units, and fault masking in a uniform manner. He obtains necessary and sufficient conditions for a system to be diagnosable using the new fault model. The complexity of the diagnosability problem in the model is shown to be co-NP-complete. He then examines the problem of system reconfiguration following identification of faulty components. In particular, reconfigurability of multipipelines is considered in detail. He alternates the pipeline stages with testing and reconfiguring circuitry. The pipelines are reconfigured by programming the switches in a distributed manner. The switch programming algorithm is optimal in the sense that it recovers the maximum number of pipelines under any fault pattern. A proof of its optimality is also presented.

  4. 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.

  5. Diagnosis of fault gearbox with wavelet packet decomposition and vector statistics method

    NASA Astrophysics Data System (ADS)

    Ren, Xueping; Shao, Wei; Ma, Wensheng

    2008-12-01

    Vibration signals from fault gearbox are usually complex with many different frequencies. As a result, it is difficult to find early symptoms of a potential fault in a gearbox. WPD (Wavelet Packet Decomposition) have been established as the most wide spread tool to disclose transient information in signals and wavelet packet filter is found to be very effective in detection of symptoms from vibration signals of a gearbox with early fatigue tooth crack. The paper presents a method to decompose the fault vibration signals with WPD and analysis the decomposed vectors with statistic algorithm to diagnosis the gearbox fault. The method is considered to be effective with the aim of gearbox fault detection and diagnosis.

  6. Completing fault models for abductive diagnosis

    SciTech Connect

    Knill, E.; Cox, P.T.; Pietrzykowski, T.

    1992-11-05

    In logic-based diagnosis, the consistency-based method is used to determine the possible sets of faulty devices. If the fault models of the devices are incomplete or nondeterministic, then this method does not necessarily yield abductive explanations of system behavior. Such explanations give additional information about faulty behavior and can be used for prediction. Unfortunately, system descriptions for the consistency-based method are often not suitable for abductive diagnosis. Methods for completing the fault models for abductive diagnosis have been suggested informally by Poole and by Cox et al. Here we formalize these methods by introducing a standard form for system descriptions. The properties of these methods are determined in relation to consistency-based diagnosis and compared to other ideas for integrating consistency-based and abductive diagnosis.

  7. Fault diagnosis in sparse multiprocessor systems

    NASA Technical Reports Server (NTRS)

    Blough, Douglas M.; Sullivan, Gregory F.; Masson, Gerald M.

    1988-01-01

    The problem of fault diagnosis in multiprocessor systems is considered under a uniformly probabilistic model in which processors are faulty with probability p. This work focuses on minimizing the number of tests that must be conducted in order to correctly diagnose the state of every processor in the system with high probability. A diagnosis algorithm that can correctly diagnose the state of every processor with probability approaching one in a class of systems performing slightly greater than a linear number of tests is presented. A nearly matching lower bound on the number of tests required to achieve correct diagnosis in arbitrary systems is also proven. The number of tests required under this probabilistic model is shown to be significantly less than under a bounded-size fault set model. Because the number of tests that must be conducted is a measure of the diagnosis overhead, these results represent a dramatic improvement in the performance of system-level diagnosis technique.

  8. Model based fault diagnosis of a rotor-bearing system for misalignment and unbalance under steady-state condition

    NASA Astrophysics Data System (ADS)

    Jalan, Arun Kr.; Mohanty, A. R.

    2009-11-01

    Vibration monitoring is one of the primary techniques of condition monitoring of rotating machines. Shaft misalignment and rotor unbalance are the main sources of vibration in rotating machines. In this study a model based technique for fault diagnosis of rotor-bearing system is described. Using the residual generation technique, residual vibrations are generated from experimental results for the rotor bearing system subject to misalignment and unbalance, and then the residual forces due to presence of faults are calculated. These residual forces are compared with the equivalent theoretical forces due to faults. The fault condition and location of faults are successfully detected by this model based technique.

  9. Sequential Testing Algorithms for Multiple Fault Diagnosis

    NASA Technical Reports Server (NTRS)

    Shakeri, Mojdeh; Raghavan, Vijaya; Pattipati, Krishna R.; Patterson-Hine, Ann

    1997-01-01

    In this paper, we consider the problem of constructing optimal and near-optimal test sequencing algorithms for multiple fault diagnosis. The computational complexity of solving the optimal multiple-fault isolation problem is super-exponential, that is, it is much more difficult than the single-fault isolation problem, which, by itself, is NP-hard. By employing concepts from information theory and AND/OR graph search, we present several test sequencing algorithms for the multiple fault isolation problem. These algorithms provide a trade-off between the degree of suboptimality and computational complexity. Furthermore, we present novel diagnostic strategies that generate a diagnostic directed graph (digraph), instead of a diagnostic tree, for multiple fault diagnosis. Using this approach, the storage complexity of the overall diagnostic strategy reduces substantially. The algorithms developed herein have been successfully applied to several real-world systems. Computational results indicate that the size of a multiple fault strategy is strictly related to the structure of the system.

  10. Use of autocorrelation of wavelet coefficients for fault diagnosis

    NASA Astrophysics Data System (ADS)

    Rafiee, J.; Tse, P. W.

    2009-07-01

    This paper presents a novel time-frequency-based feature recognition system for gear fault diagnosis using autocorrelation of continuous wavelet coefficients (CWC). Furthermore, it introduces an original mathematical approximation of gearbox vibration signals which approximates sinusoidal components of noisy vibration signals generated from gearboxes, including incipient and serious gear failures using autocorrelation of CWC. First, the drawbacks of the continuous wavelet transform (CWT) have been eliminated using autocorrelation function. Secondly, the autocorrelation of CWC is introduced as an original pattern for fault identification in machine condition monitoring. Thirdly, a sinusoidal summation function consisting of eight terms was used to approximate the periodic waveforms generated by autocorrelation of CWC for normal gearboxes (NGs) as well as occurrences of incipient and severe gear fault (e.g. slight-worn, medium-worn, and broken-tooth gears). In other words, the size of vibration signals can be reduced with minimal loss of significant frequency content by means of the sinusoidal approximation of generated autocorrelation waveforms of CWC as reported in this paper.

  11. Gearbox tooth cut fault diagnostics using acoustic emission and vibration sensors--a comparative study.

    PubMed

    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

  12. 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

  13. Fault detection and diagnosis of photovoltaic systems

    NASA Astrophysics Data System (ADS)

    Wu, Xing

    The rapid growth of the solar industry over the past several years has expanded the significance of photovoltaic (PV) systems. One of the primary aims of research in building-integrated PV systems is to improve the performance of the system's efficiency, availability, and reliability. Although much work has been done on technological design to increase a photovoltaic module's efficiency, there is little research so far on fault diagnosis for PV systems. Faults in a PV system, if not detected, may not only reduce power generation, but also threaten the availability and reliability, effectively the "security" of the whole system. In this paper, first a circuit-based simulation baseline model of a PV system with maximum power point tracking (MPPT) is developed using MATLAB software. MATLAB is one of the most popular tools for integrating computation, visualization and programming in an easy-to-use modeling environment. Second, data collection of a PV system at variable surface temperatures and insolation levels under normal operation is acquired. The developed simulation model of PV system is then calibrated and improved by comparing modeled I-V and P-V characteristics with measured I--V and P--V characteristics to make sure the simulated curves are close to those measured values from the experiments. Finally, based on the circuit-based simulation model, a PV model of various types of faults will be developed by changing conditions or inputs in the MATLAB model, and the I--V and P--V characteristic curves, and the time-dependent voltage and current characteristics of the fault modalities will be characterized for each type of fault. These will be developed as benchmark I-V or P-V, or prototype transient curves. If a fault occurs in a PV system, polling and comparing actual measured I--V and P--V characteristic curves with both normal operational curves and these baseline fault curves will aid in fault diagnosis.

  14. 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.

  15. 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

  16. Studies on system-level fault diagnosis and related topics

    SciTech Connect

    Sen, A.

    1987-01-01

    This dissertation deals mainly with the diagnosis aspects of fault-tolerant computing. A number of system models are studied, and their diagnosability conditions established. Fault-diagnosis algorithms for some models are proposed and complexity of diagnosis problem analyzed for some other models. Finally, some fault-tolerant computer networks are studied, and efficient routing algorithms are proposed for these networks.

  17. A Fault Diagnosis Approach for Rolling Bearings Based on EMD Method and Eigenvector Algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Jinyu; Huang, Xianxiang

    Fault diagnosis of rolling bearings is still a very important and difficult research task on engineering. After analyzing the shortcomings of current bearing fault diagnosis technologies, a new approach based on Empirical Mode Decomposition (EMD) and blind equalization eigenvector algorithm (EVA) for rolling bearings fault diagnosis is proposed. In this approach, the characteristic high-frequency signal with amplitude and channel modulation of a rolling bearing with local damage is first separated from the mechanical vibration signal as an Intrinsic Mode Function (IMF) by using EMD, then the source impact vibration signal yielded by local damage is extracted by means of a EVA model and algorithm. Finally, the presented approach is used to analyze an impacting experiment and two real signals collected from rolling bearings with outer race damage or inner race damage. The results show that the EMD and EVA based approach can effectively detect rolling bearing fault.

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

    PubMed

    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

  19. 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

  20. 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.

  1. A PC based fault diagnosis expert system

    NASA Technical Reports Server (NTRS)

    Marsh, Christopher A.

    1990-01-01

    The Integrated Status Assessment (ISA) prototype expert system performs system level fault diagnosis using rules and models created by the user. The ISA evolved from concepts to a stand-alone demonstration prototype using OPS5 on a LISP Machine. The LISP based prototype was rewritten in C and the C Language Integrated Production System (CLIPS) to run on a Personal Computer (PC) and a graphics workstation. The ISA prototype has been used to demonstrate fault diagnosis functions of Space Station Freedom's Operation Management System (OMS). This paper describes the development of the ISA prototype from early concepts to the current PC/workstation version used today and describes future areas of development for the prototype.

  2. 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 hasmoredegraded.less

  3. Statistical Fault Detection & Diagnosis Expert System

    SciTech Connect

    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 has degraded.

  4. Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method

    NASA Astrophysics Data System (ADS)

    Li, Wei; Zhu, Zhencai; Jiang, Fan; Zhou, Gongbo; Chen, Guoan

    2015-01-01

    Fault diagnosis of rotating machinery is receiving more and more attentions. Vibration signals of rotating machinery are commonly analyzed to extract features of faults, and the features are identified with classifiers, e.g. artificial neural networks (ANNs) and support vector machines (SVMs). Due to nonlinear behaviors and unknown noises in machinery, the extracted features are varying from sample to sample, which may result in false classifications. It is also difficult to analytically ensure the accuracy of fault diagnosis. In this paper, a feature extraction and evaluation method is proposed for fault diagnosis of rotating machinery. Based on the central limit theory, an extraction procedure is given to obtain the statistical features with the help of existing signal processing tools. The obtained statistical features approximately obey normal distributions. They can significantly improve the performance of fault classification, and it is verified by taking ANN and SVM classifiers as examples. Then the statistical features are evaluated with a decoupling technique and compared with thresholds to make the decision on fault classification. The proposed evaluation method only requires simple algebraic computation, and the accuracy of fault classification can be analytically guaranteed in terms of the so-called false classification rate (FCR). An experiment is carried out to verify the effectiveness of the proposed method, where the unbalanced fault of rotor, inner race fault, outer race fault and ball fault of bearings are considered.

  5. 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.

  6. Gear faults diagnosis based on wavelet-AR model and PCA

    NASA Astrophysics Data System (ADS)

    Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Peng, Zhongxiao

    2010-08-01

    Gear mechanisms are an important element in a variety of industrial applications and about 80% of the breakdowns of the transmission machinery are caused by the gear failure. Efficient incipient faults detection and accurate faults diagnosis are therefore critical to machinery normal operation. The use of mechanical vibration signals for fault diagnosis is significant and effective due to advances in the progress of digital signal processing techniques. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-faults diagnosis was presented in this paper based on the wavelet-Autoregressive (AR) model and Principal Component Analysis (PCA) method. The virtual prototype simulation and the experimental test were firstly carried out and the comparison results prove that the traditional Fast Fourier Transform Algorithm (FFT) analysis is not appropriate for the gear fault detection and identification. Then the wavelet-AR model was applied to extract the feature sets of the gear fault vibration data. In this procedure, the wavelet transform was used to decompose and de-noise the original signal to obtain fault signals, and the fault type information was extracted by the AR parameters. In order to eliminate the redundant fault features, the PCA was furthermore adopted to fuse the AR parameters into one characteristic to enhance the fault defection and identification. The experimental results indicate that the proposed method based on the wavelet-AR model and PCA is feasible and reliable in the gear multi-faults signal diagnosis, and the isolation of different gear conditions, including normal, single crack, single wear, compound fault of wear and spalling etc., has been effectively accomplished.

  7. Fault detection and diagnosis of HVAC systems

    SciTech Connect

    Han, C.Y.; Xiao, Y.; Ruther, C.J.

    1999-07-01

    This paper presents a model-based fault detection and diagnosis (FDD) system for building heating, ventilating, and air conditioning (HVAC). Model-based fault detection is based on the strategy of determining the difference or the residuals between the normal and the existing patterns. Their approach was to attack the problem on many levels of abstraction: from the signal level, controller programming level, and system component, all the way up to the information and knowledge processing level. The various issues of real implementation of the system and the processing of real-time on-line data in actual systems of campus buildings using the proven technology and off-the-shelf commercial tools are discussed. The research was based on input and output points and software control programs found in typical direct digital control systems used for variable-air-volume air handlers and VAV cooling and hot water reheat terminal units.

  8. 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

  9. Laboratory observations of fault-normal vibrations during stick slip

    SciTech Connect

    Bodin, P.; Brown, S.; Matheson, D.

    1998-12-01

    We report laboratory observations of interface separation waves during stick slip on a fault in a uniform polymer material. Our observations, made at stress levels expected at midcrustal depths, share many macroscopic properties with ruptures of faults in rocks. We observed a drop in fault-normal stress shortly before the onset of, and during, stick slip at points along the fault during a rupture. We suggest that {ital P} wave energy in front of the propagating rupture tip is responsible for the drop in normal stress. We also interpret that stick slip took place within a traveling slip pulse, and we suggest that the dynamic stress drop within the slipping patch exceeded the overall static stress drop by a factor of at least 5 within a few millimeters of the fault. Our experiments did not resolve whether the fault surfaces actually separate or if fault-normal stress is just greatly reduced. In either case the net result is that fault slip is permitted to take place with much less frictional resistance than that expected from the applied load. Our observations provide laboratory evidence that fault-normal vibrations may play an important role in sustaining a rupture by facilitating the propagation of a transient instability. Faults may appear weak in part because they are dynamically weakened as they slip during rupture while retaining their strength during the interseismic period. {copyright} 1998 American Geophysical Union

  10. Implementation of a model based fault detection and diagnosis technique for actuation faults of the SSME

    NASA Technical Reports Server (NTRS)

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

    1991-01-01

    In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the Space Shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the Space Shuttle Main Engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

  11. Automated fault location and diagnosis on electric power distribution feeders

    SciTech Connect

    Zhu, J.; Lubkeman, D.L.; Girgis, A.A.

    1997-04-01

    This paper presents new techniques for locating and diagnosing faults on electric power distribution feeders. The proposed fault location and diagnosis scheme is capable of accurately identifying the location of a fault upon its occurrence, based on the integration of information available from disturbance recording devices with knowledge contained in a distribution feeder database. The developed fault location and diagnosis system can also be applied to the investigation of temporary faults that may not result in a blown fuse. The proposed fault location algorithm is based on the steady-state analysis of the faulted distribution network. To deal with the uncertainties inherent in the system modeling and the phasor estimation, the fault location algorithm has been adapted to estimate fault regions based on probabilistic modeling and analysis. Since the distribution feeder is a radial network, multiple possibilities of fault locations could be computed with measurements available only at the substation. To identify the actual fault location, a fault diagnosis algorithm has been developed to prune down and rank the possible fault locations by integrating the available pieces of evidence. Testing of the developed fault location and diagnosis system using field data has demonstrated its potential for practical use.

  12. 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.

  13. Fault diagnosis with the Aladdin transient classifier

    NASA Astrophysics Data System (ADS)

    Roverso, Davide

    2003-08-01

    The purpose of Aladdin is to assist plant operators in the early detection and diagnosis of faults and anomalies in the plant that either have an impact on the plant performance, or that could lead to a plant shutdown or component damage if allowed to go unnoticed. The kind of early fault detection and diagnosis performed by Aladdin is aimed at allowing more time for decision making, increasing the operator awareness, reducing component damage, and supporting improved plant availability and reliability. In this paper we describe in broad lines the Aladdin transient classifier, which combines techniques such as recurrent neural network ensembles, Wavelet On-Line Pre-processing (WOLP), and Autonomous Recursive Task Decomposition (ARTD), in an attempt to improve the practical applicability and scalability of this type of systems to real processes and machinery. The paper focuses then on describing an application of Aladdin to a Nuclear Power Plant (NPP) through the use of the HAMBO experimental simulator of the Forsmark 3 boiling water reactor NPP in Sweden. It should be pointed out that Aladdin is not necessarily restricted to applications in NPPs. Other types of power plants, or even other types of processes, can also benefit from the diagnostic capabilities of Aladdin.

  14. Automated Diagnosis Of Faults In Antenna-Aiming Systems

    NASA Technical Reports Server (NTRS)

    Smyth, Patrick J.; Mellstrom, Jeffrey A.

    1993-01-01

    Report discusses research directed toward automated diagnosis of faults in complicated electromechanical and hydraulic systems aiming 70-m and 34-m antennas of Deep Space Network communication system.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Envelope extraction based dimension reduction for independent component analysis in fault diagnosis of rolling element bearing

    NASA Astrophysics Data System (ADS)

    Guo, Yu; Na, Jing; Li, Bin; Fung, Rong-Fong

    2014-06-01

    A robust feature extraction scheme for the rolling element bearing (REB) fault diagnosis is proposed by combining the envelope extraction and the independent component analysis (ICA). In the present approach, the envelope extraction is not only utilized to obtain the impulsive component corresponding to the faults from the REB, but also to reduce the dimension of vibration sources included in the sensor-picked signals. Consequently, the difficulty for applying the ICA algorithm under the conditions that the sensor number is limited and the source number is unknown can be successfully eliminated. Then, the ICA algorithm is employed to separate the envelopes according to the independence of vibration sources. Finally, the vibration features related to the REB faults can be separated from disturbances and clearly exposed by the envelope spectrum. Simulations and experimental tests are conducted to validate the proposed method.

  20. Research of singular value decomposition based on slip matrix for rolling bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Cong, Feiyun; Zhong, Wei; Tong, Shuiguang; Tang, Ning; Chen, Jin

    2015-05-01

    Rolling element bearings are at the heart of most rotating machines and they bear the function of connectivity between the rotor and stator. It is important to distinguish the incipient fault before the bearing step into serious failure. The Slip Matrix (SM) construction method based on Singular Value Decomposition (SVD) is proposed in this paper. The SM based fault feature extraction and impulses intelligent detection methods are introduced as the key steps for rolling bearing fault diagnosis. The numerical simulation of rolling bearing fault signal is adopted which shows that the proposed method is good at fault impulses detection in strong background noise environment. The rolling element bearing accelerated life test is performed for the acquisition of experimental data of rolling bearing fault. With the rolling bearing running from normal state to failure, the initial fault signal part can be picked out from the whole life vibration data of the rolling bearing. The vibration signal is close to the nature fault signal which is acquired from a rolling bearing applied in industrial field. The analysis result shows that the proposed method has an excellent performance in rolling bearing fault detection.

  1. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

    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.

  3. 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.

  4. Fault diagnosis for the Space Shuttle main engine

    NASA Technical Reports Server (NTRS)

    Duyar, Ahmet; Merrill, Walter

    1992-01-01

    A conceptual design of a model-based fault detection and diagnosis system is developed for the Space Shuttle main engine. The design approach consists of process modeling, residual generation, and fault detection and diagnosis. The engine is modeled using a discrete time, quasilinear state-space representation. Model parameters are determined by identification. Residuals generated from the model are used by a neural network to detect and diagnose engine component faults. Fault diagnosis is accomplished by training the neural network to recognize the pattern of the respective fault signatures. Preliminary results for a failed valve, generated using a full, nonlinear simulation of the engine, are presented. These results indicate that the developed approach can be used for fault detection and diagnosis. The results also show that the developed model is an accurate and reliable predictor of the highly nonlinear and very complex engine.

  5. Diagnosis without repair for hybrid fault situations. [in computer systems

    NASA Technical Reports Server (NTRS)

    Mallela, S.; Masson, G. M.

    1980-01-01

    In the present paper, the concept of a hybrid fault situation is introduced, which specifies bounded combinations of permanently faulty and intermittently faulty units in a system. The general class of hybrid fault situations includes, as special cases, the all permanent fault case and the unrestricted intermittent fault case, which have been previously considered with PMC models. An approach compatible with the diagnosis of permanent fault situations is then applied to the diagnosis of hybrid fault situation. The motivation for doing so is the common practice of testing for the presence of intermittent faults in systems by means of repeated applications of tests that are designed for the detection of permanent faults. The testing assignment of PMC models of system is characterized, and interrelationships between the number of intermittently and permanently faulty units that can be diagnosed is established.

  6. Fault degradation assessment of water hydraulic motor by impulse vibration signal with Wavelet Packet Analysis and Kolmogorov Smirnov Test

    NASA Astrophysics Data System (ADS)

    Chen, H. X.; Chua, Patrick S. K.; Lim, G. H.

    2008-10-01

    The machinery fault diagnosis is important for improving reliability and performance of systems. Many methods such as Time Synchronous Average (TSA), Fast Fourier Transform (FFT)-based spectrum analysis and short-time Fourier transform (STFT) have been applied in fault diagnosis and condition monitoring of mechanical system. The above methods analyze the signal in frequency domain with low resolution, which is not suitable for non-stationary vibration signal. The Kolmogorov-Smirnov (KS) test is a simple and precise technique in vibration signal analysis for machinery fault diagnosis. It has limited use and advantage to analyze the vibration signal with higher noise directly. In this paper, a new method for the fault degradation assessment of the water hydraulic motor is proposed based on Wavelet Packet Analysis (WPA) and KS test to analyze the impulsive energy of the vibration signal, which is used to detect the piston condition of water hydraulic motor. WPA is used to analyze the impulsive vibration signal from the casing of the water hydraulic motor to obtain the impulsive energy. The impulsive energy of the vibration signal can be obtained by the multi-decomposition based on Wavelet Packet Transform (WPT) and used as feature values to assess the fault degradation of the pistons. The kurtosis of the impulsive energy in the reconstructed signal from the Wavelet Packet coefficients is used to extract the feature values of the impulse energy by calculating the coefficients of the WPT multi-decomposition. The KS test is used to compare the kurtosis of the impulse energy of the vibration signal statistically under the different piston conditions. The results show the applicability and effectiveness of the proposed method to assess the fault degradation of the pistons in the water hydraulic motor.

  7. 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.

  8. Fault diagnosis of direct-drive wind turbine based on support vector machine

    NASA Astrophysics Data System (ADS)

    An, X. L.; Jiang, D. X.; Li, S. H.; Chen, J.

    2011-07-01

    A fault diagnosis method of direct-drive wind turbine based on support vector machine (SVM) and feature selection is presented. The time-domain feature parameters of main shaft vibration signal in the horizontal and vertical directions are considered in the method. Firstly, in laboratory scale five experiments of direct-drive wind turbine with normal condition, wind wheel mass imbalance fault, wind wheel aerodynamic imbalance fault, yaw fault and blade airfoil change fault are carried out. The features of five experiments are analyzed. Secondly, the sensitive time-domain feature parameters in the horizontal and vertical directions of vibration signal in the five conditions are selected and used as feature samples. By training, the mapping relation between feature parameters and fault types are established in SVM model. Finally, the performance of the proposed method is verified through experimental data. The results show that the proposed method is effective in identifying the fault of wind turbine. It has good classification ability and robustness to diagnose the fault of direct-drive wind turbine.

  9. 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.

  10. 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.

  11. An approach to performance assessment and fault diagnosis for rotating machinery equipment

    NASA Astrophysics Data System (ADS)

    Tao, Xiaochuang; Lu, Chen; Lu, Chuan; Wang, Zili

    2013-12-01

    Predict and prevent maintenance is routinely carried out. However, how to address the problem of performance assessment maximizing the use of available monitoring data, and how to build a framework that integrates performance assessment, fault detection, and diagnosis are still a significant challenge. For this purpose, this article introduces an approach to performance assessment and fault diagnosis for rotating machinery, including wavelet packet decomposition for extracting energy feature samples from vibration signals acquired during normal and faulty conditions; clustering analysis for demonstrating the separability of the samples; and Fisher discriminant analysis for providing an optimal lower-dimensional representation, in terms of maximizing the separability among different populations, by projecting the samples into a new space. In the new low-dimensional space, the Mahalanobis distance (MD) between the new measurement data and normal population can be calculated for performance assessment. Moreover, this model for performance assessment only requires data to be available in normal conditions and any one of all possible fault conditions, without the necessity for the full life cycle of condition monitoring data. In addition, if monitoring data under different fault conditions are available, the fault mode can be identified accurately by comparing the MDs between the new measurement data and each fault population. Finally, the proposed method was verified to be successful on performance assessment and fault diagnosis via a hydraulic pump test and a ball bearing test.

  12. 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

  13. Fault diagnosis based on continuous simulation models

    NASA Technical Reports Server (NTRS)

    Feyock, Stefan

    1987-01-01

    The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the diagnosis of system faults. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.

  14. 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.

  15. Fault diagnosis in non-rectangular interconnection networks

    SciTech Connect

    Opper, E.; Lipovski, G.J.

    1983-01-01

    A method for fault diagnosis in a class of multistage interconnection networks which can be used in a real-time multicomputer system is presented. The method allows single and multiple fault diagnosis of regular sw-Banyan networks with arbitrary spread s, arbitrary fanout f, and arbitrary number of levels l, and covers stuck-at-0, stuck-at-1 and bridge type faults in the data and the control parts of the network. The method is based on a graph-theoretic approach and can be applied serially for simple fault detection or in parallel for fault detection and location. Analytical bounds on the number of tests which are needed for the network diagnosis are given and some implementation issues on the Texas reconfigurable array computer are presented. 19 references.

  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. Catastrophic fault diagnosis in dynamic systems using bond graph methods

    SciTech Connect

    Yarom, Tamar.

    1990-01-01

    Detection and diagnosis of faults has become a critical issue in high performance engineering systems as well as in mass-produced equipment. It is particularly helpful when the diagnosis can be made at the initial design level with respect to a prospective fault list. A number of powerful methods have been developed for aiding in the general fault analysis of designs. Catastrophic faults represent the limit case of complete local failure of connections or components. They result in the interruption of energy transfer between corresponding points in the system. In this work the conventional approach to fault detection and diagnosis is extended by means of bond-graph methods to a wide variety of engineering systems. Attention is focused on catastrophic fault diagnosis. A catastrophic fault dictionary is generated from the system model based on topological properties of the bond graph. The dictionary is processed by existing methods to extract a catastrophic fault report to aid the engineer in performing a design analysis.

  18. 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.

  19. Fault diagnosis at substations based on sequential event recorders

    SciTech Connect

    Hertz, A.; Fauquembergue, P. )

    1992-05-01

    Expert systems have been developed to help dispatchers understand the immediate situation after a fault occurrence on Extra High Voltage or High Voltage power systems. This paper presents work specifically related to fault diagnosis at the substation level using recorded sequential signals from various protective and control devices. When necessary, comparisons will be made with relevant diagnostic systems at the centralized, supervisor level.

  20. Pattern-based fault diagnosis using neural networks

    NASA Technical Reports Server (NTRS)

    Dietz, W. E.; Kiech, E. L.; Ali, M.

    1988-01-01

    An architecture for a real-time pattern-based diagnostic expert system capable of accommodating noisy, incomplete, and possibly erroneous input data is outlined. Results from prototype systems applied to jet and rocket engine fault diagnosis are presented. The ability of a neural network-based system to be trained via the presentation of behavioral patterns associated with fault conditions is demonstrated.

  1. 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.

  2. Application of the Teager-Kaiser energy operator in bearing fault diagnosis.

    PubMed

    Henríquez Rodríguez, Patricia; Alonso, Jesús B; Ferrer, Miguel A; Travieso, Carlos M

    2013-03-01

    Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing diagnosis techniques have been developed. Some of them feature the bearing vibration signal with statistical measures and others extract the bearing fault characteristic frequency from the AM component of the vibration signal. In this paper, we propose to transform the vibration signal to the Teager-Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The diagnosis is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal. PMID:23352553

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

    NASA Astrophysics Data System (ADS)

    Bokoski, 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.

  4. Applications of detrended-fluctuation analysis to gearbox fault diagnosis

    NASA Astrophysics Data System (ADS)

    de Moura, E. P.; Vieira, A. P.; Irmão, M. A. S.; Silva, A. A.

    2009-04-01

    Aiming at fault diagnosis, we study vibration signals obtained from gearboxes under various conditions. We consider normal gearboxes, gearboxes containing scratched gears, and gearboxes containing toothless gears, both unloaded and under load, with several rotation frequencies. By applying detrended-fluctuation analysis (DFA), a mathematical tool introduced to study fractal properties of time series, we are able to distinguish the signals with respect to their working conditions. For each signal, DFA involves performing a linear fit to the data inside intervals of a certain size, and evaluating the corresponding fluctuations detrended by the local fit. Repeating this procedure for many interval sizes yields a curve of the average fluctuation as a function of size. From the curves, we define vectors whose components correspond to the average fluctuation associated with suitably chosen interval sizes. We finally apply principal component analysis to the set of all vectors, obtaining very good clustering of the transformed vectors according to the different working conditions, with a performance comparable to that obtained from Fourier analysis, especially for gears working under load.

  5. A survey of an introduction to fault diagnosis algorithms

    NASA Technical Reports Server (NTRS)

    Mathur, F. P.

    1972-01-01

    This report surveys the field of diagnosis and introduces some of the key algorithms and heuristics currently in use. Fault diagnosis is an important and a rapidly growing discipline. This is important in the design of self-repairable computers because the present diagnosis resolution of its fault-tolerant computer is limited to a functional unit or processor. Better resolution is necessary before failed units can become partially reuseable. The approach that holds the greatest promise is that of resident microdiagnostics; however, that presupposes a microprogrammable architecture for the computer being self-diagnosed. The presentation is tutorial and contains examples. An extensive bibliography of some 220 entries is included.

  6. 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.

  7. Automatic fault diagnosis of rotating machines by time-scale manifold ridge analysis

    NASA Astrophysics Data System (ADS)

    Wang, Jun; He, Qingbo; Kong, Fanrang

    2013-10-01

    This paper explores the improved time-scale representation by considering the non-linear property for effectively identifying rotating machine faults in the time-scale domain. A new time-scale signature, called time-scale manifold (TSM), is proposed in this study through combining phase space reconstruction (PSR), continuous wavelet transform (CWT), and manifold learning. For the TSM generation, an optimal scale band is selected to eliminate the influence of unconcerned scale components, and the noise in the selected band is suppressed by manifold learning to highlight the inherent non-linear structure of faulty impacts. The TSM reserves the non-stationary information and reveals the non-linear structure of the fault pattern, with the merits of noise suppression and resolution improvement. The TSM ridge is further extracted by seeking the ridge with energy concentration lying on the TSM signature. It inherits the advantages of both the TSM and ridge analysis, and hence is beneficial to demodulation of the fault information. Through analyzing the instantaneous amplitude (IA) of the TSM ridge, in which the noise is nearly not contained, the fault characteristic frequency can be exactly identified. The whole process of the proposed fault diagnosis scheme is automatic, and its effectiveness has been verified by means of typical faulty vibration/acoustic signals from a gearbox and bearings. A reliable performance of the new method is validated in comparison with traditional enveloping methods for rotating machine fault diagnosis.

  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. Improved CICA algorithm used for single channel compound fault diagnosis of rolling bearings

    NASA Astrophysics Data System (ADS)

    Chen, Guohua; Qie, Longfei; Zhang, Aijun; Han, Jin

    2016-01-01

    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 realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method (E-CICA) is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform(DWT) method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio(ER) of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals.

  10. Anti-aliasing lifting scheme for mechanical vibration fault feature extraction

    NASA Astrophysics Data System (ADS)

    Bao, Wen; Zhou, Rui; Yang, Jianguo; Yu, Daren; Li, Ning

    2009-07-01

    A troublesome problem in application of wavelet transform for mechanical vibration fault feature extraction is frequency aliasing. In this paper, an anti-aliasing lifting scheme is proposed to solve this problem. With this method, the input signal is firstly transformed by a redundant lifting scheme to avoid the aliasing caused by split and merge operations. Then the resultant coefficients and their single subband reconstructed signals are further processed to remove the aliasing caused by the unideal frequency property of lifting filters based on the fast Fourier transform (FFT) technique. Because the aliasing in each subband signal is eliminated, the ratio of signal to noise (SNR) is improved. The anti-aliasing lifting scheme is applied to analyze a practical vibration signal measured from a faulty ball bearing and testing results confirm that the proposed method is effective for extracting weak fault feature from a complex background. The proposed method is also applied to the fault diagnosis of valve trains in different working conditions on a gasoline engine. The experimental results show that using the features extracted from the anti-aliasing lifting scheme for classification can obtain a higher accuracy than using those extracted from the lifting scheme and the redundant lifting scheme.

  11. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory.

    PubMed

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-01

    Sensor data fusion plays an important role in fault diagnosis. Dempster-Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

  12. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

    PubMed Central

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-01

    Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

  13. Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis

    NASA Astrophysics Data System (ADS)

    Liu, Hongmei; Wang, Xuan; Lu, Chen

    2015-08-01

    A rolling bearing vibration signal is nonlinear and non-stationary and has multiple components and multifractal properties. A rolling-bearing fault-diagnosis method based on Local Characteristic-scale Decomposition-Teager Energy Operator (LCD-TEO) and multifractal detrended fluctuation analysis (MF-DFA) is first proposed in this paper. First, the vibration signal was decomposed into several intrinsic scale components (ISCs) by using LCD, which is a newly developed signal decomposition method. Second, the instantaneous amplitude was obtained by applying the TEO to each major ISC for demodulation. Third, the intrinsic multifractality features hidden in each major ISC were extracted by using MF-DFA, among which the generalized Hurst exponents are selected as the multifractal feature in this paper. Finally, the feature vectors were obtained by applying principal components analysis (PCA) to the extracted multifractality features, thus reducing the dimension of the multifractal features and obtaining the fault feature insensitive to variation in working conditions, further enhancing the accuracy of diagnosis. According to the extracted feature vector, rolling bearing faults can be diagnosed under variable working conditions. The experimental results demonstrate its desirable diagnostic performance under both different working conditions and different fault severities. Simultaneously, the results of comparison show that the performance of the proposed diagnostic method outperforms that of Hilbert-Huang transform (HHT) combined with MF-DFA or LCD-TEO combined with mono-fractal analysis.

  14. Fault diagnosis in spur gears based on genetic algorithm and random forest

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

    There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

  15. 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.

  16. 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.

  17. 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

  18. 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

  19. An intelligent fault diagnosis method of rolling bearings based on regularized kernel Marginal Fisher analysis

    NASA Astrophysics Data System (ADS)

    Jiang, Li; Shi, Tielin; Xuan, Jianping

    2012-05-01

    Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.

  20. Fault diagnosis viewed as a left invertibility problem.

    PubMed

    Martnez-Guerra, R; Mata-Machuca, J L; Rincn-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

  1. A data structure and algorithm for fault diagnosis

    NASA Technical Reports Server (NTRS)

    Bosworth, Edward L., Jr.

    1987-01-01

    Results of preliminary research on the design of a knowledge based fault diagnosis system for use with on-orbit spacecraft such as the Hubble Space Telescope are presented. A candidate data structure and associated search algorithm from which the knowledge based system can evolve is discussed. This algorithmic approach will then be examined in view of its inability to diagnose certain common faults. From that critique, a design for the corresponding knowledge based system will be given.

  2. Development of a generic rotating machinery fault diagnosis approach insensitive to machine speed and support type

    NASA Astrophysics Data System (ADS)

    Nembhard, Adrian D.; Sinha, Jyoti K.; Yunusa-Kaltungo, A.

    2015-02-01

    Despite numerous difficulties that can be encountered when using trend monitoring of harmonic components from the simple amplitude spectra to aid diagnosis of rotor related faults on large multi-stage multi-bearing rotating machines, the technique continues to be the mainstay in industry. This is due in part to factors including a lack of adequate experimental validation of newly proposed techniques aimed at improving or replacing this traditional practice. Nevertheless, in recent studies, simple but robust Individual Speed Individual Foundation (ISIF) and Multi-Speed Individual Foundation (MSIF) fault diagnosis (FD) methods that both used a single vibration sensor per bearing without the use of phase information was applied with good results to fixed and variable speed machines respectively. A similar Individual Speed Multi-Foundation (ISMF) technique later enabled FD by direct comparison of vibration data between similarly configured machines with different dynamic characteristics operating at the same steady-state speed. However, the efficacy of these techniques was questioned as they were all applied to experimental rigs with the same few rotor related faults. Thus, the objective of this study is to test the transferability of these said techniques on a wider range of rotor related faults on different machines. A new Multi-Speed Multi-Foundation (MSMF) method which facilitates FD by the direct comparison of vibration data from similarly configured machines with different dynamic characteristics operating at different steady-state speeds is also proposed. It is observed that the previously proposed methods are indeed able to separate the range of conditions tested on machines with different dynamic characteristics. Analysis done with newly proposed MSMF approach gives improved isolation of fault conditions tested compared to the previously proposed techniques.

  3. The role of knowledge structures in fault diagnosis

    NASA Technical Reports Server (NTRS)

    Smith, P. J.; Giffin, W. C.; Rockwell, T. H.; Thomas, M. E.

    1984-01-01

    The use of human memory and knowledge structures to direct fault diagnosis performance was investigated. The performances of 20 pilots with instrument flight ratings were studied in a fault diagnosis task. The pilots were read a scenario which described flight conditions under which the symptoms which are indicative of a problem were detected. They were asked to think out loud as they requested and interpreted various pieces of information to diagnose the cause of the problem. Only 11 of the 20 pilots successfully diagnosed the problem. Pilot performance on this fault diagnosis task was modeled in the use of domain specific knowledge organized in a frame system. Eighteen frames, with a common structure, were necessary to account for the data from all twenty subjects.

  4. Software For Fault-Tree Diagnosis Of A System

    NASA Technical Reports Server (NTRS)

    Iverson, Dave; Patterson-Hine, Ann; Liao, Jack

    1993-01-01

    Fault Tree Diagnosis System (FTDS) computer program is automated-diagnostic-system program identifying likely causes of specified failure on basis of information represented in system-reliability mathematical models known as fault trees. Is modified implementation of failure-cause-identification phase of Narayanan's and Viswanadham's methodology for acquisition of knowledge and reasoning in analyzing failures of systems. Knowledge base of if/then rules replaced with object-oriented fault-tree representation. Enhancement yields more-efficient identification of causes of failures and enables dynamic updating of knowledge base. Written in C language, C++, and Common LISP.

  5. 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.

  6. Object-oriented fault tree models applied to system diagnosis

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; Patterson-Hine, F. A.

    1990-01-01

    When a diagnosis system is used in a dynamic environment, such as the distributed computer system planned for use on Space Station Freedom, it must execute quickly and its knowledge base must be easily updated. Representing system knowledge as object-oriented augmented fault trees provides both features. The diagnosis system described here is based on the failure cause identification process of the diagnostic system described by Narayanan and Viswanadham. Their system has been enhanced in this implementation by replacing the knowledge base of if-then rules with an object-oriented fault tree representation. This allows the system to perform its task much faster and facilitates dynamic updating of the knowledge base in a changing diagnosis environment. Accessing the information contained in the objects is more efficient than performing a lookup operation on an indexed rule base. Additionally, the object-oriented fault trees can be easily updated to represent current system status. This paper describes the fault tree representation, the diagnosis algorithm extensions, and an example application of this system. Comparisons are made between the object-oriented fault tree knowledge structure solution and one implementation of a rule-based solution. Plans for future work on this system are also discussed.

  7. A gearbox fault diagnosis scheme based on near-field acoustic holography and spatial distribution features of sound field

    NASA Astrophysics Data System (ADS)

    Lu, Wenbo; Jiang, Weikang; Yuan, Guoqing; Yan, Li

    2013-05-01

    Vibration signal analysis is the main technique in machine condition monitoring or fault diagnosis, whereas in some cases vibration-based diagnosis is restrained because of its contact measurement. Acoustic-based diagnosis (ABD) with non-contact measurement has received little attention, although sound field may contain abundant information related to fault pattern. A new scheme of ABD for gearbox based on near-field acoustic holography (NAH) and spatial distribution features of sound field is presented in this paper. It focuses on applying distribution information of sound field to gearbox fault diagnosis. A two-stage industrial helical gearbox is experimentally studied in a semi-anechoic chamber and a lab workshop, respectively. Firstly, multi-class faults (mild pitting, moderate pitting, severe pitting and tooth breakage) are simulated, respectively. Secondly, sound fields and corresponding acoustic images in different gearbox running conditions are obtained by fast Fourier transform (FFT) based NAH. Thirdly, by introducing texture analysis to fault diagnosis, spatial distribution features are extracted from acoustic images for capturing fault patterns underlying the sound field. Finally, the features are fed into multi-class support vector machine for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with traditional ABD method. Even with strong noise interference, spatial distribution features of sound field can reliably reveal the fault patterns of gearbox, and thus the satisfactory accuracy can be obtained. The combination of histogram features and gray level gradient co-occurrence matrix features is suggested for good diagnosis accuracy and low time cost.

  8. Research into a distributed fault diagnosis system and its application

    NASA Astrophysics Data System (ADS)

    Qian, Suxiang; Jiao, Weidong; Lou, Yongjian; Shen, Xiaomei

    2005-12-01

    CORBA (Common Object Request Broker Architecture) is a solution to distributed computing methods over heterogeneity systems, which establishes a communication protocol between distributed objects. It takes great emphasis on realizing the interoperation between distributed objects. However, only after developing some application approaches and some practical technology in monitoring and diagnosis, can the customers share the monitoring and diagnosis information, so that the purpose of realizing remote multi-expert cooperation diagnosis online can be achieved. This paper aims at building an open fault monitoring and diagnosis platform combining CORBA, Web and agent. Heterogeneity diagnosis object interoperate in independent thread through the CORBA (soft-bus), realizing sharing resource and multi-expert cooperation diagnosis online, solving the disadvantage such as lack of diagnosis knowledge, oneness of diagnosis technique and imperfectness of analysis function, so that more complicated and further diagnosis can be carried on. Take high-speed centrifugal air compressor set for example, we demonstrate a distributed diagnosis based on CORBA. It proves that we can find out more efficient approaches to settle the problems such as real-time monitoring and diagnosis on the net and the break-up of complicated tasks, inosculating CORBA, Web technique and agent frame model to carry on complemental research. In this system, Multi-diagnosis Intelligent Agent helps improve diagnosis efficiency. Besides, this system offers an open circumstances, which is easy for the diagnosis objects to upgrade and for new diagnosis server objects to join in.

  9. Application of classification functions to chiller fault detection and diagnosis

    SciTech Connect

    Stylianou, M.

    1997-12-31

    This paper describes the application of a statistical pattern recognition algorithm (SPRA) to fault detection and diagnosis of commercial reciprocating chillers. The developed fault detection and diagnosis module has been trained to recognize five distinct conditions, namely, normal operation, refrigerant leak, restriction in the liquid refrigerant line, and restrictions in the water circuits of the evaporator and condenser. The algorithm used in the development is described, and the results of its application to an experimental test bench are discussed. Experimental results show that the SPRA provides an effective way of classifying patterns in multivariable, multiclass problems without having to explicitly use a rule-based system.

  10. 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

  11. Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

    In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

  12. Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Jiang, Huiming; Chen, Jin; Dong, Guangming; Liu, Tao; Chen, Gang

    2015-02-01

    Based on the traditional theory of singular value decomposition (SVD), singular values (SVs) and ratios of neighboring singular values (NSVRs) are introduced to the feature extraction of vibration signals. The proposed feature extraction method is called SV-NSVR. Combined with selected SV-NSVR features, continuous hidden Markov model (CHMM) is used to realize the automatic classification. Then the SV-NSVR and CHMM based method is applied in fault diagnosis and performance assessment of rolling element bearings. The simulation and experimental results show that this method has a higher accuracy for the bearing fault diagnosis compared with those using other SVD features, and it is effective for the performance assessment of rolling element bearings.

  13. Comparative investigation of diagnosis media for induction machine mechanical unbalance fault.

    PubMed

    Salah, Mohamed; Bacha, Khmais; Chaari, Abdelkader

    2013-11-01

    For an induction machine, we suggest a theoretical development of the mechanical unbalance effect on the analytical expressions of radial vibration and stator current. Related spectra are described and characteristic defect frequencies are determined. Moreover, the stray flux expressions are developed for both axial and radial sensor coil positions and a substitute diagnosis technique is proposed. In addition, the load torque effect on the detection efficiency of these diagnosis media is discussed and a comparative investigation is performed. The decisive factor of comparison is the fault sensitivity. Experimental results show that spectral analysis of the axial stray flux can be an alternative solution to cover effectiveness limitation of the traditional stator current technique and to substitute the classical vibration practice. PMID:23938005

  14. Research on fault diagnosis technique on aerocamera communication based on fault tree analysis

    NASA Astrophysics Data System (ADS)

    Li, Lijuan; He, Binggao; Tian, Chengjun; Yang, Chengyu; Duan, Jie

    2008-12-01

    ARINC429 is the standard of digital transmission of avionic device. This paper used fault tree analysis to diagnosis failures of aerocamera 429 communication, built up fault tree of aerocamera 429 communication, analyzed and diagnosed the failures, and designed the detecting flaw, finished aerocamera 429 communication detecting system finally. This detecting system can detect aerocamera 429 communication board fast and effectively, and cut down period of clearing of fault. In addition, it can increase the direction of maintenance and repair, improve the overall function of aerocamera.

  15. Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine.

    PubMed

    Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

    2016-01-01

    This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox. PMID:26848665

  16. Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine

    PubMed Central

    Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

    2016-01-01

    This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox. PMID:26848665

  17. Multiscale envelope manifold for enhanced fault diagnosis of rotating machines

    NASA Astrophysics Data System (ADS)

    Wang, Jun; He, Qingbo; Kong, Fanrang

    2015-02-01

    The wavelet transform has been widely used in the field of machinery fault diagnosis for its good property of band-pass filtering. However, the filtered signal still faces the contamination of in-band noise. This paper focuses on wavelet enveloping, and proposes a new method, called multiscale envelope manifold (MEM), to extract the envelope information of fault impacts with in-band noise suppression. The MEM addresses manifold learning on the wavelet envelopes at multiple scales. Specifically, the proposed method is conducted by three following steps. First, the continuous wavelet transform (CWT) with complex Morlet wavelet base is introduced to obtain the wavelet envelopes at all scales. Second, the wavelet envelopes are restricted in one or more narrow scale bands to simply include the envelope information of fault impacts. The scale band is determined through a smoothness index-based (SI-based) selection method by considering the impulsiveness inside the power spectrum. Third, the manifold learning algorithm is conducted on the wavelet envelopes at selected scales to extract the intrinsic envelope manifold of fault-related impulses. The MEM combines the envelope information at multiple scales in a nonlinear approach, and may thus preserve the factual envelope structure of machinery fault. Simulation studies and experimental verifications confirm that the new method is effective for enhanced fault diagnosis of rotating machines.

  18. Algorithms for Multiple Fault Diagnosis With Unreliable Tests

    NASA Technical Reports Server (NTRS)

    Shakeri, Mojdeh; Raghavan, Vijaya; Pattipati, Krishna R.; Patterson-Hine, Ann

    1997-01-01

    In this paper, we consider the problem of constructing optimal and near-optimal multiple fault diagnosis (MFD) in bipartite systems with unreliable (imperfect) tests. It is known that exact computation of conditional probabilities for multiple fault diagnosis is NP-hard. The novel feature of our diagnostic algorithms is the use of Lagrangian relaxation and subgradient optimization methods to provide: (1) near optimal solutions for the MFD problem, and (2) upper bounds for an optimal branch-and-bound algorithm. The proposed method is illustrated using several examples. Computational results indicate that: (1) our algorithm has superior computational performance to the existing algorithms (approximately three orders of magnitude improvement), (2) the near optimal algorithm generates the most likely candidates with a very high accuracy, and (3) our algorithm can find the most likely candidates in systems with as many as 1000 faults.

  19. Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions

    NASA Astrophysics Data System (ADS)

    Wang, Yi; Xu, Guanghua; Zhang, Qing; Liu, Dan; Jiang, Kuosheng

    2015-07-01

    During the past decades, the conventional envelope analysis has been one of the main approaches in vibration signal processing. However, the envelope analysis is based on stationary assumption, thus it is not applicable to the fault diagnosis of bearings under rotating speed variation conditions. This constraint limits the bearing diagnosis in industrial applications significantly. In order to extend the conventional diagnosis technique to speed variation cases, a rotating speed isolation method is proposed. This method consists of four main steps: (a) a low-pass filter is used to separate the rotating speed components and the resonance frequency band from the original signal; (b) the trend line of instantaneous rotating frequency (IRF) is extracted by ridge detection from the short-time spectrum of the low-pass filtered signal; (c) the envelope signal is obtained by fast kurtogram based resonance demodulation; (d) the trend line of instantaneous fault characteristic frequency (IFCF) is extracted by ridge detection from the short-time spectrum of the envelope signal; (e) the rotating speed is isolated and the instantaneous fault characteristic order (FCO), which is obtained by simply dividing the IFCF by IRF, can be used to identify the fault type. By rotating speed isolation, the bearing faults under speed variation conditions can be detected without additional tachometers. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals. The results show that the proposed method outperforms the conventional envelope analysis method and is effective in bearing diagnosis under speed variation conditions.

  20. A distributed expert system for fault diagnosis

    SciTech Connect

    Cardozo, E.; Talukdar, S.N.

    1988-05-01

    This paper describes a hybrid approach to synthesizing solutions for diagnosis and set covering problems from the area of power system operations. The approach combines expert systems written in a rule-based language (OPS5) with algorithmic programs written in C and Lisp. An environment called DPSK has been developed to allow these programs to be run in parallel in a network of computers. Speeds sufficient for real-time applications can thereby be obtained.

  1. Experimental investigation for fault diagnosis based on a hybrid approach using wavelet packet and support vector classification.

    PubMed

    Li, Pengfei; Jiang, Yongying; Xiang, Jiawei

    2014-01-01

    To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples. PMID:24688361

  2. A hybrid fault diagnosis method using morphological filter-translation invariant wavelet and improved ensemble empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Meng, Lingjie; Xiang, Jiawei; Wang, Yanxue; Jiang, Yongying; Gao, Haifeng

    2015-01-01

    Defective rolling bearing response is often characterized by the presence of periodic impulses, which are usually immersed in heavy noise. Therefore, a hybrid fault diagnosis approach is proposed. The morphological filter combining with translation invariant wavelet is taken as the pre-filter process unit to reduce the narrowband impulses and random noises in the original signal, then the purified signal will be decomposed by improved ensemble empirical mode decomposition (EEMD), in which a new selection method integrating autocorrelation analysis with the first two intrinsic mode functions (IMFs) having the maximum energies is put forward to eliminate the pseudo low-frequency components of IMFs. Applying the envelope analysis on those selected IMFs, the defect information is easily extracted. The proposed hybrid approach is evaluated by simulations and vibration signals of defective bearings with outer race fault, inner race fault, rolling element fault. Results show that the approach is feasible and effective for the fault detection of rolling bearing.

  3. A diagnosis system using object-oriented fault tree models

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; Patterson-Hine, F. A.

    1990-01-01

    Spaceborne computing systems must provide reliable, continuous operation for extended periods. Due to weight, power, and volume constraints, these systems must manage resources very effectively. A fault diagnosis algorithm is described which enables fast and flexible diagnoses in the dynamic distributed computing environments planned for future space missions. The algorithm uses a knowledge base that is easily changed and updated to reflect current system status. Augmented fault trees represented in an object-oriented form provide deep system knowledge that is easy to access and revise as a system changes. Given such a fault tree, a set of failure events that have occurred, and a set of failure events that have not occurred, this diagnosis system uses forward and backward chaining to propagate causal and temporal information about other failure events in the system being diagnosed. Once the system has established temporal and causal constraints, it reasons backward from heuristically selected failure events to find a set of basic failure events which are a likely cause of the occurrence of the top failure event in the fault tree. The diagnosis system has been implemented in common LISP using Flavors.

  4. On fault-tolerant structure, distributed fault-diagnosis, reconfiguration, and recovery of the array processors

    SciTech Connect

    Hosseini, S.H.

    1989-07-01

    The increasing need for the design of high-performance computers has led to the design of special purpose computers such as array processors. This paper studies the design of fault-tolerant array processors. First, it is shown how hardware redundancy can be employed in the existing structures in order to make them capable of withstanding the failure of some of the array links and processors. Then distributed fault-tolerance schemes are introduced for the diagnosis of the faulty elements, reconfiguration, and recovery of the array. Fault tolerance is maintained by the cooperation of processors in a decentralized form of control without the participation of any type of hardcore or fault-free central controller such as a host computer.

  5. 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

  6. A feature extraction method based on information theory for fault diagnosis of reciprocating machinery.

    PubMed

    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

  7. 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.

  8. 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.

  9. A coupled rotor-fuselage vibration analysis for helicopter rotor system fault detection

    NASA Astrophysics Data System (ADS)

    Yang, Mao

    A coupled rotor-fuselage vibration analysis for helicopter rotor system fault detection is developed. The coupled rotor/fuselage/vibration absorbers (bifilar type) system incorporates consistent structural, aerodynamic and inertial couplings. The aeroelastic analysis is based on finite element methods in space and time. The coupled rotor, absorbers and fuselage equations are transformed into the modal space and solved in the fixed coordinate system. A coupled trim procedure is used to solve the responses of rotor, fuselage and vibration absorber, rotor trim control and vehicle orientation simultaneously. Rotor system faults are modeled by changing blade structural, inertial and aerodynamic properties. Both adjustable and component faults, such as misadjusted trim-tab, misadjusted pitch-control rod (PCR), imbalanced mass and pitch-control bearing freeplay, are investigated. Detailed SH-60 helicopter fuselage NASTRAN model is integrated into the analysis. Validation study was performed using SH-60 helicopter flight test data. The prediction of fuselage natural frequencies show fairly large error compared to shake test data. Analytical predictions of fuselage baseline (without fault) 4/rev vibration and fault-induced 1/rev vibration and blade displacement deviations are compared with SH-60 flight test (with prescribed fault) data. The fault-induced 1/rev fuselage vibration (magnitude and phase) predicted by present analysis generally capture the trend of the flight test data, although prediction under-predicts. The large discrepancy of fault-induced 1/rev vibration magnitude at hover between prediction and flight test data partially comes from the variation of flight condition (not perfect hover) and partially due to the effect of the rotor-fuselage aerodynamic interaction (wake effect) at low speed which is not considered in the analysis. Also the differences in the phase prediction is not clear since only the magnitude and phase information were given instead of the original vibration time-history. The imbalanced mass fault causes higher 1/rev roll vibration that is insensitive to the airspeed. The misadjusted trim-tab fault induced 1/rev vertical vibration increases with airspeed. The misadjusted pitch-control rod fault causes high vibration at hover. A parametric study was conducted to identify key factors that affect the fault-induced fuselage vibration. Analysis show that elastic fuselage model and precise hub modeling (inclusion of vibration absorbers) are essential to the vibration pre diction. The analysis shows that a compound fault can be expressed as a linear combination of individual faults involved. Aircraft operational parameters, such as gross-weight; center of gravity location, flight speed, flight path and aircraft configuration, have significant impact on the fault-induced 1/rev vibration. Prediction show that there are certain patterns in the fault-induced 1/rev hub-loads. Thus measuring both fuselage vibration and hub loads may benefit rotor system fault detection.

  10. Exchanged ridge demodulation of time-scale manifold for enhanced fault diagnosis of rotating machinery

    NASA Astrophysics Data System (ADS)

    Wang, Jun; He, Qingbo

    2014-05-01

    The vibration or acoustic signal from rotating machinery with localized fault usually behaves as the form of amplitude modulation (AM) and/or frequency modulation (FM). The demodulation techniques are conventional ways to reveal the fault characteristics from the analyzed signals. One of these techniques is the time-scale manifold (TSM) ridge demodulation method with the merits of good time-frequency localization and in-band noise suppression properties. However, due to the essential attribute of wavelet ridge, the survived in-band noise on the achieved TSM will still disturb the envelope extraction of fault-induced impulses. This paper presents an improved TSM ridge demodulation method, called exchanged ridge demodulation of TSM, by combining the benefits of the first two TSMs: the noise suppression of the first TSM and the noise separation of the second TSM. Specifically, the ridge on the second TSM can capture the fault-induced impulses precisely while avoiding the in-band noise smartly. By putting this ridge on the first TSM, the corresponding instantaneous amplitude (IA) waveform can represent the real envelope of pure faulty impulses. Moreover, an adaptive selection method for Morlet wavelet parameters is also proposed based on the smoothness index (SI) in the time-scale domain for an optimal time-scale representation of analyzed signal. The effectiveness of the proposed method is verified by means of a simulation study and applications to diagnosis of bearing defects and gear fault.

  11. Development of an on-line diagnosis system for rotor vibration via model-based intelligent inference

    PubMed

    Bai; Hsiao; Tsai; Lin

    2000-01-01

    An on-line fault detection and isolation technique is proposed for the diagnosis of rotating machinery. The architecture of the system consists of a feature generation module and a fault inference module. Lateral vibration data are used for calculating the system features. Both continuous-time and discrete-time parameter estimation algorithms are employed for generating the features. A neural fuzzy network is exploited for intelligent inference of faults based on the extracted features. The proposed method is implemented on a digital signal processor. Experiments carried out for a rotor kit and a centrifugal fan indicate the potential of the proposed techniques in predictive maintenance. PMID:10641641

  12. Visual Analysis of SOM Network in Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    Ji-Hong, Ren; Jiang-Cheng, Chen; Nan, Wang

    SOM network (self-organizing feature map neural network) learning with no instructors which has self-adaptive, self-learning features. The advantage is to maintain the topology of original data. It is in extensive application in the field of the data classification, knowledge acquisition, process monitoring fault identification and so on. SOM network is used for rotor fault diagnosis. The U matrix map and D matrix is used as visualization tools to simulate and analyses the classification results, and it is com-pared with the general SOM network clustering results. The conclusion is that the SOM network visualization method is simple and easy to understand, and has high rate in fault discrimination.

  13. Sensor-based fault diagnosis in a flight expert system

    NASA Technical Reports Server (NTRS)

    Ali, M.; Scharnhorst, D. A.

    1985-01-01

    A prototype of a knowledge-based flight expert system (FLES) has been developed to assist airplane pilots in monitoring, analyzing, and diagnosing faults and to provide support in reducing the pilot's own mistakes. A sensor simulation model has been developed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Advanced Concept System (ACS), a future-generation airplane, and partly on the Boeing 737, an existing airplane. The architecture of FLES contains several subsystems. One of the major subsystems performs fault diagnosis in the electrical system of the ACS. This paper describes the mechanism and functionality of the automatic diagnosis performed in this expert system.

  14. Semi-supervised learning and condition fusion for fault diagnosis

    NASA Astrophysics Data System (ADS)

    Yuan, Jin; Liu, Xuemei

    2013-07-01

    Supervised learning has been developed to collect condition monitoring (CM) data for fault diagnosis and prognosis. However, labeling the condition monitoring data is expensive due to the use of field knowledge while unlabeled CM data contain significant information of normal conditions or faults, which cannot be explored by supervised learning. Manifold regularization (MR) based semi-supervised learning (SSL) is first introduced to fault detection by utilizing both labeled and unlabeled CM data, and then a new single-conditions labeled mode based on MR is proposed for SSL learning. This approach, leveraged by effectively exploiting the embedded intrinsic geometric manifolds, outperforms supervised learning in both single-conditions labeled and all-conditions labeled modes within the application of two real-life fault detection datasets. The experimental results also suggest that most effective classifier in practical application could be trained by the SSL approach and fault type representation with medium load condition. The improved predictive performance implies that the manifold assumption of MR has its inherent fundamentals. Finally, the manifold fundamental of single-conditions labeled mode is analyzed with dimensionality reduction.

  15. Reconfigurable control system design for fault diagnosis and accommodation.

    PubMed

    Ho, Liang-Wei; Yen, Gary G

    2002-12-01

    The growing demand in system reliability and survivability under failures has urged ever-increasing research effort on the development of fault diagnosis and accommodation. In this paper, the on-line fault tolerant control problem for dynamic systems under unanticipated failures is investigated from a realistic point of view without any specific assumption on the type of system dynamical structure or failure scenarios. The sufficient conditions for system on-line stability under catastrophic failures have been derived using the discrete-time Lyapunov stability theory. Based upon the existing control theory and the modern computational intelligence techniques, an on-line fault accommodation control strategy is proposed to deal with the desired trajectory-tracking problems for systems suffering from various unknown and unanticipated catastrophic component failures. Theoretical analysis indicates that the control problem of interest can be solved on-line without a complete realization of the unknown failure dynamics provided an on-line estimator satisfies certain conditions. Through the on-line estimator, effective control signals to accommodate the dynamic failures can be computed using only the partially available information of the faults. Several on-line simulation studies have been presented to demonstrate the effectiveness of the proposed strategy. To investigate the feasibility of using the developed technique for unanticipated fault accommodation in hardware under the real-time environment, an on-line fault tolerant control test bed has been constructed to validate the proposed technology. Both on-line simulations and the real-time experiment show encouraging results and promising futures of on-line real-time fault tolerant control based solely upon insufficient information of the system dynamics and the failure dynamics. PMID:12528199

  16. Modeling, estimation, fault detection and fault diagnosis of spacecraft air contaminants

    NASA Astrophysics Data System (ADS)

    Narayan, Anand P.

    1998-07-01

    The objective of this dissertation is to develop a framework for the modeling, estimation, fault detection and diagnosis of air contaminants aboard spacecraft. Safe air is a vital resource aboard spacecraft for crewed missions, and especially so in long range missions, where the luxury of returning to earth for a clean-up does not exist. This research uses modern control theory in conjunction with advanced fluid mechanics to achieve the objective of developing an implementable comprehensive monitoring systems, suitable for use on space missions. First, a three-dimensional transport model is developed in order to model the dispersion of air contaminants. The flow field, which is an important input to the transport model, is obtained by solving the Navier Stokes equations for the cabin geometry and the appropriate boundary conditions, using a finite element method. Steady flow fields are computed for various conditions for both laminar and turbulent cases. Contamination dispersion studies are undertaken both for routine substances introduced through the inlet ducts and for emissions of toxics inside the cabin volume. The dispersion studies indicate that lumped models and even a two-dimensional model are sometimes inadequate to assure that the Spacecraft Maximum Allowable Concentrations (SMACs) are not exceeded locally. Since the research was targeted at real-time application aboard Spacecraft, a state estimation routine is implemented using Implicit Kalman Filtering. The routine makes use of the model predictions and measurements from the sensor system in order to arrive at an optimal estimate of the state of the system for each time step. Fault detection is accomplished through the use of analytical redundancy, where error residuals from the Kalman filter are monitored in order to detect any faults in the system, and to distinguish between sensor and process faults. Finally, a fault diagnosis system is developed, which is a combination of sensitivity analysis and an Extended Kalman Filter, which is used to estimate the location and capacity of an unknown source emission in the system. The sensitivity analysis involves pre-calculating sensitivity coefficients, which measure the response of each sensor to a source emission at each location in the cabin, and in the event of a fault, current measurements are used and inverted to arrive at an initial guess for the unknown source that is causing the fault. An Extended Implicit Kalman filter, developed especially for this application then makes use of the initial guess to arrive at an optimal estimate for the unknown source, by minimizing the squared estimation error. The fault diagnosis procedure is successfully tested for various test cases.

  17. 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.

  18. Application of hybrid wavelet neural network for missile fault diagnosis system

    NASA Astrophysics Data System (ADS)

    Hu, Jun; Li, Guiyan; Jia, Shaowen

    2007-11-01

    In missile weapon system, exact fault prediction and diagnosis is very important for missile security, according to the specialty and complexity of the missile fault diagnosis, a novel expert system design method based on the hybrid neural network ensembles is proposed in this paper. To improve the limitation of applying traditional fault diagnosis method to the diagnosis method of the diagnosis of missile fault, with large amounts of typical missile fault samples and raw measurable parametric data available, the missile fault diagnosis system based on wavelet neural network ensembles can be created applying general construction techniques of the wavelet neural network fault diagnosis system, including signal binary wavelet transform, fault feature extraction/selection and network training. The back-propagation (BP) algorithm is used to fulfill the parameter initialization and the neural network structure (WNN). By means of choosing enough practical samples to verity the wavelet neural network (WNN) and the information representing the faults is inputted into the trained WNN, and according to the output result the type of fault can be determined. It's proved that through diagnosis of the missile from several different sides by use of different parameters the diagnosis result is more reliable. The method can be generalized to other devices' fault diagnosis.

  19. LMD method and multi-class RWSVM of fault diagnosis for rotating machinery using condition monitoring information.

    PubMed

    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

  20. A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains

    NASA Astrophysics Data System (ADS)

    Hu, Bingbing; Li, Bing

    2016-02-01

    It is very difficult to detect weak fault signatures due to the large amount of noise in a wind turbine system. Multiscale noise tuning stochastic resonance (MSTSR) has proved to be an effective way to extract weak signals buried in strong noise. However, the MSTSR method originally based on discrete wavelet transform (DWT) has disadvantages such as shift variance and the aliasing effects in engineering application. In this paper, the dual-tree complex wavelet transform (DTCWT) is introduced into the MSTSR method, which makes it possible to further improve the system output signal-to-noise ratio and the accuracy of fault diagnosis by the merits of DTCWT (nearly shift invariant and reduced aliasing effects). Moreover, this method utilizes the relationship between the two dual-tree wavelet basis functions, instead of matching the single wavelet basis function to the signal being analyzed, which may speed up the signal processing and be employed in on-line engineering monitoring. The proposed method is applied to the analysis of bearing outer ring and shaft coupling vibration signals carrying fault information. The results confirm that the method performs better in extracting the fault features than the original DWT-based MSTSR, the wavelet transform with post spectral analysis, and EMD-based spectral analysis methods.

  1. 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.

  2. Design of a fault diagnosis system for next generation nuclear power plants

    SciTech Connect

    Zhao, K.; Upadhyaya, B.R.; Wood, R.T.

    2004-07-01

    A new design approach for fault diagnosis is developed for next generation nuclear power plants. In the nuclear reactor design phase, data reconciliation is used as an efficient tool to determine the measurement requirements to achieve the specified goal of fault diagnosis. In the reactor operation phase, the plant measurements are collected to estimate uncertain model parameters so that a high fidelity model can be obtained for fault diagnosis. The proposed algorithm of fault detection and isolation is able to combine the strength of first principle model based fault diagnosis and the historical data based fault diagnosis. Principal component analysis on the reconciled data is used to develop a statistical model for fault detection. The updating of the principal component model based on the most recent reconciled data is a locally linearized model around the current plant measurements, so that it is applicable to any generic nonlinear systems. The sensor fault diagnosis and process fault diagnosis are decoupled through considering the process fault diagnosis as a parameter estimation problem. The developed approach has been applied to the IRIS helical coil steam generator system to monitor the operational performance of individual steam generators. This approach is general enough to design fault diagnosis systems for the next generation nuclear power plants. (authors)

  3. 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.

  4. Sequential fuzzy diagnosis method for motor roller bearing in variable operating conditions based on vibration analysis.

    PubMed

    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

  5. 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

  6. An implementation of a hybrid intelligent tool for distribution system fault diagnosis

    SciTech Connect

    Momoh, J.A.; Dias, L.G.; Laird, D.N.

    1997-04-01

    The common fault in distribution systems due to line outages consists of single-line-to-ground (SLG) faults, with low or high fault impedance. The presence of arcing is commonplace in high impedance SLG faults. Recently, artificial intelligence (AI) based techniques have been introduced for low/high impedance fault diagnosis in ungrounded distribution systems and high impedance fault diagnosis in grounded distribution systems. So far no tool has been developed to identify, locate and classify faults on grounded and ungrounded systems. This paper describes an integrated package for fault diagnosis in either grounded or ungrounded distribution systems. It utilizes rule based schemes as well as artificial neural networks (ANN) to detect, classify and locate faults. Its application on sample test data as well as field test data are reported in the paper.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. Time-series methods for fault detection and identification in vibrating structures.

    PubMed

    Fassois, Spilios D; Sakellariou, John S

    2007-02-15

    An overview of the principles and techniques of time-series methods for fault detection, identification and estimation in vibrating structures is presented, and certain new methods are introduced. The methods are classified, and their features and operation are discussed. Their practicality and effectiveness are demonstrated through brief presentations of three case studies pertaining to fault detection, identification and estimation in an aircraft panel, a scale aircraft skeleton structure and a simple nonlinear simulated structure. PMID:17255046

  14. 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

  15. 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

  16. 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.

  17. Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants

    NASA Astrophysics Data System (ADS)

    Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

    2013-02-01

    Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system.

  18. Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants.

    PubMed

    Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

    2013-02-01

    Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system. PMID:23464251

  19. 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.

  20. Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

    NASA Astrophysics Data System (ADS)

    Cabrera, Diego; Sancho, Fernando; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Li, Chuan; Vásquez, Rafael E.

    2015-09-01

    This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal's condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients' energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters' space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.

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

    PubMed Central

    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

  3. Expert systems for fault diagnosis in nuclear reactor control

    NASA Astrophysics Data System (ADS)

    Jalel, N. A.; Nicholson, H.

    1990-11-01

    An expert system for accident analysis and fault diagnosis for the Loss Of Fluid Test (LOFT) reactor, a small scale pressurized water reactor, was developed for a personal computer. The knowledge of the system is presented using a production rule approach with a backward chaining inference engine. The data base of the system includes simulated dependent state variables of the LOFT reactor model. Another system is designed to assist the operator in choosing the appropriate cooling mode and to diagnose the fault in the selected cooling system. The response tree, which is used to provide the link between a list of very specific accident sequences and a set of generic emergency procedures which help the operator in monitoring system status, and to differentiate between different accident sequences and select the correct procedures, is used to build the system knowledge base. Both systems are written in TURBO PROLOG language and can be run on an IBM PC compatible with 640k RAM, 40 Mbyte hard disk and color graphics.

  4. 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.

  5. 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.

  6. Human problem solving performance in a fault diagnosis task

    NASA Technical Reports Server (NTRS)

    Rouse, W. B.

    1978-01-01

    It is proposed that humans in automated systems will be asked to assume the role of troubleshooter or problem solver and that the problems which they will be asked to solve in such systems will not be amenable to rote solution. The design of visual displays for problem solving in such situations is considered, and the results of two experimental investigations of human problem solving performance in the diagnosis of faults in graphically displayed network problems are discussed. The effects of problem size, forced-pacing, computer aiding, and training are considered. Results indicate that human performance deviates from optimality as problem size increases. Forced-pacing appears to cause the human to adopt fairly brute force strategies, as compared to those adopted in self-paced situations. Computer aiding substantially lessens the number of mistaken diagnoses by performing the bookkeeping portions of the task.

  7. 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.

  8. Multi-stable stochastic resonance and its application research on mechanical fault diagnosis

    NASA Astrophysics Data System (ADS)

    Li, Jimeng; Chen, Xuefeng; He, Zhengjia

    2013-10-01

    It is difficult to extract the fault features of a rotating machine via vibration analysis due to interference from background noise. Stochastic resonance (SR), used as a method of utilising noise to amplify weak signals in nonlinear dynamical systems, can detect weak signals overwhelmed in the noise. However, the detection effect of current SR methods is still unsatisfactory. To further increase the output signal-to-noise ratio (SNR) and improve the detection effect of SR, the present study proposes an improved SR method with a multi-stable model for identifying the defect-induced rotating machine faults by analysing the influence relationship between the resonance model and the resonance effect. Due to the structural characteristics of three potential wells and two barriers, the proposed resonance model can not only further amplify weak signals, but also convert into a monostable model, a bistable model or a tristable model. This result is achieved by adjusting system parameters and thus obtaining a better matching of the input signals and resonance models. Therefore, the multi-stable SR method, combined with the characteristics of the multi-stable model, can both increase the output SNR and improve the detection effect and also detect the low SNR signals and enhance the processing capability of SR for weak signals. Finally, the proposed method is applied to a gearbox fault diagnosis in a rolling mill in which two local faults located in the big gear and the pinion, respectively, are found successfully. It can be concluded that multi-stable SR method has practical value in engineering. The influence relationship between a resonance model and SR is analysed. An improved SR method with a multi-stable model is presented. The signal processing performance of multi-stable SR is analysed comparatively. Simulation and application show the validity and superiority of multi-stable SR.

  9. Development of rules for single-line fault diagnosis in delta-delta connected distribution systems

    SciTech Connect

    Momoh, J.A.; Dias, L.G.; Thor, T.; Laird, D.N.

    1994-12-31

    Single-line fault diagnosis in delta-delta connected distribution systems suffers due to the low fault currents associated with such faults. Simulation tests on this type of system reveals that rule based decision support can be used of such diagnosis. This paper describes the development of rules for single-line fault diagnosis utilizing simulation test results. The key parameters used are the voltage magnitude of each phase at the bus bar and the currents on the feeders including their sequence components.

  10. The use of hybrid automata for fault-tolerant vibration control for parametric failures

    NASA Astrophysics Data System (ADS)

    Byreddy, Chakradhar; Frampton, Kenneth D.; Yongmin, Kim

    2006-03-01

    The purpose of this work is to make use of hybrid automata for vibration control reconfiguration under system failures. Fault detection and isolation (FDI) filters are used to monitor an active vibration control system. When system failures occur (specifically parametric faults) the FDI filters detect and identify the specific failure. In this work we are specifically interested in parametric faults such as changes in system physical parameters; however this approach works equally well with additive faults such as sensor or actuator failures. The FDI filter output is used to drive a hybrid automaton, which selects the appropriate controller and FDI filter from a library. The hybrid automata also implements switching between controllers and filters in order to maintain optimal performance under faulty operating conditions. The biggest challenge in developing this system is managing the switching and in maintaining stability during the discontinuous switches. Therefore, in addition to vibration control, the stability associated with switching compensators and FDI filters is studied. Furthermore, the performance of two types of FDI filters is compared: filters based on parameter estimation methods and so called "Beard-Jones" filters. Finally, these simulations help in understanding the use of hybrid automata for fault-tolerant control.

  11. Fault diagnosis of an air-handling unit using artificial neural networks

    SciTech Connect

    Lee, W.Y.; House, J.M.; Park, C.; Kelly, G.E.

    1996-11-01

    The objective of this study is to describe the application of artificial neural networks to the problem of fault diagnosis in an air-handling unit. Initially, residuals of system variables that can be used to quantify the dominant symptoms of fault modes of operation are selected. Idealized steady-state patterns of the residuals are then defined for each fault mode of operation. The steady-state relationship between the dominant symptoms and the faults is learned by an artificial neural network using the backpropagation algorithm. The trained neural network is applied to experimental data for various faults and successfully identifies each fault.

  12. 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.

  13. 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

  14. Gearbox fault diagnosis using adaptive zero phase time-varying filter based on multi-scale chirplet sparse signal decomposition

    NASA Astrophysics Data System (ADS)

    Wu, Chunyan; Liu, Jian; Peng, Fuqiang; Yu, Dejie; Li, Rong

    2013-07-01

    When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

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

    NASA Astrophysics Data System (ADS)

    Damour, Cdric; 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.

  16. 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.

  17. Low-cost coding techniques for digital fault diagnosis

    NASA Technical Reports Server (NTRS)

    Avizienis, A. A.

    1973-01-01

    Published report discusses fault location properties of arithmetic codes. Criterion for effectiveness of given code is detection probability of local fault by application of checking algorithm to results of entire set of algorithms of processor. Report also presents analysis of arithmetic codes with low-cost check algorithm which possesses partial fault-location properties.

  18. An expert system for fault diagnosis in a Space Shuttle main engine

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Gupta, U. K.

    1990-01-01

    The detection and diagnosis of SSME faults in an early stage is important in order to allow enough time for fault preventive or corrective measurements. Since most of the faults in a complex system like SSME develop rapidly, early detection and diagnosis of faults is critical for the survival of space vehicles. An expert system has been designed for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations. This paper describes an innovative machine learning approach which is employed for the automatic training of this expert system.

  19. On the application of a machine learning technique to fault diagnosis of power distribution lines

    SciTech Connect

    Togami, Masato; Abe, Norihiro; Kitahashi, T.; Ogawa, Harunao

    1995-10-01

    This paper presents one method for fault diagnosis of power distribution lines by using a decision tree. The conventional method, using a decision tree, applies only to discrete attribute values. To apply it to fault diagnosis of power distribution lines, in practice it must be revised in order to treat attributes whose values range over certain widths. This is because the sensor value or attribute value varies owing to the resistance of the fault point or is influenced by noise. The proposed method is useful when the attribute value has such a property, and it takes into consideration the cost of acquiring the information and the probability of the occurrence of a fault.

  20. 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.

  1. Fault detection and diagnosis of power converters using artificial neural networks

    SciTech Connect

    Swarup, K.S.; Chandrasekharaiah, H.S.

    1995-12-31

    Fault detection and diagnosis in real-time are areas of research interest in knowledge-based expert systems. Rule-based and model-based approaches have been successfully applied to some domains, but are too slow to be effectively applied in a real-time environment. This paper explores the suitability of using artificial neural networks for fault detection and diagnosis of power converter systems. The paper describes a neural network design and simulation environment for real-time fault diagnosis of thyristor converters used in HVDC power transmission systems.

  2. A new angular resampling algorithm for the bearing fault diagnosis under the time-varying rotational speed

    NASA Astrophysics Data System (ADS)

    Wang, Tianyang; Cheng, Weidong; Li, Jianvong; Chu, Fulei

    2015-07-01

    Order tracking is one of the most effective algorithms to eliminate the effect of time-varying rotational speed on the rotary machines. However, this algorithm is not suitable for the faulty rolling bearing unless the peak time of the fault-induced impulse is set as zero which cannot be met in the real engineering. The traditional resampling process will cause uneven intervals between the adjacent impulse peaks in the angular domain and then affect the envelope analysis-based diagnosis result. To solve this problem, a new resampling algorithm with three parts is proposed: (a) linearly fitting the instantaneous rotational speed measured by the tachometer, (b) resampling the vibration signal from the time domain to the angular domain using the traditional method, (c) calculating the envelope deformation amount and then compensating the resampled result. The effectiveness of the proposed method has been validated by both the simulated and experimental bearing vibration signals.

  3. 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.

  4. Development of a fault diagnosis method for heating systems using neural networks

    SciTech Connect

    Li, X.; Vaezi-Nejad, H.; Visier, J.C.

    1996-11-01

    The application of artificial neural networks (ANNs) for developing a fault diagnosis (FD) method in complex heating systems is presented in this paper. The six operating modes with faults used to develop this FD method came from the results of a detailed investigation in cooperation with heating system maintenance experts and are among the most important operating faults for this type of system. Because a daily diagnosis is generally sufficient, the ANNs have been developed using the daily values obtained by a preprocessing of the numerical simulation data. This paper presents the first step of the method development. It demonstrates the feasibility of using ANNs for fault diagnosis of a specific heating, ventilating, and air-conditioning (HVAC) system provided training data representative of the behavior of the system with and without faults are available. The next step will consist of developing a generic method that requires less training data.

  5. An artificial intelligence approach to onboard fault monitoring and diagnosis for aircraft applications

    NASA Technical Reports Server (NTRS)

    Schutte, P. C.; Abbott, K. H.

    1986-01-01

    Real-time onboard fault monitoring and diagnosis for aircraft applications, whether performed by the human pilot or by automation, presents many difficult problems. Quick response to failures may be critical, the pilot often must compensate for the failure while diagnosing it, his information about the state of the aircraft is often incomplete, and the behavior of the aircraft changes as the effect of the failure propagates through the system. A research effort was initiated to identify guidelines for automation of onboard fault monitoring and diagnosis and associated crew interfaces. The effort began by determining the flight crew's information requirements for fault monitoring and diagnosis and the various reasoning strategies they use. Based on this information, a conceptual architecture was developed for the fault monitoring and diagnosis process. This architecture represents an approach and a framework which, once incorporated with the necessary detail and knowledge, can be a fully operational fault monitoring and diagnosis system, as well as providing the basis for comparison of this approach to other fault monitoring and diagnosis concepts. The architecture encompasses all aspects of the aircraft's operation, including navigation, guidance and controls, and subsystem status. The portion of the architecture that encompasses subsystem monitoring and diagnosis was implemented for an aircraft turbofan engine to explore and demonstrate the AI concepts involved. This paper describes the architecture and the implementation for the engine subsystem.

  6. A general model for the study of fault tolerance and diagnosis.

    NASA Technical Reports Server (NTRS)

    Meyer, J. F.

    1973-01-01

    The concept of a 'system with faults' is introduced as a suggested point of departure for the theoretical study of fault tolerance and diagnosis in systems. The model is defined relative to a general representation scheme for systems and, depending on the choice of representation, can be used to investigate either hardware or software faults that occur during either the design or use of a system.

  7. 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%.

  8. On-line early fault detection and diagnosis of municipal solid waste incinerators

    SciTech Connect

    Zhao Jinsong Huang Jianchao; Sun Wei

    2008-11-15

    A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the fault diagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows that automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI.

  9. 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

  10. 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

  11. Fault Diagnosis of Steam Generator Using Signed Directed Graph and Artificial Neural Networks

    SciTech Connect

    Aly, Mohamed N.; Hegazy, Hesham N.

    2006-07-01

    Diagnosis is a very complex and important task for finding the root cause of faults in nuclear power plants. The objective of this paper is to investigate the feasibility of using the combination of signed directed graph (SDG) and artificial neural networks for fault diagnosis in nuclear power plants especially in U-Tube steam generator. Signed directed graph has been the most widely used form of qualitative based model methods for process fault diagnosis. It is constructed to represent the cause-effect relations among the dynamic process variables. Signed directed graph consists of nodes represent the process variables and branches. The branch represents the qualitative influence of a process variable on the related variable. The main problem in fault diagnosis using the signed directed graph is the unmeasured variables. Therefore, neural networks are used to estimate the values of unmeasured nodes. In this work, different four cases of faults in the steam generator ( SG) have been diagnosed, three of them are single fault and the fourth is multiple fault. The first three faults are by pass valve leakage (Vbp(+)), main feed water valve opening increase (Vfw(+)), main feed water valve opening decrease (Vfw (-)). The fourth fault is a multiple fault where by-pass valve leakage and main feed water valve opening decrease (Vbp(+) and Vfw (-)) in the same time. The used data are collected from a basic principle simulator of pressurized water reactor 925 Mwe. The signed directed graph of the steam generator is constructed to represent the cause-effect relations among SG variables. It consists of 26 nodes represent the SG variables, and 48 branches represent the cause effect relations among this variables. For each fault the values of measured nodes are coming from sensors and the values of unmeasured nodes are coming from the trained neural networks. These values of the nodes are compared by normal values to get the sign of the nodes. The cause-effect graph for each fault is constructed from the steam generator signed directed graph by removing the invalid (normal) nodes and inconsistent branches. Then in the cause-effect graph we search about the node which does not have an input branch. This node is the fault origin node. The result of this work demonstrated that this method can be used in nuclear power plant fault diagnosis. The advantages of this method are, it enables us to diagnose a multi fault, it is not restricted by pre-defined faults, and it is fast method. (authors)

  12. Development of a component centered fault monitoring and diagnosis knowledge based system for space power system

    NASA Technical Reports Server (NTRS)

    Lee, S. C.; Lollar, Louis F.

    1988-01-01

    The overall approach currently being taken in the development of AMPERES (Autonomously Managed Power System Extendable Real-time Expert System), a knowledge-based expert system for fault monitoring and diagnosis of space power systems, is discussed. The system architecture, knowledge representation, and fault monitoring and diagnosis strategy are examined. A 'component-centered' approach developed in this project is described. Critical issues requiring further study are identified.

  13. Engine Fault Diagnosis using DTW, MFCC and FFT

    NASA Astrophysics Data System (ADS)

    Singh, Vrijendra; Meena, Narendra

    . In this paper we have used a combination of three algorithms: Dynamic time warping (DTW) and the coefficients of Mel frequency Cepstrum (MFC) and Fast Fourier Transformation (FFT) for classifying various engine faults. Dynamic time warping and MFCC (Mel Frequency Cepstral Coefficients), FFT are used usually for automatic speech recognition purposes. This paper introduces DTW algorithm and the coefficients extracted from Mel Frequency Cepstrum, FFT for automatic fault detection and identification (FDI) of internal combustion engines for the first time. The objective of the current work was to develop a new intelligent system that should be able to predict the possible fault in a running engine at different-different workshops. We are doing this first time. Basically we took different-different samples of Engine fault and applied these algorithms, extracted features from it and used Fuzzy Rule Base approach for fault Classification.

  14. Incipient multiple fault diagnosis in real time with applications to large-scale systems

    SciTech Connect

    Chung, H.Y.; Bien, Z.; Park, J.H.; Seon, P.H. . Dept. of Electrical Engineering)

    1994-08-01

    By using a modified signed directed graph (SDG) together with the distributed artificial neutral networks and a knowledge-based system, a method of incipient multi-fault diagnosis is presented for large-scale physical systems with complex pipes and instrumentations such as valves, actuators, sensors, and controllers. The proposed method is designed so as to (1) make a real-time incipient fault diagnosis possible for large-scale systems, (2) perform the fault diagnosis not only in the steady-state case but also in the transient case as well by using a concept of fault propagation time, which is newly adopted in the SDG model, (3) provide with highly reliable diagnosis results and explanation capability of faults diagnosed as in an expert system, and (4) diagnose the pipe damage such as leaking, break, or throttling. This method is applied for diagnosis of a pressurizer in the Kori Nuclear Power Plant (NPP) unit 2 in Korea under a transient condition, and its result is reported to show satisfactory performance of the method for the incipient multi-fault diagnosis of such a large-scale system in a real-time manner.

  15. 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.

  16. Actuator fault diagnosis and fault-tolerant control: Application to the quadruple-tank process

    NASA Astrophysics Data System (ADS)

    Buciakowski, Mariusz; de Rozprza-Faygel, Micha?; Ocha?ek, Joanna; Witczak, Marcin

    2014-12-01

    The paper focuses on an important problem related to the modern control systems, which is the robust fault-tolerant control. In particular, the problem is oriented towards a practical application to quadruple-tank process. The proposed approach starts with a general description of the system and fault-tolerant strategy, which is composed of a suitably integrated fault estimator and robust controller. The subsequent part of the paper is concerned with the design of robust controller as well as the proposed fault-tolerant control scheme. To confirm the effectiveness of the proposed approach, the final part of the paper presents experimental results for considered the quadruple-tank process.

  17. Fault diagnosis and temperature sensor recovery for an air-handling unit

    SciTech Connect

    Lee, W.Y.; Shin, D.R.; House, J.M.

    1997-12-31

    The presence of faults and the influence they have on system operation is a real concern in the heating, ventilating, and air-conditioning (HVAC) community. A fault can be defined as an inadmissible or unacceptable property of a system or a component. Unless corrected, faults can lead to increased energy use, shorter equipment life, and uncomfortable and/or unhealthy conditions for building occupants. This paper describes the use of a two-stage artificial neural network for fault diagnosis in a simulated air-handling unit. The stage one neural network is trained to identify the subsystem in which a fault occurs. The stage two neural network is trained to diagnose the specific cause of a fault at the subsystem level. Regression equations for the supply and mixed-air temperatures are obtained from simulation data and are used to compute input parameters to the neutral networks. Simulation results are presented that demonstrate that, after a successful diagnosis of a supply air temperature sensor fault, the recovered estimate of the supply air temperature obtained from the regression equation can be used in a feedback control loop to bring the supply air temperature back to the setpoint value. Results are also presented that illustrate the evolution of the diagnosis of the two-stage artificial neural network from normal operation to various fault modes of operation.

  18. 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.

  19. 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

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

    PubMed Central

    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

  1. 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.

  2. On-line fault diagnosis of power substation using connectionist expert system

    SciTech Connect

    Yang, H.T.; Chang, W.Y.; Huang, C.L.

    1995-02-01

    This paper proposes a new connectionist (or neural network) expert system for on-line fault diagnosis of a power substation. The Connectionist Expert Diagnosis System has similar profile of an expert system, but can be constructed much more easily from elemental samples. These samples associate the faults with their protective relays and breakers as well as the bus voltages and feeder currents. Through an elaborately designed structure, alarm signals are processed by different connectionist models. The output of the connectionist models is then integrated to provide the final conclusion with a confidence level. The proposed approach has been practically verified by testing on a typical Taiwan Power (Taipower) secondary substation. The test results show that rapid and exactly correct diagnosis is obtained even for the fault conditions involving multiple faults or failure operation of protective relay and circuit breaker. Moreover, the system can be transplanted into various substations with little additional implementation effort.

  3. 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.

  4. 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 cant 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

  5. Fault diagnosis for micro-gas turbine engine sensors via wavelet entropy.

    PubMed

    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. A wavelet decomposition analysis of vibration signal for bearing fault detection

    NASA Astrophysics Data System (ADS)

    Nizwan, C. K. E.; Ong, S. A.; Yusof, M. F. M.; Baharom, M. Z.

    2013-12-01

    This paper presents a study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data was acquired from three different types of bearing defect i.e. corroded, outer race defect and point defect. The experiments were carried out at three different speeds which are 10%, 50% and 90% of the maximum motor speed. The time domain vibration data measured from accelerometer was then transformed into frequency domain using a frequency analyzer in order to study the frequency characteristics of the signal. The DWT was utilized to decomposed signal at different frequency scale. Then, root mean square (RMS) for every decomposition level was calculated to detect the defect features in vibration signals by referring to the trend of vibrational energy retention at every decomposition. Based on the result, the defective bearings show significant deviation in retaining RMS value after a few levels of decomposition. The findings indicate that Wavelet decomposition analysis can be used to develop an effective bearing condition monitoring tool. This signal processing analysis is recommended in on-line monitoring while the machine is on operation.

  8. An expert system for fault section diagnosis of power systems using fuzzy relations

    SciTech Connect

    Cho, H.J.; Park, J.K.

    1997-02-01

    This paper proposes an expert system using fuzzy relations to deal with uncertainties imposed on fault section diagnosis of power systems. The authors build sagittal diagrams which represent the fuzzy relations for power systems, and diagnose fault sections using the sagittal diagrams. Next, they examine the malfunction or wrong alarm of relays and circuit breakers based on the alarm information and the estimated fault section. The proposed system provides the fault section candidates in terms of the degree of membership and the malfunction or wrong alarm. An operator monitors these candidates and is able to diagnose the fault section, coping with uncertainties. Experimental studies for real power systems reveal usefulness of the proposed technique to diagnose faults that have uncertainty.

  9. 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

  10. 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

  11. 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

  12. 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

  13. Simplified interval observer scheme: a new approach for fault diagnosis in instruments.

    PubMed

    Martínez-Sibaja, Albino; Astorga-Zaragoza, Carlos M; Alvarado-Lassman, Alejandro; Posada-Gómez, Rubén; Aguila-Rodríguez, Gerardo; Rodríguez-Jarquin, José P; Adam-Medina, Manuel

    2011-01-01

    There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of fault diagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors. PMID:22346593

  14. Simplified Interval Observer Scheme: A New Approach for Fault Diagnosis in Instruments

    PubMed Central

    Martínez-Sibaja, Albino; Astorga-Zaragoza, Carlos M.; Alvarado-Lassman, Alejandro; Posada-Gómez, Rubén; Aguila-Rodríguez, Gerardo; Rodríguez-Jarquin, José P.; Adam-Medina, Manuel

    2011-01-01

    There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of fault diagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors. PMID:22346593

  15. 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.

  16. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    PubMed

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. PMID:26626623

  17. 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.

  18. 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.

  19. A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery.

    PubMed

    Liu, Zhiwen; He, Zhengjia; Guo, Wei; Tang, Zhangchun

    2016-03-01

    In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method. PMID:26753616

  20. Fault diagnosis and accommodation of a three-tank system based on analytical redundancy.

    PubMed

    Theilliol, Didier; Noura, Hassan; Ponsart, Jean-Christophe

    2002-07-01

    This paper investigates the application of a fault diagnosis and accommodation method to a real system composed of three tanks. The performance of a closed-loop system can be altered by the occurrence of faults which can, in some circumstances, cause serious damage on the system. The research goal is to prevent the system deterioration by developing a controller that has some capabilities to compensate for faults, that is, the fault accommodation or fault-tolerant control. In this paper, a two-step scheme composed of a fault detection, isolation and estimation module, and a control compensation module is presented. The main contribution is to develop a unique structured residual generator able to isolate and estimate both sensor and actuator faults. This estimation is of paramount importance to compensate for these faults and to preserve the system performances. The application of this method to the three-tank system gives encouraging results which are presented and commented on in case of various kinds of faults. PMID:12160349

  1. 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.

  2. An evaluation of a real-time fault diagnosis expert system for aircraft applications

    NASA Technical Reports Server (NTRS)

    Schutte, Paul C.; Abbott, Kathy H.; Palmer, Michael T.; Ricks, Wendell R.

    1987-01-01

    A fault monitoring and diagnosis expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in fault monitoring, fault diagnosis, and recovery planning. The present implementation of this concept performs monitoring and diagnosis for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.

  3. Developing a new transformer fault diagnosis system through evolutionary fuzzy logic

    SciTech Connect

    Huang, Y.C.; Huang, C.L.; Yang, H.T.

    1997-04-01

    To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and their actual fault types, the proposed EP-based development technique is then employed to automatically modify the fuzzy if-then rules and simultaneously adjust the corresponding membership functions. In comparison to results of the conventional DGA and the artificial neural networks (ANN) classification methods, the proposed method has been verified to possess superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.

  4. Implementation of a research prototype onboard fault monitoring and diagnosis system

    NASA Technical Reports Server (NTRS)

    Palmer, Michael T.; Abbott, Kathy H.; Schutte, Paul C.; Ricks, Wendell R.

    1987-01-01

    Due to the dynamic and complex nature of in-flight fault monitoring and diagnosis, a research effort was undertaken at NASA Langley Research Center to investigate the application of artificial intelligence techniques for improved situational awareness. Under this research effort, concepts were developed and a software architecture was designed to address the complexities of onboard monitoring and diagnosis. This paper describes the implementation of these concepts in a computer program called FaultFinder. The implementation of the monitoring, diagnosis, and interface functions as separate modules is discussed, as well as the blackboard designed for the communication of these modules. Some related issues concerning the future installation of FaultFinder in an aircraft are also discussed.

  5. A Diagnosis method of the small end fault on reciprocating compressor connecting rod

    NASA Astrophysics Data System (ADS)

    Jiang, Zhinong; Mao, Zhiwei; Yao, Ziyun; Zhang, Jinjie

    2015-08-01

    The connecting rod is the key moving part of a reciprocating compressor, of which the stress state is extremely complicate and the wear fault of the small end is always a bottleneck problem in the field of fault monitoring and diagnosing. This paper is aimed to present a new method to diagnose the above wear fault. Firstly, a contact model of a clearance in the revolute joint of the small end of a connecting rod bearing (SECRB) was established and a multi-body simulation tool was utilized to simulate the slider-crank mechanism with a clearance, from which the dynamic influence of wear gap in SECRB of a slider-crank mechanism was obtained. Based on the study above, we extracted the characteristics of the wear fault of SECRB and then proposed a brand new approach to monitoring and diagnosing this wear fault by analyzing the angle domain of vibration signals. The availability was verified by conducting an experiment on a reciprocating compressor. And the experimental results show that this method can not only accurately diagnose the wear fault of SECRB but also approximately estimate its severity. This study laid a foundation for the online monitoring and early warning of this fault.

  6. Study and Application of Acoustic Emission Testing in Fault Diagnosis of Low-Speed Heavy-Duty Gears

    PubMed Central

    Gao, Lixin; Zai, Fenlou; Su, Shanbin; Wang, Huaqing; Chen, Peng; Liu, Limei

    2011-01-01

    Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the faults of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the fault diagnosis of on-spot low-speed heavy-duty gears and could be a significant supplement to vibration testing diagnosis. PMID:22346592

  7. 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.

  8. Investigation of candidate data structures and search algorithms to support a knowledge based fault diagnosis system

    NASA Technical Reports Server (NTRS)

    Bosworth, Edward L., Jr.

    1987-01-01

    The focus of this research is the investigation of data structures and associated search algorithms for automated fault diagnosis of complex systems such as the Hubble Space Telescope. Such data structures and algorithms will form the basis of a more sophisticated Knowledge Based Fault Diagnosis System. As a part of the research, several prototypes were written in VAXLISP and implemented on one of the VAX-11/780's at the Marshall Space Flight Center. This report describes and gives the rationale for both the data structures and algorithms selected. A brief discussion of a user interface is also included.

  9. 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.

  10. 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.

  11. Physically-based modeling of speed sensors for fault diagnosis and fault tolerant control in wind turbines

    NASA Astrophysics Data System (ADS)

    Weber, Wolfgang; Jungjohann, Jonas; Schulte, Horst

    2014-12-01

    In this paper, a generic physically-based modeling framework for encoder type speed sensors is derived. The consideration takes into account the nominal fault-free and two most relevant fault cases. The advantage of this approach is a reconstruction of the output waveforms in dependence of the internal physical parameter changes which enables a more accurate diagnosis and identification of faulty incremental encoders i.a. in wind turbines. The objectives are to describe the effect of the tilt and eccentric of the encoder disk on the digital output signals and the influence of the accuracy of the speed measurement in wind turbines. Simulation results show the applicability and effectiveness of the proposed approach.

  12. Spectrum auto-correlation analysis and its application to fault diagnosis of rolling element bearings

    NASA Astrophysics Data System (ADS)

    Ming, A. B.; Qin, Z. Y.; Zhang, W.; Chu, F. L.

    2013-12-01

    Bearing failure is one of the most common reasons of machine breakdowns and accidents. Therefore, the fault diagnosis of rolling element bearings is of great significance to the safe and efficient operation of machines owing to its fault indication and accident prevention capability in engineering applications. Based on the orthogonal projection theory, a novel method is proposed to extract the fault characteristic frequency for the incipient fault diagnosis of rolling element bearings in this paper. With the capability of exposing the oscillation frequency of the signal energy, the proposed method is a generalized form of the squared envelope analysis and named as spectral auto-correlation analysis (SACA). Meanwhile, the SACA is a simplified form of the cyclostationary analysis as well and can be iteratively carried out in applications. Simulations and experiments are used to evaluate the efficiency of the proposed method. Comparing the results of SACA, the traditional envelope analysis and the squared envelope analysis, it is found that the result of SACA is more legible due to the more prominent harmonic amplitudes of the fault characteristic frequency and that the SACA with the proper iteration will further enhance the fault features.

  13. Development and implementation of a power system fault diagnosis expert system

    SciTech Connect

    Minakawa, T.; Ichikawa, Y.; Kunugi, M.; Wada, N.; Shimada, K.; Utsunomiya, M.

    1995-05-01

    This paper describes a fault diagnosis expert system installed at the tohoku Electric Power Company. The main features of this system are careful selection of the inferencing input data, rapid inferencing, integration of the expert system with other systems in a practical structure, and the adoption of a domain shell. This system aims for improved practicability by using time-tagged data from circuit breakers, protective relays, and automatic reclosing relays in addition to the input data used in earlier systems. Furthermore, this system also uses data from fault detection systems that locate fault points within electric stations. This system uses an AI-specific back-end processor to perform inferencing rapidly. Additionally, this fault diagnosis expert system is interfaced and integrated with a restorative operations expert system, an intelligent alarm processing system, and a protective relay setting and management system. Authors developed and adopted a power system fault diagnosis domain shell to ease system development, and used the protective relay operation simulation function of a protective relay setting and management system for system verification.

  14. Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis

    NASA Astrophysics Data System (ADS)

    Liang, B.; Iwnicki, S. D.; Zhao, Y.

    2013-08-01

    The power spectrum is defined as the square of the magnitude of the Fourier transform (FT) of a signal. The advantage of FT analysis is that it allows the decomposition of a signal into individual periodic frequency components and establishes the relative intensity of each component. It is the most commonly used signal processing technique today. If the same principle is applied for the detection of periodicity components in a Fourier spectrum, the process is called the cepstrum analysis. Cepstrum analysis is a very useful tool for detection families of harmonics with uniform spacing or the families of sidebands commonly found in gearbox, bearing and engine vibration fault spectra. Higher order spectra (HOS) (also known as polyspectra) consist of higher order moment of spectra which are able to detect non-linear interactions between frequency components. For HOS, the most commonly used is the bispectrum. The bispectrum is the third-order frequency domain measure, which contains information that standard power spectral analysis techniques cannot provide. It is well known that neural networks can represent complex non-linear relationships, and therefore they are extremely useful for fault identification and classification. This paper presents an application of power spectrum, cepstrum, bispectrum and neural network for fault pattern extraction of induction motors. The potential for using the power spectrum, cepstrum, bispectrum and neural network as a means for differentiating between healthy and faulty induction motor operation is examined. A series of experiments is done and the advantages and disadvantages between them are discussed. It has been found that a combination of power spectrum, cepstrum and bispectrum plus neural network analyses could be a very useful tool for condition monitoring and fault diagnosis of induction motors.

  15. Effectiveness of MED for Fault Diagnosis in Roller Bearings

    NASA Astrophysics Data System (ADS)

    Pennacchi, P.; Ricci, Roberto; Chatterton, S.; Borghesani, P.

    Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.

  16. 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.

  17. 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.

  18. 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.

  19. Fault diagnosis using noise modeling and a new artificial immune system based algorithm

    NASA Astrophysics Data System (ADS)

    Abbasi, Farshid; Mojtahedi, Alireza; Ettefagh, Mir Mohammad

    2015-12-01

    A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

  20. The fault diagnosis of large-scale wind turbine based on expert system

    NASA Astrophysics Data System (ADS)

    Chen, Changzheng; Li, Yun

    2011-10-01

    The wind turbine is the critical equipment for wind power, due to the poor working environment and the long running, the wind turbine components will have a variety of failures. Planned maintenance which has long been used is unable to understand the operational status of equipment comprehensively and timely in a way, especially for large wind machine, the repair work took too long time and cause serious damage. Therefore, fault diagnosis and predictive maintenance becomes more imminent. In this paper, the fault symptoms and corresponding reason of the large-scale wind turbine parts are analyzed and summarized ,such as gear box, generator, yaw system, and so on . And on this basis, the large-scale wind turbine fault diagnosis expert system was constructed by using expert system tool CLIPS and Visual C + +.

  1. 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.

  2. Sliding Mode Approaches for Robust Control, State Estimation, Secure Communication, and Fault Diagnosis in Nuclear Systems

    NASA Astrophysics Data System (ADS)

    Ablay, Gunyaz

    Using traditional control methods for controller design, parameter estimation and fault diagnosis may lead to poor results with nuclear systems in practice because of approximations and uncertainties in the system models used, possibly resulting in unexpected plant unavailability. This experience has led to an interest in development of robust control, estimation and fault diagnosis methods. One particularly robust approach is the sliding mode control methodology. Sliding mode approaches have been of great interest and importance in industry and engineering in the recent decades due to their potential for producing economic, safe and reliable designs. In order to utilize these advantages, sliding mode approaches are implemented for robust control, state estimation, secure communication and fault diagnosis in nuclear plant systems. In addition, a sliding mode output observer is developed for fault diagnosis in dynamical systems. To validate the effectiveness of the methodologies, several nuclear plant system models are considered for applications, including point reactor kinetics, xenon concentration dynamics, an uncertain pressurizer model, a U-tube steam generator model and a coupled nonlinear nuclear reactor model.

  3. Theory of reliable systems. [reliability analysis and on-line fault diagnosis

    NASA Technical Reports Server (NTRS)

    Meyer, J. F.

    1974-01-01

    Research is reported in the program to refine the current notion of system reliability by identifying and investigating attributes of a system which are important to reliability considerations, and to develop techniques which facilitate analysis of system reliability. Reliability analysis, and on-line fault diagnosis are discussed.

  4. An Annotated Selective Bibliography on Human Performance in Fault Diagnosis Tasks. Technical Report 435. Final Report.

    ERIC Educational Resources Information Center

    Johnson, William B.; And Others

    This annotated bibliography developed in connection with an ongoing investigation of the use of computer simulations for fault diagnosis training cites 61 published works taken predominantly from the disciplines of engineering, psychology, and education. A review of the existing literature included computer searches of the past ten years of

  5. 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.

  6. 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.

  7. Boolean modeling and fault diagnosis in oxidative stress response

    PubMed Central

    2012-01-01

    Background Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean network model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI3-Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean network model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer. Results In this paper, we have developed a Boolean network model for the oxidative stress response. This model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI3k/Akt pathway. Conclusions It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological network with feedback will need to be further simplified before it can be even considered for practical biological validation. PMID:23134720

  8. 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).

  9. 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.

  10. Fault diagnosis of the polypropylene production process (UNIPOL PP) using ANFIS.

    PubMed

    Lau, C K; Heng, Y S; Hussain, M A; Mohamad Nor, M I

    2010-10-01

    The performance of a chemical process plant can gradually degrade due to deterioration of the process equipment and unpermitted deviation of the characteristic variables of the system. Hence, advanced supervision is required for early detection, isolation and correction of abnormal conditions. This work presents the use of an adaptive neuro-fuzzy inference system (ANFIS) for online fault diagnosis of a gas-phase polypropylene production process with emphasis on fast and accurate diagnosis, multiple fault identification and adaptability. The most influential inputs are selected from the raw measured data sets and fed to multiple ANFIS classifiers to identify faults occurring in the process, eliminating the requirement of a detailed process model. Simulation results illustrated that the proposed method effectively diagnosed different fault types and severities, and that it has a better performance compared to a conventional multivariate statistical approach based on principal component analysis (PCA). The proposed method is shown to be simple to apply, robust to measurement noise and able to rapidly discriminate between multiple faults occurring simultaneously. This method is applicable for plant-wide monitoring and can serve as an early warning system to identify process upsets that could threaten the process operation ahead of time. PMID:20667537

  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. 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.

  13. Data-based hybrid tension estimation and fault diagnosis of cold rolling continuous annealing processes.

    PubMed

    Liu, Qiang; Chai, Tianyou; Wang, Hong; Qin, Si-Zhao Joe

    2011-12-01

    The continuous annealing process line (CAPL) of cold rolling is an important unit to improve the mechanical properties of steel strips in steel making. In continuous annealing processes, strip tension is an important factor, which indicates whether the line operates steadily. Abnormal tension profile distribution along the production line can lead to strip break and roll slippage. Therefore, it is essential to estimate the whole tension profile in order to prevent the occurrence of faults. However, in real annealing processes, only a limited number of strip tension sensors are installed along the machine direction. Since the effects of strip temperature, gas flow, bearing friction, strip inertia, and roll eccentricity can lead to nonlinear tension dynamics, it is difficult to apply the first-principles induced model to estimate the tension profile distribution. In this paper, a novel data-based hybrid tension estimation and fault diagnosis method is proposed to estimate the unmeasured tension between two neighboring rolls. The main model is established by an observer-based method using a limited number of measured tensions, speeds, and currents of each roll, where the tension error compensation model is designed by applying neural networks principal component regression. The corresponding tension fault diagnosis method is designed using the estimated tensions. Finally, the proposed tension estimation and fault diagnosis method was applied to a real CAPL in a steel-making company, demonstrating the effectiveness of the proposed method. PMID:21954208

  14. Dense framelets with two generators and their application in mechanical fault diagnosis

    NASA Astrophysics Data System (ADS)

    Qin, Yi; Wang, Jiaxu; Mao, Yongfang

    2013-11-01

    Wavelet analysis has been widely applied to mechanical fault diagnosis. Aiming at the problems of current wavelet basis, such as low time-frequency sampling, asymmetry and poor shift-invariance, this paper develops a new family of dense framelets with two generators and some desirable properties. To perform the corresponding framelet transform, three-channel iterated filterbank should be used, where the first and the third channel is decimated while the second channel is undecimated. This arrangement is very helpful for extracting the fault feature of the mid and low frequency band signal components and obtaining some symmetric framelets. To obtain framelets with high symmetry and a specified number of vanishing moments, B-spline and maximally flat linear FIR filter are, respectively, used to design filterbank. Three symmetric framelets and one framelets with symmetric low-pass filter and high-pass filter are constructed. Compared with the higher density framelets and orthonormal wavelets, the proposed framelets have better shift-invariance and denoising performance. Finally, the proposed framelets are applied to fault diagnosis of two gearboxes. The results show that the proposed framelets can be effectively applied to mechanical fault diagnosis and is superior to other commonly-used framelets/wavelets.

  15. Development of the Task-Based Expert System for Machine Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    Bo, Ma; Zhi-nong, Jiang; Zhong-qing, Wei

    2012-05-01

    The operating mechanism of expert systems widely used in fault diagnosis is to formulate a set of diagnostic rules, according to the mechanism and symptoms of faults, in order to instruct the fault diagnosis or directly give diagnostic results. In practice, due to differences existing in such aspects as production technology, drivers, etc., a certain fault may derive from different causes, which will lead to a lower diagnostic accuracy of expert systems. Besides, a variety of expert systems now available have a dual problem of low generality and low expandability, of which the former can lead to the repeated development of expert systems for different machines, while the latter restricts users from expanding the system. Aimed at these problems, a type of task-based software architecture of expert system is proposed in this paper, which permits a specific optimization based on a set of common rules, and allows users to add or modify rules on a man-machine dialog so as to keep on absorbing and improving the expert knowledge. Finally, the integration of the expert system with the condition monitoring system to implement the automatic and semi-automatic diagnosis is introduced.

  16. 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.

  17. 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 fusion method that exploits temporal data correlations over time. We solve this NP-hard optimization problem via a Lagrangian relaxation (variational) technique. The third step optimizes the classifier parameters, viz., probabilities of detection and false alarm, using a genetic algorithm. The proposed algorithm is demonstrated by computing the diagnostic performance metrics on a twin-spool commercial jet engine, an automotive engine, and UCI datasets (problems with high classification error are specifically chosen for experimentation). We show that the primal-dual optimization framework performed consistently better than any traditional fusion technique, even when it is forced to give a single fault decision across a range of classification problems. Secondly, we implement the inference algorithms to diagnose faults in vehicle systems that are controlled by a network of electronic control units (ECUs). The faults, originating from various interactions and especially between hardware and software, are particularly challenging to address. Our basic strategy is to divide the fault universe of such cyber-physical systems in a hierarchical manner, and monitor the critical variables/signals that have impact at different levels of interactions. The proposed diagnostic strategy is validated on an electrical power generation and storage system (EPGS) controlled by two ECUs in an environment with CANoe/MATLAB co-simulation. Eleven faults are injected with the failures originating in actuator hardware, sensor, controller hardware and software components. Diagnostic matrix is established to represent the relationship between the faults and the test outcomes (also known as fault signatures) via simulations. The results show that the proposed diagnostic strategy is effective in addressing the interaction-caused faults.

  18. 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.

  19. 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.

  20. A data-driven multiplicative fault diagnosis approach for automation processes.

    PubMed

    Hao, Haiyang; Zhang, Kai; Ding, Steven X; Chen, Zhiwen; Lei, Yaguo

    2014-09-01

    This paper presents a new data-driven method for diagnosing multiplicative key performance degradation in automation processes. Different from the well-established additive fault diagnosis approaches, the proposed method aims at identifying those low-level components which increase the variability of process variables and cause performance degradation. Based on process data, features of multiplicative fault are extracted. To identify the root cause, the impact of fault on each process variable is evaluated in the sense of contribution to performance degradation. Then, a numerical example is used to illustrate the functionalities of the method and Monte-Carlo simulation is performed to demonstrate the effectiveness from the statistical viewpoint. Finally, to show the practical applicability, a case study on the Tennessee Eastman process is presented. PMID:24434125

  1. 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.

  2. Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

    NASA Astrophysics Data System (ADS)

    Wang, Yingmin; Zhang, Fujun; Cui, Tao; Zhou, Jinlong

    2016-03-01

    Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can't be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015-0.017 5 and sample error is controlled within 0-0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed: complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show: intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis; the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.

  3. 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

  4. 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

  5. An observer based approach for achieving fault diagnosis and fault tolerant control of systems modeled as hybrid Petri nets.

    PubMed

    Renganathan, K; Bhaskar, VidhyaCharan

    2011-07-01

    In this paper, we propose an approach for achieving detection and identification of faults, and provide fault tolerant control for systems that are modeled using timed hybrid Petri nets. For this purpose, an observer based technique is adopted which is useful in detection of faults, such as sensor faults, actuator faults, signal conditioning faults, etc. The concepts of estimation, reachability and diagnosability have been considered for analyzing faulty behaviors, and based on the detected faults, different schemes are proposed for achieving fault tolerant control using optimization techniques. These concepts are applied to a typical three tank system and numerical results are obtained. PMID:21507399

  6. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    NASA Astrophysics Data System (ADS)

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate "possibly" non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines, (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines, (c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

  7. 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.

  8. 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

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

    PubMed Central

    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

  10. Classification techniques for fault detection and diagnosis of an air-handling unit

    SciTech Connect

    House, J.M.; Lee, W.Y.; Shin, D.R.

    1999-07-01

    The objective of this study is to demonstrate the application of several classification techniques to the problem of detecting and diagnosing faults in data generated by a variable-air-volume air-handling unit simulation model and to describe the strengths and weaknesses of the techniques considered. Artificial neural network classifiers, nearest neighbor classifiers, nearest prototype classifiers, a rule-based classifier, and a Bayes classifier are considered for both fault detection and diagnostics. Based on the performance of the classification techniques, the Bayes classifier appears to be a good choice for fault detection. It is a straightforward method that requires limited memory and computational effort, and it consistently yielded the lowest percentage of incorrect diagnosis. For fault diagnosis, the rule-based method is favored for classification problems such as the one considered here, where the various classes of faulty operation are well separated and can be distinguished by a single dominant symptom or feature. Results also indicate that the success or failure of classification techniques hinges to a large degree on an ability to separate different classes of operation in some feature (temperature, pressure, etc.) space. Hence, preprocessing of data to extract dominant features is as important as the selection of the classifier.

  11. Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, M.; Hu, N. Q.; Qin, G. J.

    2011-07-01

    In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

  12. 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.

  13. [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

  14. Fault Diagnosis System of Wind Turbine Generator Based on Petri Net

    NASA Astrophysics Data System (ADS)

    Zhang, Han

    Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.

  15. An architecture for automated fault diagnosis. [Space Station Module/Power Management And Distribution

    NASA Technical Reports Server (NTRS)

    Ashworth, Barry R.

    1989-01-01

    A description is given of the SSM/PMAD power system automation testbed, which was developed using a systems engineering approach. The architecture includes a knowledge-based system and has been successfully used in power system management and fault diagnosis. Architectural issues which effect overall system activities and performance are examined. The knowledge-based system is discussed along with its associated automation implications, and interfaces throughout the system are presented.

  16. A novel method for feature extraction using crossover characteristics of nonlinear data and its application to fault diagnosis of rotary machinery

    NASA Astrophysics Data System (ADS)

    Lin, Jinshan; Chen, Qian

    2014-10-01

    Defective rotary machinery typically exhibits a complex dynamical behavior, which is hard to analyze. Detrended Fluctuation Analysis (DFA) is a robust tool for uncovering long-range correlations hidden in nonstationary data. By DFA, an original series can be compressed into a fluctuation series, which can well preserve the dynamical characteristics of the original series. Lately, the fluctuation series has been separately analyzed by principal component analysis (PCA) and neural network (NN) for fault diagnosis of rotary machinery. However, the feature parameters extracted by PCA or NN normally lack clear physical meaning. In addition, the execution of PCA or NN usually consumes extra time. Interestingly, the scaling-law curve, by which the relation between the fluctuation function and the time scale can be illustrated graphically in a log-log plot, usually exhibits crossover properties. As a result, this study exploited the interesting crossover properties for fault diagnosis of rotary machinery and proposed a novel method for feature extraction of nonlinear data. The proposed method consists of three parts. Firstly, the vibration data from defective rotary machinery are analyzed by DFA and the resultant scaling-law curve is obtained. Secondly, the crossover points in the scaling-law curve are located and then employed to segment the entire scaling-law curve into several different scaling regions, in each of which a single Hurst exponent can be estimated. Thirdly, the whole or a part of the Hurst exponents are used as feature parameters for describing the conditions of defective rotary machinery. Next, the performance of the proposed method was measured using both real gearbox and rolling bearing vibration data with different fault types and severity. The results indicate that the proposed method can ease the problems mentioned previously and performs well in identifying fault types and severity of rotary machinery.

  17. Sound based induction motor fault diagnosis using Kohonen self-organizing map

    NASA Astrophysics Data System (ADS)

    Germen, Emin; Ba?aran, Murat; Fidan, Mehmet

    2014-05-01

    The induction motors, which have simple structures and design, are the essential elements of the industry. Their long-lasting utilization in critical processes possibly causes unavoidable mechanical and electrical defects that can deteriorate the production. The early diagnosis of the defects in induction motors is crucial in order to avoid interruption of manufacturing. In this work, the mechanical and the electrical faults which can be observed frequently on the induction motors are classified by means of analysis of the acoustic data of squirrel cage induction motors recorded by using several microphones simultaneously since the true nature of propagation of sound around the running motor provides specific clues about the types of the faults. In order to reveal the traces of the faults, multiple microphones are placed in a hemispherical shape around the motor. Correlation and wavelet-based analyses are applied for extracting necessary features from the recorded data. The features obtained from same types of motors with different kind of faults are used for the classification using the Self-Organizing Maps method. As it is described in this paper, highly motivating results are obtained both on the separation of healthy motor and faulty one and on the classification of fault types.

  18. Non-adaptive fault diagnosis for low-degree networks via lightpath probing method

    NASA Astrophysics Data System (ADS)

    Zhang, Hang; Li, Yanhe; Guo, Yili; Zhang, Hanyi; Zheng, Xiaoping

    2008-11-01

    This paper considers the fault-diagnosis problem for low-degree all-optical networks. Failure-detection schemes which subject to topological constraints are proposed. Optical probe signals are sent in parallel along a set of designed lightpaths. The network health state is derived from the results of this end-to-end test (i.e., probe syndromes). The design objectives are to minimize the diagnosis cost which is represented by the number of probes. We obtain a lower bound of the number of the probes to identify any single link failure and implement this scheme in several networks as well.

  19. Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays via RBF neural networks.

    PubMed

    Yi, Qu; Zhan-ming, Li; Er-chao, Li

    2012-11-01

    A new fault detection and diagnosis (FDD) problem via the output probability density functions (PDFs) for non-gausian stochastic distribution systems (SDSs) is investigated. The PDFs can be approximated by radial basis functions (RBFs) neural networks. Different from conventional FDD problems, the measured information for FDD is the output stochastic distributions and the stochastic variables involved are not confined to Gaussian ones. A (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. In this work, a nonlinear adaptive observer-based fault detection and diagnosis algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the fault. Stability and Convergency analysis is performed in fault detection and fault diagnosis analysis for the error dynamic system. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained. PMID:22902083

  20. Fundamental Study on Vibration Diagnosis for High Speed Rotational Machine using Wavelet Transform

    NASA Astrophysics Data System (ADS)

    Kawada, Masatake; Yamada, Koji; Yamashita, Katsuya

    In this paper we presented results of fundamental study to introduce the wavelet transform to vibration diagnosis for high-speed rotational machine such as steam turbine, gas turbine, and generator and so on. It is required to detect and distinguish typical vibration of high-speed rotational machine accurately in order to diagnose the machine. The wavelet transform is used in many fields because it is able to visualize phenomenon in time-frequency domain and to detect the beginning time and the duration of it. We made a model rotor supported with two journal bearings to simulate contact vibration, clearance vibration, and oil whip. The vibration phenomena were measured with vertical and horizontal displacement meters at the rotor and vertical and horizontal accelerometers at the rotor bearing and visualized in the time-frequency domain by the wavelet transform. It is found that the dynamic spectra obtained by the wavelet transform of the vertical and horizontal components of displacement and acceleration signals are different for each vibration phenomenon, therefore, this method is able to distinguish each kind of vibration phenomenon. Each vibration phenomenon can be detected and distinguished at the early stage.

  1. High pressure air compressor valve fault diagnosis using feedforward neural networks

    NASA Astrophysics Data System (ADS)

    James Li, C.; Yu, Xueli

    1995-09-01

    Feedforward neural networks (FNNs) are developed and implemented to classify a four-stage high pressure air compressor into one of the following conditions: baseline, suction or exhaust valve faults. These FNNs are used for the compressor's automatic condition monitoring and fault diagnosis. Measurements of 39 variables are obtained under different baseline conditions and third-stage suction and exhaust valve faults. These variables include pressures and temperatures at all stages, voltage between phase aand phase b, voltage between phase band phase c, total three-phase real power, cooling water flow rate, etc. To reduce the number of variables, the amount of their discriminatory information is quantified by scattering matrices to identify statistical significant ones. Measurements of the selected variables are then used by a fully automatic structural and weight learning algorithm to construct three-layer FNNs to classify the compressor's condition. This learning algorithm requires neither guesses of initial weight values nor number of neurons in the hidden layer of an FNN. It takes an incremental approach in which a hidden neuron is trained by exemplars and then augmented to the existing network. These exemplars are then made orthogonal to the newly identified hidden neuron. They are subsequently used for the training of the next hidden neuron. The betterment continues until a desired accuracy is reached. After the neural networks are established, novel measurements from various conditions that haven't been previously seen by the FNNs are then used to evaluate their ability in fault diagnosis. The trained neural networks provide very accurate diagnosis for suction and discharge valve defects.

  2. 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.

  3. 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.

  4. From Fault-Diagnosis and Performance Recovery of a Controlled System to Chaotic Secure Communication

    NASA Astrophysics Data System (ADS)

    Hsu, Wen-Teng; Tsai, Jason Sheng-Hong; Guo, Fang-Cheng; Guo, Shu-Mei; Shieh, Leang-San

    Chaotic systems are often applied to encryption on secure communication, but they may not provide high-degree security. In order to improve the security of communication, chaotic systems may need to add other secure signals, but this may cause the system to diverge. In this paper, we redesign a communication scheme that could create secure communication with additional secure signals, and the proposed scheme could keep system convergence. First, we introduce the universal state-space adaptive observer-based fault diagnosis/estimator and the high-performance tracker for the sampled-data linear time-varying system with unanticipated decay factors in actuators/system states. Besides, robustness, convergence in the mean, and tracking ability are given in this paper. A residual generation scheme and a mechanism for auto-tuning switched gain is also presented, so that the introduced methodology is applicable for the fault detection and diagnosis (FDD) for actuator and state faults to yield a high tracking performance recovery. The evolutionary programming-based adaptive observer is then applied to the problem of secure communication. Whenever the tracker induces a large control input which might not conform to the input constraint of some physical systems, the proposed modified linear quadratic optimal tracker (LQT) can effectively restrict the control input within the specified constraint interval, under the acceptable tracking performance. The effectiveness of the proposed design methodology is illustrated through tracking control simulation examples.

  5. 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

  6. A modular neural network scheme applied to fault diagnosis in electric power systems.

    PubMed

    Flores, Agustn; Quiles, Eduardo; Garca, Emilio; Morant, Francisco; Correcher, Antonio

    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

  7. A Flight Expert System (FLES) For On-Board Fault Monitoring And Diagnosis

    NASA Astrophysics Data System (ADS)

    Ali, M.; Scharnhorst, D...; Ai, C. S.; Ferber, H. J.

    1986-03-01

    The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher work loads for the pilot and tend to induce a higher probability of errors. Therefore, we have developed an architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing and recovering from in-flight faults. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Lockheed Advanced Concept System (ACS), a future generation airplane, and partly on the Boeing 737, an existing airplane. A distinction between two types of faults, maladjustments and malfunctions, has led us to take two approaches to fault diagnosis. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to assess abnormal situations during airplane flight. This paper describes our reasons for handling malfunctions and maladjustments separately and the use of domain knowledge in the diagnosis of each.

  8. 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.

  9. 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

  10. 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.

  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. A flight expert system (FLES) for on-board fault monitoring and diagnosis

    NASA Technical Reports Server (NTRS)

    Ali, M.; Scharnhorst, D. A.; Ai, C. S.; Ferber, H. J.

    1986-01-01

    The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher work loads for the pilot and tend to induce a higher probability of errors. Therefore, an architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing and recovering from in-flight faults has been developed. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Lockheed Advanced Concept System (ACS), a future generation airplane, and partly on the Boeing 737, an existing airplane. A distinction between two types of faults, maladjustments and malfunctions, has led us to take two approaches to fault diagnosis. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to assess abnormal situations during airplane flight. This paper describes the reasons for handling malfunctions and maladjustments separately and the use of domain knowledge in the diagnosis of each.

  13. Fault Diagnosis in Discrete-Event Systems with Incomplete Models: Learnability and Diagnosability.

    PubMed

    Kwong, Raymond H; Yonge-Mallo, David L

    2015-07-01

    Most model-based approaches to fault diagnosis of discrete-event systems require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system, or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In a previous paper, we addressed the problem of diagnosing faults given an incomplete model of the discrete-event system. We presented the learning diagnoser which not only diagnoses faults, but also attempts to learn missing model information through parsimonious hypothesis generation. In this paper, we study the properties of learnability and diagnosability. Learnability deals with the issue of whether the missing model information can be learned, while diagnosability corresponds to the ability to detect and isolate a fault after it has occurred. We provide conditions under which the learning diagnoser can learn missing model information. We define the notions of weak and strong diagnosability and also give conditions under which they hold. PMID:25204002

  14. Use of fuzzy cause-effect digraph for resolution fault diagnosis for process plants. 2: Diagnostic algorithm and applications

    SciTech Connect

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

    1995-05-01

    A new model graph called the fuzzy cause-effect digraph (FCDG) model was already proposed in part 1, and its capability to eliminate spurious interpretations attributed to system compensation and inverse responses from backward loops and forward paths is to be demonstrated. In this paper the authors attempt to develop a new fault diagnosis algorithm based on the fuzzy cause-effect digraph model. This method applies fuzzy reasoning to estimate the states of unmeasured variables, to explain fault propagation paths, and to locate fault origins. In particular, it can obtain the fault origin occurring in the process with single and multiple loops at the early stage of fault. This study uses a CSTR as an example to explicate this diagnosis method and compares the results with those of other methods.

  15. 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.

  16. Wireless power transfer and fault diagnosis of high-voltage power line via robotic bird

    NASA Astrophysics Data System (ADS)

    Liu, Chunhua; Chau, K. T.; Zhang, Zhen; Qiu, Chun; Li, Wenlong; Ching, T. W.

    2015-05-01

    This paper presents a new idea of wireless power transfer (WPT) and fault diagnosis (FD) of high-voltage power line via robotic bird. The key is to present the conceptual robotic bird with WPT coupling coil for detecting and capturing the energy from the high-voltage power line. If the power line works in normal condition, the robotic bird is able to stand on the power line and extract energy from it. If fault occurs on the power line, the corresponding magnetic field distribution will become different from that in the normal situation. By analyzing the magnetic field distribution of the power line, the WPT to the robotic bird and the FD by the robotic bird are performed and verified.

  17. 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

  18. Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

    NASA Astrophysics Data System (ADS)

    Wang, Wen-Jing; Cui, Ling-Li; Chen, Dao-Yun

    2015-11-01

    Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains. One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment. In this work, we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum (MPS) through a multi-scale morphology analysis procedure. The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves. Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.

  19. 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.

  20. 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.

  1. Use Of Communicating Expert Systems In Fault Diagnosis For Space Station Applications

    NASA Astrophysics Data System (ADS)

    Read, J. Y.; Howland, T. P.; Perkins, W. A.

    1987-02-01

    In many complex monitoring and diagnostic applications it is useful to build separate expert systems for different subsystems and have them operate (on different computers) mostly in an independent mode with only occasional interactions. There are many advantages of such a scheme including easier knowledge engineering and increased overall system performance. Using an enhanced version of the Lockheed Expert System (LES), we have developed a communicating expert systems for fault diagnosis and fault correction in a prototype for the Space Station Air Revitalization System (ARS). The system consists of three communicating expert systems, one for oxygen generation, one for CO2 removal and a supervisor for overall control. The three expert system modules communicate via mailboxes. The purpose of this work is to gain an understanding of the problems involved and advantages of using such a communicating expert systems framework.

  2. 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

  3. An embedded intelligent monitoring system for rotating machiney vibrations

    NASA Astrophysics Data System (ADS)

    Wang, Li; Han, Qingkai; Zhang, Mo; Zhang, Tianxia; Wen, Bangchun

    2007-12-01

    As required of multi-level and network based fault diagnosis system for large-scale rotating machinery, a new kind of embedded intelligent local set is introduced in the paper for machine condition monitoring and fundamental diagnosis. Its functions include high-speed acquisition of vibration signals, time-frequency analyses, ANN-based fault diagnosis and remote communications. The experimental results show that it performs with higher efficiency with real time, reliability and accuracy in data acquisition, vibration monitoring and typical fault diagnosis.

  4. 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.

  5. 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.

  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. Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms

    NASA Astrophysics Data System (ADS)

    Sanz, Javier; Perera, Ricardo; Huerta, Consuelo

    2007-05-01

    This paper presents a new technique for monitoring the condition of rotating machinery from vibration analyses. The proposed method combines the capability of wavelet transform (WT) to treat transient signals with the ability of auto-associative neural networks to extract features of data sets in an unsupervised mode. Trained and configured networks with WT coefficients of nonfaulty signals are used as a method to detect the novelties or anomalies of faulty signals. The effectiveness of the proposed technique is evaluated using the numerical data and experimental vibration data of a gearbox. Despite the fact that noise is present in both cases, results demonstrated that the proposed method is a good candidate to be used as an online diagnosis tool for rotating machinery.

  8. A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

    PubMed Central

    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. Advanced fault diagnosis for the mass production of small-power electric motors

    NASA Astrophysics Data System (ADS)

    Filbert, Dieter

    1993-09-01

    High quality is a principal goal in the mass production of electric niotors (i.e. d.c. motors for cars and universal motors for house hold appliances).The processing of vibration and acoustical signals are widely used in quality assurance in the mass production but the coupling of the sensors to the motor as well as noise produced in the environment make it still difficult to get reproducible diagnostic results. High quality in production can be achieved by the powerful modern diagnostic methods which became possible because of the progress in microelectronics (microprocessors and signal processors). This progress made mathematical methods and signal processing applicable. Therefore this paper deals with diagnostic methods that use the measured signals of voltage, current and speed only but achieve a good testing. It gives an overview of new methods for the feature extraction and fault detection on small power electric motors.

  10. 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.

  11. 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.

  12. Hard competitive growing neural network for the diagnosis of small bearing faults

    NASA Astrophysics Data System (ADS)

    Barakat, M.; El Badaoui, M.; Guillet, F.

    2013-05-01

    A hard competitive growing neural network (HC-GNN) with shrinkage learning is put forward to detect and diagnose small bearing faults. Structure determination based on supervised learning is an important issue in pattern classification. For that reason, the proposed approach introduces new hidden units whenever necessary and adjusts their shapes to minimize the risk of misclassification. This leads to smaller networks compared to classical radial basis functions or probabilistic neural networks and therefore enables the use of large data sets with satisfactory classification accuracy. This technique is based on the following concepts: (1) growing architecture, (2) dynamic adaptive learning, (3), convergence by means of several criteria, (4) embedded weighted feature selection, and (5) optimized network structure. HC-GNN consists of two main stages and runs in an iterative way. The first stage learns weighted selected parameters to well-known classes while the second stage associates the testing parameters of unknown samples to the learned classes. This approach is applied on a machinery system with different small bearing faults at various speeds and loads. The challenge is to detect and diagnose these faults regardless of the motor's shaft speed. Obtained results are analyzed, explained and compared with various techniques that have been widely investigated in diagnosis area.

  13. 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.

  14. 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

  15. 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

  16. A flight expert system (FLES) for on-board fault monitoring and diagnosis

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Scharnhorst, D. A.; Ai, C. S.; Feber, H. J.

    1987-01-01

    The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher workloads for the pilot and tend to induce a higher probability of errors. Therefore, an architecture for a flight expert system (FLES) is developed to assist pilots in monitoring, diagnosing and recovering from in-flight faults. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with airplane status information during the diagnostic process. The simulator is based on the Lockheed Advanced Concept System (ACS), a future generation airplane, and on the Boeing 737. A distinction between two types of faults, maladjustments and malfunctions, has led to two approaches to fault diagnosis. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to access abnormal situations during airplane flight. Malfunctions and maladjustments are handled separately, diagnosed using domain knowledge.

  17. Adaptive neural network/expert system that learns fault diagnosis for different structures

    NASA Astrophysics Data System (ADS)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  18. 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.

  19. 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.

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

    DOEpatents

    Reifman, Jaques; Wei, Thomas Y. C.

    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.

  1. 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

  2. Fault diagnosis using a diagnostic shell and its verification results by connecting to an operator training simulator

    SciTech Connect

    Kobayashi, T.; Moridera, D.; Komai, K.; Fukui, S.; Matsumoto, K.

    1995-02-01

    This paper describes a fault diagnostic system using a diagnostic shell, MELDASH, and results that confirm its effectiveness. The diagnostic shell that reflects and makes use of the nature of model-based diagnosis is developed to overcome the drawbacks of methods that depend on operator knowledge. A high-performance fault diagnostic system is constructed simply by adding an application model to the diagnostic shell. A prototype system is verified by connecting it to an operator training simulator. It is able to make a proper diagnosis in 79 difficult fault cases. Verification results shows that the prototype system has sufficient accuracy. The authors confirm the effectiveness of this fault diagnostic method for future energy management systems.

  3. 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

  4. Fault diagnosis and fault-tolerant finite control set-model predictive control of a multiphase voltage-source inverter supplying BLDC motor.

    PubMed

    Salehifar, Mehdi; Moreno-Equilaz, Manuel

    2016-01-01

    Due to its fault tolerance, a multiphase brushless direct current (BLDC) motor can meet high reliability demand for application in electric vehicles. The voltage-source inverter (VSI) supplying the motor is subjected to open circuit faults. Therefore, it is necessary to design a fault-tolerant (FT) control algorithm with an embedded fault diagnosis (FD) block. In this paper, finite control set-model predictive control (FCS-MPC) is developed to implement the fault-tolerant control algorithm of a five-phase BLDC motor. The developed control method is fast, simple, and flexible. A FD method based on available information from the control block is proposed; this method is simple, robust to common transients in motor and able to localize multiple open circuit faults. The proposed FD and FT control algorithm are embedded in a five-phase BLDC motor drive. In order to validate the theory presented, simulation and experimental results are conducted on a five-phase two-level VSI supplying a five-phase BLDC motor. PMID:26549566

  5. Usefulness of high-resolution thermography in fault diagnosis of fluid power components and systems

    NASA Astrophysics Data System (ADS)

    Pietola, Matti; Varrio, Jukka P.

    1996-03-01

    Infrared thermography has been used routinely in industrial applications for quite a long time. For example, the condition of electric power lines, district heating networks, electric circuits and components, heat exchangers, pipes and its insulations, cooling towers, and various machines and motors is monitored using infrared imaging techniques. Also the usage of this technology in predictive maintenance has proved successful, mainly because of effective computers and tailored softwares available. However, the usage of thermal sensing technique in fluid power systems and components (or other automation systems in fact) is not as common. One apparent reason is that a fluid power circuit is not (and nor is a hydraulic component) an easy object of making thermal image analyses. Especially the high flow speed, fast pressure changes and fast movements make the diagnosis complex and difficult. Also the number of people whose knowledge is good both in thermography and fluid power systems is not significant. In this paper a preliminary study of how thermography could be used in the condition monitoring, fault diagnosis and predictive maintenance of fluid power components and systems is presented. The shortages and limitations of thermal imaging in the condition monitoring of fluid power are also discussed. Among many other cases the following is discussed: (1) pressure valves (leakage, wrong settings), (2) check valves (leakage); (3) cylinders (leakage and other damages); (4) directional valves and valve assemblies; (5) pumps and motors (leakage in piston or control plate, bearings). The biggest advantage of using thermography in the predictive maintenance and fault diagnosis of fluid power components and systems could be achieved in the process industry and perhaps in the commissioning of fluid power systems in the industry. In the industry the predictive maintenance of fluid power with the aid of an infrared camera could be done as part of a condition monitoring of other systems, for instance bearings.

  6. Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform

    NASA Astrophysics Data System (ADS)

    Ebrahimi, Bashir Mahdi; Faiz, Jawad; Lotfi-fard, S.; Pillay, P.

    2012-07-01

    This paper introduces novel indices for broken rotor bars diagnosis in three-phase induction motors based on wavelet coefficients of stator current in a specific frequency band. These indices enable to diagnose occurrence and determine number of broken bars in different loads precisely. Besides thanks to the suitability of wavelet transform in transient conditions, it is possible to detect the fault during the start-up of the motor. This is important in the case of start-up of large induction motors with long starting time and also motors with frequent start-up. Furthermore, broken rotor bars in induction motor are detected using spectra analysis of the stator current. It is also shown that rise of number of broken bars and load levels increases amplitude of the particular side-band components of the stator currents in the faulty case. An induction motor with 1, 2, 3 and 4 broken bars at the rated load and the motor with 4 broken bars at no-load, 33%, 66%, 100% and 133% rated load are investigated. Time stepping finite element method is used for modeling broken rotor bars faults in induction motors. In this modeling, effects of the stator winding distribution, stator and rotor slots, geometrical and physical characteristics of different parts of the motor and non-linearity of the core materials are taken into account. The simulation results are are verified by the experimental results.

  7. 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

  8. 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

  9. 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

  10. 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.

  11. A Fuzzy Reasoning Design for Fault Detection and Diagnosis of a Computer-Controlled System

    PubMed Central

    Ting, Y.; Lu, W.B.; Chen, C.H.; Wang, G.K.

    2008-01-01

    A Fuzzy Reasoning and Verification Petri Nets (FRVPNs) model is established for an error detection and diagnosis mechanism (EDDM) applied to a complex fault-tolerant PC-controlled system. The inference accuracy can be improved through the hierarchical design of a two-level fuzzy rule decision tree (FRDT) and a Petri nets (PNs) technique to transform the fuzzy rule into the FRVPNs model. Several simulation examples of the assumed failure events were carried out by using the FRVPNs and the Mamdani fuzzy method with MATLAB tools. The reasoning performance of the developed FRVPNs was verified by comparing the inference outcome to that of the Mamdani method. Both methods result in the same conclusions. Thus, the present study demonstratrates that the proposed FRVPNs model is able to achieve the purpose of reasoning, and furthermore, determining of the failure event of the monitored application program. PMID:19255619

  12. 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.

  13. Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis.

    PubMed

    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

  14. 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

  15. Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis

    NASA Astrophysics Data System (ADS)

    Wang, Dong; Tse, Peter W.; Guo, Wei; Miao, Qiang

    2011-02-01

    A novel method for enhancing gearbox fault diagnosis and prognosis is developed by fusion of multiple health indicators through support vector data description. First, the Comblet transform is used to identify gear residual error signals from the raw signal. Second, based on the observation of gear residual error signals, a total of 11 gear health indicators are identified, and are categorized into two types of indicators. The first and second types of indicators are for fault diagnosis and prognosis, respectively. The first type has six indicators, which are sensitive to impulsive signals triggered by anomalous impacts. The second type has five indicators, which are suitable for tracking degradation of faults. Third, through the support vector data description, the first six health indicators are fused into type one indicators for fault diagnosis. The remaining five indicators are fused into type two indicators for fault prognosis. Finally, a Gaussian kernel is designed to enhance the performance of type one and two indicators by optimal range of width size. The effectiveness of the proposed method is validated through experiments. The new method has been proven to be superior to methods that use unfused indicators individually.

  16. Application to induction motor faults diagnosis of the amplitude recovery method combined with FFT

    NASA Astrophysics Data System (ADS)

    Liu, Yukun; Guo, Liwei; Wang, Qixiang; An, Guoqing; Guo, Ming; Lian, Hao

    2010-11-01

    This paper presents a signal processing method - amplitude recovery method (abbreviated to ARM) - that can be used as the signal pre-processing for fast Fourier transform (FFT) in order to analyze the spectrum of the other-order harmonics rather than the fundamental frequency in stator currents and diagnose subtle faults in induction motors. In this situation, the ARM functions as a filter that can filter out the component of the fundamental frequency from three phases of stator currents of the induction motor. The filtering result of the ARM can be provided to FFT to do further spectrum analysis. In this way, the amplitudes of other-order frequencies can be extracted and analyzed independently. If the FFT is used without the ARM pre-processing and the components of other-order frequencies, compared to the fundamental frequency, are fainter, the amplitudes of other-order frequencies are not able easily to extract out from stator currents. The reason is when the FFT is used direct to analyze the original signal, all the frequencies in the spectrum analysis of original stator current signal have the same weight. The ARM is capable of separating the other-order part in stator currents from the fundamental-order part. Compared to the existent digital filters, the ARM has the benefits, including its stop-band narrow enough just to stop the fundamental frequency, its simple operations of algebra and trigonometry without any integration, and its deduction direct from mathematics equations without any artificial adjustment. The ARM can be also used by itself as a coarse-grained diagnosis of faults in induction motors when they are working. These features can be applied to monitor and diagnose the subtle faults in induction motors to guard them from some damages when they are in operation. The diagnosis application of ARM combined with FFT is also displayed in this paper with the experimented induction motor. The test results verify the rationality and feasibility of the ARM. It should be clarified that the ARM must be applied in three phases of currents in electrical machines. For a single phase of alternating current or direct current, it can do nothing. However, since three-phase electrical machines have a dominant position in the application field in modern economic society and it is natural and convenient to acquire three phases of stator currents during the three-phase electrical machines are tested, it is necessary and meaningful to develop the ARM to diagnose and guard them.

  17. Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

    PubMed

    Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe

    2016-03-01

    The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. PMID:26838675

  18. Serological tests for diagnosis and staging of hand-arm vibration syndrome (HAVS).

    PubMed

    Kao, Dennis S; Yan, Ji-Geng; Zhang, Lin-Ling; Kaplan, Rachel E; Riley, Danny A; Matloub, Hani S

    2008-06-01

    The current gold standard for the diagnosis and staging of hand-arm vibration syndrome (HAVS) is the Stockholm workshop scale, which is subjective and relies on the patient's recalling ability and honesty. Therefore, great potentials exist for diagnostic and staging errors. The purpose of this study is to determine if objective serum tests, such as levels of soluble thrombomodulin (sTM) and soluble intercellular adhesion molecule-1 (sICAM-1), may be used in the diagnosis and staging of HAVS. Twenty two nonsmokers were divided into a control group (n = 11) and a vibration group (n = 11). The control group included subjects without history of frequent vibrating tool use. The vibration group included construction workers with average vibrating tool use of 12.2 years. All were classified according to the Stockholm workshop scale (SN, sensorineural symptoms; V, vascular symptoms. SN0, no numbness; SN1, intermittent numbness; SN2, reduced sensory perception; SN3, reduced tactile discrimination; V0, no vasospasmic attacks; V1, intermittent vasospasm involving distal phalanges; V2, intermittent vasospasm extending to middle phalanges; V3, intermittent vasospasm extending to proximal phalanges; V4, skin atrophy/necrosis). All control subjects were SN0 V0. Seven out of 11 vibration subjects were SN1 V1, and 4 out of 11 were SN1 V2. A 10-cm(3) sample of venous blood was collected from each subject. The sTM and sICAM-1 levels were determined by enzyme-linked immunosorbent assay. The mean plasma sTM levels were as follows: control group = 2.93 +/- 0.47 ng/ml, and vibration group = 3.61 +/- 0.24 ng/ml. The mean plasma sICAM-1 levels were as follows: control group = 218.8 +/- 54.1 ng/ml, and vibration group = 300.3 +/- 53.2 ng/ml. The sTM and sICAM-1 differences between control and vibration groups were statistically significant (p < 0.0002 and p < 0.001, respectively). When reference ranges provided by Hemostasis Reference Lab were used as cut-off values, all sTM and sICAM-1 levels were within range, except three vibration individuals (27%) who had sICAM-1 levels greater than the reference range. This was not statistically significant (p = 0.08). When subjects were compared based on the Stockholm workshop scale, mean plasma sTM levels were SN0 V0 group = 2.93 +/- 0.47 ng/ml, SN1 V1 group = 3.59 +/- 0.25 ng/ml, and SN1 V2 group = 3.65 +/- 0.27 ng/ml, and mean plasma sICAM-1 levels were SN0 V0 = 219 +/- 54.1 ng/ml, SN1 V1 = 275 +/- 33.5 ng/ml, and SN1 V2 = 345 +/- 54.6 ng/ml. The difference in sTM level among the three groups was statistically significant (p < 0.001). The difference in sICAM-1 level among the three groups was also statistically significant (p < 0.002). The sTM and sICAM-1 levels are statistically higher in subjects with HAVS, with levels proportional to the disease severity. However, large population studies are needed to determine the "real-life" standard reference ranges for sTM and sICAM-1. PMID:18780088

  19. 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.

  20. 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.

  1. 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

  2. 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%.

  3. Real-Time Condition Monitoring and Fault Diagnosis of Gear Train Systems Using Instantaneous Angular Speed (IAS) Analysis

    NASA Astrophysics Data System (ADS)

    Sait, Abdulrahman S.

    This dissertation presents a reliable technique for monitoring the condition of rotating machinery by applying instantaneous angular speed (IAS) analysis. A new analysis of the effects of changes in the orientation of the line of action and the pressure angle of the resultant force acting on gear tooth profile of spur gear under different levels of tooth damage is utilized. The analysis and experimental work discussed in this dissertation provide a clear understating of the effects of damage on the IAS by analyzing the digital signals output of rotary incremental optical encoder. A comprehensive literature review of state of the knowledge in condition monitoring and fault diagnostics of rotating machinery, including gearbox system is presented. Progress and new developments over the past 30 years in failure detection techniques of rotating machinery including engines, bearings and gearboxes are thoroughly reviewed. This work is limited to the analysis of a gear train system with gear tooth surface faults utilizing angular motion analysis technique. Angular motion data were acquired using an incremental optical encoder. Results are compared to a vibration-based technique. The vibration data were acquired using an accelerometer. The signals were obtained and analyzed in the phase domains using signal averaging to determine the existence and position of faults on the gear train system. Forces between the mating teeth surfaces are analyzed and simulated to validate the influence of the presence of damage on the pressure angle and the IAS. National Instruments hardware is used and NI LabVIEW software code is developed for real-time, online condition monitoring systems and fault detection techniques. The sensitivity of optical encoders to gear fault detection techniques is experimentally investigated by applying IAS analysis under different gear damage levels and different operating conditions. A reliable methodology is developed for selecting appropriate testing/operating conditions of a rotating system to generate an alarm system for damage detection.

  4. 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.

  5. 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.

  6. 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

  7. Envelope calculation of the multi-component signal and its application to the deterministic component cancellation in bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Ming, A. B.; Zhang, W.; Qin, Z. Y.; Chu, F. L.

    2015-01-01

    Commonly presented as cyclic impulse responses with some degrees of randomness, the vibrations induced by bearing faults are multi-component signals and usually overwhelmed by other deterministic components, which may degrade the efficiency of the traditional envelope analysis used for bearing fault feature extraction. In this paper, the envelope of the multi-component signal, including both discrete frequency components and cyclic impulse responses, is theoretically calculated by the Hilbert transform in both time and frequency domains at first. Then, a novel deterministic component cancellation method is proposed based on the iterative calculation of the signal envelope. Finally, simulations and experiments are used to validate the theoretical calculation and the proposed deterministic component cancellation method. It is indicated that the oscillation part of the envelope is dominated by the cross-terms of the multi-component signal, and that the cross-terms between a discrete frequency component and cyclic impulse responses present as new cyclic impulse responses, which retain the cyclic feature of the original ones. Furthermore, the deterministic component can be canceled by iteratively subtracting the direct current (DC) offset of the envelope. Compared with the cepstrum pre-whiten (CPW) method, used to separate the deterministic (discrete frequency) component from the random component (vibration induced by the bearing fault), the proposed method is more efficient to the shifting of the cyclic impulse responses from the powerful deterministic component with little disruption, and is more suitable for the real time signal processing owing to the high efficient calculation of the Hilbert transform.

  8. Prototype fault-diagnosis system for NASA space station power management and control. Master's thesis

    SciTech Connect

    Hester, G.L.

    1988-09-01

    The Power Management and Distribution System (PMAD) Prototype utilizes a computer graphics interface with a computer expert system running transparent to the user and a computer communications interface that links the two together, all enabling the diagnosis of PMAD system faults. The prototype design is based on the concept that an astronaut on a space station will instruct an expert system through a graphic interface to run a system or component check on the PMAD system. The graphics interface determines which type of evaluations was requested and sends that information through the communications interface to the expert system. The expert system receives the information and, based on the type of evaluation requested, executes the appropriate rules in the knowledge base and sends the resulting status back to the graphics interface and the astronaut. The PMAD System Prototype serves as a proposed training tool for NASA to use in the training of new personnel who will be designing and developing the NASA Space station expert systems.

  9. Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion

    NASA Astrophysics Data System (ADS)

    Wang, Yuefei; Xue, Chuang; Jia, Xiaohan; Peng, Xueyuan

    2015-05-01

    This paper proposes a method of diagnosing faults in reciprocating compressor valves using the acoustic emission signal coupled with the simulated valve motion. The actual working condition of a valve can be obtained by analyzing the acoustic emission signal in the crank angle domain and the valve movement can be predicted by simulating the valve motion. The exact opening and closing locations of a normal valve, provided by the simulated valve motion, can be used as references for the valve fault diagnosis. The typical valve faults are diagnosed to validate the feasibility and accuracy of the proposed method. The experimental results indicate that this method can easily distinguish the normal valve, valve flutter and valve delayed closing conditions. The characteristic locations of the opening and closing of the suction and discharge valves can be clearly identified in the waveform of the acoustic emission signal and the simulated valve motion.

  10. 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.

  11. 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.

  12. Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal

    NASA Astrophysics Data System (ADS)

    Janjarasjitt, S.; Ocak, H.; Loparo, K. A.

    2008-10-01

    In this paper, we introduce a modified form of the correlation integral developed by Grassberger and Procaccia referred to as the partial correlation integral, which can be computed in real time. The partial correlation integral algorithm is then used to analyze machine vibration data obtained throughout a life test of a rolling element bearing. From the experimental results, the dimensional exponent (an approximation of the correlation dimension) as computed from the partial correlation integral algorithm tends to increase as time progresses and the useful remaining life of the bearing is decreasing. The dimensional exponents of a healthy bearing and a bearing close to failure are statistically different. We also propose a computational scheme for bearing condition monitoring (diagnosis and prognosis) using the dimensional exponent integrated with a surrogate data testing technique. As a result, we can characterize the condition of the bearing from the results of the surrogate data test and furthermore, we provide some preliminary evidence that the dimensional exponent can be used to predict the failure of rolling element bearings in rotating machinery from real-time vibration data.

  13. Identification of significant intrinsic mode functions for the diagnosis of induction motor fault.

    PubMed

    Cho, Sangjin; Shahriar, Md Rifat; Chong, Uipil

    2014-08-01

    For the analysis of non-stationary signals generated by a non-linear process like fault of an induction motor, empirical mode decomposition (EMD) is the best choice as it decomposes the signal into its natural oscillatory modes known as intrinsic mode functions (IMFs). However, some of these oscillatory modes obtained from a fault signal are not significant as they do not bear any fault signature and can cause misclassification of the fault instance. To solve this issue, a novel IMF selection algorithm is proposed in this work. PMID:25096149

  14. 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.

  15. Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

    PubMed Central

    Şimşir, Mehmet; Bayır, Raif; Uyaroğlu, Yılmaz

    2016-01-01

    Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured. PMID:26819590

  16. Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network.

    PubMed

    ?im?ir, Mehmet; Bay?r, Raif; Uyaro?lu, Y?lmaz

    2016-01-01

    Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured. PMID:26819590

  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. 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).

  19. 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.

  20. [Study on fault diagnosis of power-shift steering transmission based on spectrometric analysis and SVM].

    PubMed

    Zhang, Ying-Feng; Ma, Biao; Zhang, Jin-Le; Chen, Man; Fan, Yu-Heng; Li, Wen-Chang

    2010-06-01

    Spectrometric oil analysis is an important method to study the running state of Power-Shift Steering Transmission (PSST). A method of multiple out least squares support vector regression was developed using spectrometric oil analysis data and SVM (Support Vector Machine). The spectrometric oil analysis data were studied using multiple out least squares support vector regression. It has been proved that the regression data are good in approximation effect for No. 1 PSST. And the predictive values for No. 2 PSST are highly veracious with the test data. The fault information was found and the fault position was determined through compar4tive analysis. This method has been proved to have practice significance for finding fault-hidden dangers and judging fault positions. PMID:20707155

  1. 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.

  2. 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

  3. Optimal design of the absolute positioning sensor for a high-speed maglev train and research on its fault diagnosis.

    PubMed

    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

  4. Diagnosis of helicopter gearboxes using structure-based networks

    NASA Technical Reports Server (NTRS)

    Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

    1995-01-01

    A connectionist network is introduced for fault diagnosis of helicopter gearboxes that incorporates knowledge of the gearbox structure and characteristics of the vibration features as its fuzzy weights. Diagnosis is performed by propagating the abnormal features of vibration measurements through this Structure-Based Connectionist Network (SBCN), the outputs of which represent the fault possibility values for individual components of the gearbox. The performance of this network is evaluated by applying it to experimental vibration data from an OH-58A helicopter gearbox. The diagnostic results indicate that the network performance is comparable to those obtained from supervised pattern classification.

  5. 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

  6. 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

  7. Airdata sensor based position estimation and fault diagnosis in aerial refueling

    NASA Astrophysics Data System (ADS)

    Sevil, Hakki Erhan

    Aerial refueling is the process of transferring fuel from one aircraft (the tanker) to another (the receiver) during flight. In aerial refueling operations, the receiver aircraft is exposed to nonuniform wind field induced by tanker aircraft, and this nonuniform wind field leads to differences in readings of airdata sensors placed at different locations on the receiver aircraft. There are advantages and disadvantages of this phenomenon. As an advantage, it is used as a mechanism to estimate relative position of the receiver aircraft inside the nonuniform wind field behind the tanker. Using the difference in the measurements from multiple identical sensors, a model of the nonuniform wind field that is organized as maps of the airspeed, side slip angle and angle of attack as functions of the relative position is prepared. Then, using the developed algorithms, preformed maps and instant sensor readings, the relative position receiver aircraft is determined. The disadvantage of the phenomenon is that the differences in readings of airdata sensors cause false fault detections in a redundant-sensor-based Fault Detection and Isolation (FDI) system developed based on the assumption of identical sensor readings from three airdata sensors. Such FDI algorithm successfully performs detection and isolation of sensor faults when the receiver aircraft flies solo or outside the wake of the tanker aircraft. However, the FDI algorithm yields false fault detection when the receiver aircraft enters the tanker's wake. This problem can be eliminated by modifying the FDI algorithm. For the robustness, the expected values of the sensor measurements are incorporated in the FDI algorithm, instead of the assumption of identical measurements from the sensors. The expected values, which depend on the position of the receiver relative to the tanker, are obtained from the maps of the nonuniform wind field as functions of the relative position. The new robust FDI detects and isolates sensor faults, as well as it eliminates the false fault detection in the nonuniform wind field induced by the tanker aircraft.

  8. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    NASA Astrophysics Data System (ADS)

    Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang

    2016-02-01

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses hidden in vibration signals and performs well for bearing fault diagnosis.

  9. A knowledge-based operator advisor system for integration of fault detection, control, and diagnosis to enhance the safe and reliable operation of nuclear power plants

    SciTech Connect

    Bhatnagar, R.

    1989-01-01

    A Knowledged-Based Operator Advisor System has been developed for enhancing the complex task of maintaining safe and reliable operation of nuclear power plants. The operator's activities have been organized into the four tasks of data interpretation for abstracting high level information from sensor data, plant state monitoring for identification of faults, plan execution for controlling the faults, and diagnosis for determination of root causes of faults. The Operator Advisor System is capable of identifying the abnormal functioning of the plant in terms of: (1) deviations from normality, (2) pre-enumerated abnormal events, and (3) safety threats. The classification of abnormal functioning into the three categories of deviations from normality, abnormal events, and safety threats allows the detection of faults at three levels of: (1) developing faults, (2) developed faults, and (3) safety threatening faults. After the identification of abnormal functioning the system will identify the procedures to be executed to mitigate the consequences of abnormal functioning and will help the operator by displaying the procedure steps and monitoring the success of actions taken. The system also is capable of diagnosing the root causes of abnormal functioning. The identification, and diagnosis of root causes of abnormal functioning are done in parallel to the task of procedure execution, allowing the detection of more critical safety threats while executing procedures to control abnormal events.

  10. 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.

  11. 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.

  12. 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

  13. A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor

    NASA Astrophysics Data System (ADS)

    Shi, Pu; Chen, Zheng; Vagapov, Yuriy; Zouaoui, Zoubir

    2014-01-01

    This paper proposes a new induction motor broken bar fault extent diagnostic approach under varying load conditions based on wavelet coefficients of stator current in a specific frequency band. In this paper, winding function approach (WFA) is used to develop a mathematical model to provide indication references for parameters under different load levels and different fault cases. It is shown that rise of number of broken bars and load levels increases amplitude of the particular side band components of the stator currents in faulty case. Stator current, rotor speed and torque are used to demonstrate the relationship between these parameters and broken rotor bar severity. An induction motor with 1, 2 and 3 broken bars and the motor with 3 broken bars in experiment at no-load, 50% and 100% load are investigated. A novel criterion is then developed to assess rotor fault severity based on the stator current and the rotor speed. Simulations and experimental results confirm the validity of the proposed approach.

  14. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

    NASA Astrophysics Data System (ADS)

    Jegadeeshwaran, R.; Sugumaran, V.

    2015-02-01

    Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

  15. 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.

  16. Tuning and comparing fault diagnosis methods for aeronautical systems via kriging-based optimization

    NASA Astrophysics Data System (ADS)

    Marzat, J.; Piet-Lahanier, H.; Damongeot, F.; Walter, E.

    2013-12-01

    Many approaches address fault detection and isolation (FDI) based on analytical redundancy. To rank them, it is necessary to define performance indices and realistic sets of test cases on which they will be evaluated. For the ranking to be fair, each of the methods under consideration should have its internal parameters tuned optimally. The work presented uses a combination of tools developed in the context of computer experiments to achieve this tuning from a limited number of numerical evaluations. The methodology is then extended so as to provide a robust tuning in the worst-case sense.

  17. Law-based sensor fault diagnosis and validation for building air-conditioning systems

    SciTech Connect

    Wang, S.; Wang, J.B.

    2000-07-01

    This paper presents an automatic strategy that can be used in a building energy management and control system to detect, diagnose, and evaluate soft sensor faults in building air-conditioning systems. The strategy is based on fundamental conservation (mass and energy conservation) relations and accommodates changes of plant performance and working conditions. The existence and magnitude of non-abrupt biases in chilled water flow meters and temperature sensors due to miscalibration and drift are detected. Sensor biases are estimated by minimizing the sum of the squares of the mass and energy balance residuals of selected control volumes. Validation of the strategy on a central chilling system is presented.

  18. Knowledge-based fault monitoring and diagnosis in Space Shuttle propellant loading

    NASA Technical Reports Server (NTRS)

    Scarl, E. A.; Jamieson, J.; Delaune, C.

    1984-01-01

    The LOX Expert System (LES), now being developed as a tool for the constraint-based monitoring and analysis of propellant loading at the Kennedy Space Center (KSC), is discussed. The loading of LOX at the KSC and its control and monitoring by the Launch Processing System are summarized, and the relevant problem for LES is presented. The LES database is briefly described, and the interaction of LES with KNOBS, a constraint- and frame-oriented knowledge-based system developed as a demonstration system in aid of tactical air mission planning, is the context of launch processing is discussed in detail. The design and fault isolation techniques of LES are also discussed.

  19. 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.

  20. Diagnosis of broken-bars fault in induction machines using higher order spectral analysis.

    PubMed

    Saidi, L; Fnaiech, F; Henao, H; Capolino, G-A; Cirrincione, G

    2013-01-01

    Detection and identification of induction machine faults through the stator current signal using higher order spectra analysis is presented. This technique is known as motor current signature analysis (MCSA). This paper proposes two higher order spectra techniques, namely the power spectrum and the slices of bi-spectrum used for the analysis of induction machine stator current leading to the detection of electrical failures within the rotor cage. The method has been tested by using both healthy and broken rotor bars cases for an 18.5 kW-220 V/380 V-50 Hz-2 pair of poles induction motor under different load conditions. Experimental signals have been analyzed highlighting that bi-spectrum results show their superiority in the accurate detection of rotor broken bars. Even when the induction machine is rotating at a low level of shaft load (no-load condition), the rotor fault detection is efficient. We will also demonstrate through the analysis and experimental verification, that our proposed proposed-method has better detection performance in terms of receiver operation characteristics (ROC) curves and precision-recall graph. PMID:22999985

  1. A generic task approach to a real time nuclear power plant fault diagnosis and advisory system

    SciTech Connect

    Hajek, B.K.; Miller, D.W.; Bhatnagar, R.; Stasenko, J.E.; Punch, W.F. III; Yamada, N.

    1988-01-01

    A generic task toolkit developed at The Ohio State University Laboratory for Artificial Intelligence Research (LAIR) has been used in the development of an aid for operators of nuclear power plants. The toolkit consists of high level programming tools that enable knowledge to be used in accordance with its need. That is, if diagnosis is the need, a framework for performing diagnosis is provided. The operator aid provides for monitoring the conditions in the plant, detecting abnormal events, and providing the operator with guidance and advice through procedures on what path should be followed to mitigate the consequences. 8 refs., 5 figs.

  2. Three-dimensional modeling, estimation, and fault diagnosis of spacecraft air contaminants.

    PubMed

    Narayan, A P; Ramirez, W F

    1998-01-01

    A description is given of the design and implementation of a method to track the presence of air contaminants aboard a spacecraft using an accurate physical model and of a procedure that would raise alarms when certain tolerance levels are exceeded. Because our objective is to monitor the contaminants in real time, we make use of a state estimation procedure that filters measurements from a sensor system and arrives at an optimal estimate of the state of the system. The model essentially consists of a convection-diffusion equation in three dimensions, solved implicitly using the principle of operator splitting, and uses a flowfield obtained by the solution of the Navier-Stokes equations for the cabin geometry, assuming steady-state conditions. A novel implicit Kalman filter has been used for fault detection, a procedure that is an efficient way to track the state of the system and that uses the sparse nature of the state transition matrices. PMID:11543186

  3. 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.

  4. Vibration-based condition monitoring of rotating machines using a machine composite spectrum

    NASA Astrophysics Data System (ADS)

    Elbhbah, Keri; Sinha, Jyoti K.

    2013-05-01

    Vibration-based condition monitoring (VCM) requires vibration measurement on each bearing pedestal using a number of vibration transducers and then signals processing for all the measured vibration data to identify fault(s), if any, in a rotating machine. Such a large vibration data set makes the diagnosis process complex generally for a large rotating machine supported through a number of bearing pedestals. Hence a new method is used to construct a single composite spectrum using all the measured vibration data set. This composite spectrum is expected to represent the dynamics of the complete machine assembly and can make fault diagnosis process relatively easier and more straightforward. The paper presents the concept of the proposed composite spectrum which was applied to a laboratory test rig with different simulated faults; healthy and three faulty cases named misalignment, crack shaft, and shaft rub. A comparison between the composite spectrum with and without the coherence has been investigated for the simulated faults in the rig. It has been observed that the coherent composite spectrum provides much better diagnosis compared to the non-coherent composite spectrum.

  5. Frequency Analysis of Vibrations of the Internal Combustion Engine Components in the Diagnosis of Engine Processes

    NASA Astrophysics Data System (ADS)

    Tomaszewski, Franciszek; Szyma?ski, Grzegorz

    2012-03-01

    Many methods of diagnosing internal combustion engines have been already worked out. They can be divided into methods using working processes and methods using leftover processes. Working processes give information about general condition of internal combustion engine. Leftover processes give information about condition of particular subassemblies and kinematic couples; hence they are used as autonomous processes or as processes supporting other diagnostic methods. Methods based on analysis of vibrations and noise changes to determine technical condition of object are named as vibroacoustic diagnostics. In papers about vibroacoustic diagnostics of engine, problems connected with difficulty to select test point and to define diagnostic parameters containing essential information about engine's condition, are most often omitted. Selection of engine's working parameters and conditions of taking measurements or registering vibration signal are usually based on references, researcher's experience or intuition. General assumptions about taking measurements of signal closest to its source are most often used. Application of vibrations and noise generated by working combustion engine to assess correctness of its work and technical condition has many advantages. Vibroacoustic processes are a good carrier of diagnostic information for the following reasons: - high information capacity, - high speed of data transfer (signal's component describing change in object's condition is visible the moment the inefficiency occurs), - vibration signal reflects all significant processes in combustion engine, - measurement of vibrations and noise does not require special preparations of technical object for tests and can be carried out during regular operations. This article presents a new approach to vibroacoustic diagnostics of internal combustion engine. Selection of test points of vibration on the basis of impact tests' results was suggested. Those results were applied to build dynamic models of systems of combustion engines. Such model was used to assess condition of the systems.

  6. 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.

  7. 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.

  8. Experimental investigations on vibration response of misaligned rotors

    NASA Astrophysics Data System (ADS)

    Patel, Tejas H.; Darpe, Ashish K.

    2009-10-01

    Misalignment is one of the most commonly observed faults in rotating machines. However, there have been relatively limited research efforts in the past to understand its effect on overall dynamics of the rotor system. In the existing literature, there is confusing spectral information on the rotor vibration characteristics of misalignment. The present study is aimed at understanding the dynamics of misaligned rotors and reducing the ambiguity so as to improve the reliability of the misalignment fault diagnosis. Influence of misalignment and its type on the forcing characteristics of flexible coupling is investigated followed by experimental investigation of the vibration response of misaligned coupled rotors supported on rolling element bearings. Steady-state vibration response at integer fraction of the first bending natural frequency is investigated. Effects of types of misalignments, i.e. parallel and angular misalignments, are investigated. The conventional Fourier spectrum (i.e. FFT) has limitations in revealing the directional nature of the vibrations arising out of rotor faults. In addition, it has been observed that several other rotor faults generate higher harmonics in the Fourier spectrum and hence there could be a level of uncertainty in the diagnosis when other faults are also suspect. The present work through use of full spectra has shown possibility of diagnosing misalignment through unique vibration features exhibited in the full spectra (i.e. forward/backward whirl). This provides an important tool to separate faults that generate similar frequency spectra (e.g. crack and misalignment) and lead to a more reliable misalignment diagnosis. Full spectra and orbit plots are efficiently used to reveal the unique nature of misalignment fault not clearly brought out by the previous studies, and new misalignment diagnostics recommendations are proposed.

  9. Study on open equipment condition monitoring and fault diagnosis system based on Internet

    NASA Astrophysics Data System (ADS)

    He, Hui-Long; Wang, Tai-Yong; Deng, Hui; Zeng, Ju-Xiang; Wang, Guo-Feng; Rao, Jun

    2005-12-01

    An open condition-monitoring system combined C/S (Client / Server) with B/S (Brower / Server) pattern was introduced. It consists of three parts of software: SE (Server-Terminal), ADE ( Analysis-Diagnosis-Terminal) and DAE ( Data-Acquisition-Terminal ). SE can monitor every connection request from ADE or DAE user and determinate whether ADE or DAE can be run on client's PC. As a result, the system security is improved in a sense. Additionally, the system hardware part comprises two kinds of terminal portable instruments: DASOC(data-acquisition system on chip based-on the Cygnal MCU) and DSAI(dynamic signal analysis instrument based on an embedded OS:XPE). DASOC and DSAI can realize data-acquisition, signal analysis and data-transmission based-on internet. The system structure mode has been applied to a certain power plant's enterprise information network in Tianjin. Results show that the system is successful.

  10. 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.

  11. Integrated knowledge-based system for the process/product design and fault diagnosis of plastics recycling facilities

    SciTech Connect

    DiRaddo, R.W.; Pecora, L.; Lim, B.; Salloum, G.

    1995-11-01

    Plastics constitute approximately 8% by weight of a typical municipal solid waste stream. The reprocessing of plastic materials, subsequent to consumer use, is a complex problem requiring several processing stages, such as size reduction, sorting and separation, washing and drying, and finally reforming into a final part. The design of a plastics recycling facility is a difficult task that depends on the desired final product and the feedstock being considered. Once a plant becomes operational, processing problems will always be present, due primarily to the varying composition of the input stream. It is also important to address environmental safety, process and quality control issues. Also, optimal product design is critical with regard to final product performance and appropriate market application. With this in mind, an expert system technology has been developed in order to assist users in the process/product design and fault diagnosis of plastics recycling facilities. The technology can also be employed for optimal value-added product design. A modular approach is undertaken, so that the integration of the individual process units can be optimized to handle various waste stream compositions and desired final product specifications. An expert system is employed, with a built-in search routine, and the ability to incorporate new information. Sources for the information include several knowledge support specialists.

  12. Data acquisition for a real time fault monitoring and diagnosis knowledge-based system for space power system

    NASA Technical Reports Server (NTRS)

    Wilhite, Larry D.; Lee, S. C.; Lollar, Louis F.

    1989-01-01

    The design and implementation of the real-time data acquisition and processing system employed in the AMPERES project is described, including effective data structures for efficient storage and flexible manipulation of the data by the knowledge-based system (KBS), the interprocess communication mechanism required between the data acquisition system and the KBS, and the appropriate data acquisition protocols for collecting data from the sensors. Sensor data are categorized as critical or noncritical data on the basis of the inherent frequencies of the signals and the diagnostic requirements reflected in their values. The critical data set contains 30 analog values and 42 digital values and is collected every 10 ms. The noncritical data set contains 240 analog values and is collected every second. The collected critical and noncritical data are stored in separate circular buffers. Buffers are created in shared memory to enable other processes, i.e., the fault monitoring and diagnosis process and the user interface process, to freely access the data sets.

  13. Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction

    NASA Astrophysics Data System (ADS)

    Fan, Wei; Cai, Gaigai; Zhu, Z. K.; Shen, Changqing; Huang, Weiguo; Shang, Li

    2015-05-01

    Vibration signals from a defective gearbox are often associated with important measurement information useful for gearbox fault diagnosis. The extraction of transient features from the vibration signals has always been a key issue for detecting the localized fault. In this paper, a new transient feature extraction technique is proposed for gearbox fault diagnosis based on sparse representation in wavelet basis. With the proposed method, both the impulse time and the period of transients can be effectively identified, and thus the transient features can be extracted. The effectiveness of the proposed method is verified by the simulated signals as well as the practical gearbox vibration signals. Comparison study shows that the proposed method outperforms empirical mode decomposition (EMD) in transient feature extraction.

  14. A Feature Extraction Method for Fault Classification of Rolling Bearing based on PCA

    NASA Astrophysics Data System (ADS)

    Wang, Fengtao; Sun, Jian; Yan, Dawen; Zhang, Shenghua; Cui, Liming; Xu, Yong

    2015-07-01

    This paper discusses the fault feature selection using principal component analysis (PCA) for bearing faults classification. Multiple features selected from the time-frequency domain parameters of vibration signals are analyzed. First, calculate the time domain statistical features, such as root mean square and kurtosis; meanwhile, by Fourier transformation and Hilbert transformation, the frequency statistical features are extracted from the frequency spectrum. Then the PCA is used to reduce the dimension of feature vectors drawn from raw vibration signals, which can improve real time performance and accuracy of the fault diagnosis. Finally, a fuzzy C-means (FCM) model is established to implement the diagnosis of rolling bearing faults. Practical rolling bearing experiment data is used to verify the effectiveness of the proposed method.

  15. A fuzzy-based approach for open-transistor fault diagnosis in voltage-source inverter induction motor drives

    NASA Astrophysics Data System (ADS)

    Zhang, Jianghan; Luo, Hui; Zhao, Jin; Wu, Feng

    2015-02-01

    This paper develops a novel method for the detection and isolation of open-transistor faults in voltage-source inverters feeding induction motors. Based on analyzing the load currents trajectories after Concordia transformation, six diagnostic signals each of which indicates a certain switch are extracted and a fuzzy rule base is designed to perform fuzzy reasoning in order to detect and isolate 21 fault modes including single- and double-transistor faults. In addition, the fuzzy rules are rearranged and each of them is set to a reasonable value representing the fault modes. The simulation and experiment are carried out to demonstrate the effectiveness of the proposed fuzzy approach.

  16. 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

  17. Bearing Fault Detection in Induction Motor-Gearbox Drivetrain

    NASA Astrophysics Data System (ADS)

    Cibulka, Jaroslav; Ebbesen, Morten K.; Robbersmyr, Kjell G.

    2012-05-01

    The main contribution in the hereby presented paper is to investigate the fault detection capability of a motor current signature analysis by expanding its scope to include the gearbox, and not only the induction motor. Detecting bearing faults outside the induction motor through the stator current analysis represents an interesting alternative to traditional vibration analysis. Bearing faults cause changes in the stator current spectrum that can be used for fault diagnosis purposes. A time-domain simulation of the drivetrain model is developed. The drivetrain system consists of a loaded single stage gearbox driven by a line-fed induction motor. Three typical bearing faults in the gearbox are addressed, i.e. defects in the outer raceway, the inner raceway, and the rolling element. The interaction with the fault is modelled by means of kinematical and mechanical relations. The fault region is modelled in order to achieve gradual loss and gain of contact. A bearing fault generates an additional torque component that varies at the specific bearing defect frequency. The presented dynamic electromagnetic dq-model of an induction motor is adjusted for diagnostic purpose and considers such torque variations. The bearing fault is detected as a phase modulation of the stator current sine wave at the expected bearing defect frequency.

  18. 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.

  19. Bearing fault diagnosis under variable rotational speed via the joint application of windowed fractal dimension transform and generalized demodulation: A method free from prefiltering and resampling

    NASA Astrophysics Data System (ADS)

    Shi, Juanjuan; Liang, Ming; Guan, Yunpeng

    2016-02-01

    The conventional way for bearing fault diagnosis under variable rotational speed generally includes prefiltering, resampling based on shaft rotating frequency and order spectrum analysis. However, its application is confined by three major obstacles: a) knowledge-demanding parameter determination required by prefiltering, b) unavailable shaft rotating frequency for resampling as it is coupled with instantaneous fault characteristic frequency (IFCF) by a fault characteristic coefficient (FCC) which cannot be decided without knowing what fault actually exists, and c) complicated and error-prone resampling process. As such, we propose a new method to address these problems. The proposed method free from prefiltering and resampling mainly contains the following steps: a) extracting envelope by windowed fractal dimension (FD) transform, requiring no prefiltering, b) with the envelope signal, performing short time Fourier transform (STFT) to get a clear time frequency representation (TFR), from which the IFCF and the basic demodulator for generalized demodulation (GD) can be obtained, c) applying the generalized demodulation to the envelope signal with the current demodulator, converting the trajectory of the current time-frequency component into a linear path parallel to the time axis, d) frequency analyzing the demodulated signal, followed by searching the amplitude of the constant frequency where the linear path is situated. Updating demodulator via multiplying the basic demodulator by different real numbers (i.e., coefficient λ) and repeating the steps (c)-(d), the resampling-free order spectrum is then obtained. Based on the resulting spectrum, the final diagnosis decision can be made. The proposed method for its implementation on the example of simulated data is presented. Finally, experimental data are employed to validate the effectiveness of the proposed technique.

  20. 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.

  1. Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

    PubMed

    Seera, Manjeevan; Lim, Chee Peng; Ishak, Dahaman; Singh, Harapajan

    2012-01-01

    In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors. PMID:24808459

  2. Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

    PubMed

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. PMID:22399894

  3. Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

    PubMed Central

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. PMID:22399894

  4. Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox

    NASA Astrophysics Data System (ADS)

    Cai, Gaigai; Chen, Xuefeng; He, Zhengjia

    2013-12-01

    Localized faults in gearboxes tend to result in periodic shocks and thus arouse periodic responses in vibration signals. Feature extraction has always been a key problem for localized fault diagnosis. This paper proposes a new fault feature extraction technique for gearboxes by using sparsity-enabled signal decomposition method. The sparsity-enabled signal decomposition method separates signals based on the oscillatory behavior of the signal rather than the frequency or scale. Thus, the fault feature can be nonlinearly extracted from vibration signals. During the implementation of the proposed method, tunable Q-factor wavelet transform, for which the Q-factor can be easily specified, is adopted to represent vibration signals in a sparse way, and then morphological component analysis (MCA) is employed to estimate and separate the distinct components. The corresponding optimization problem of MCA is solved by the split augmented Lagrangian shrinkage algorithm (SALSA). With the proposed method, vibration signals of the faulty gearbox can be nonlinearly decomposed into high-oscillatory component and low-oscillatory component which is the fault feature of gearboxes. To evaluate the performance of the proposed method, this paper investigates the effect of two parameters pertinent to MCA and SALSA: the Lagrange multiplier and the penalty parameter. The effectiveness of the proposed method is verified by both the simulated and practical gearbox vibration signals. Results show the proposed method outperforms empirical mode decomposition and spectral kurtosis in extracting fault features of gearboxes.

  5. Electrical Motor Current Signal Analysis using a Modulation Signal Bispectrum for the Fault Diagnosis of a Gearbox Downstream

    NASA Astrophysics Data System (ADS)

    Haram, M.; Wang, T.; Gu, F.; Ball, A. D.

    2012-05-01

    Motor current signal analysis has been an effective way for many years of monitoring electrical machines themselves. However, little work has been carried out in using this technique for monitoring their downstream equipment because of difficulties in extracting small fault components in the measured current signals. This paper investigates the characteristics of electrical current signals for monitoring the faults from a downstream gearbox using a modulation signal bispectrum (MSB), including phase effects in extracting small modulating components in a noisy measurement. An analytical study is firstly performed to understand amplitude, frequency and phase characteristics of current signals due to faults. It then explores the performance of MSB analysis in detecting weak modulating components in current signals. Experimental study based on a 10kw two stage gearbox, driven by a three phase induction motor, shows that MSB peaks at different rotational frequencies can be based to quantify the severity of gear tooth breakage and the degrees of shaft misalignment. In addition, the type and location of a fault can be recognized based on the frequency at which the change of MSB peak is the highest among different frequencies.

  6. 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.

  7. 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.

  8. 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.

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

    PubMed Central

    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

  10. 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

  11. Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Yi; Xu, Guanghua; Liang, Lin; Jiang, Kuosheng

    2015-03-01

    The kurtogram-based methods have been proved powerful and practical to detect and characterize transient components in a signal. The basic idea of the kurtogram-based methods is to use the kurtosis as a measure to discover the presence of transient impulse components and to indicate the frequency band where these occur. However, the performance of the kurtogram-based methods is poor due to the low signal-to-noise ratio. As the weak transient signal with a wide spread frequency band can be easily masked by noise. Besides, selecting signal just in one frequency band will leave out some transient features. Aiming at these shortcomings, different frequency bands signal fusion is adopted in this paper. Considering that manifold learning aims at discovering the nonlinear intrinsic structure which embedded in high dimensional data, this paper proposes a waveform feature manifold (WFM) method to extract the weak signature from waveform feature space which obtained by binary wavelet packet transform. Minimum permutation entropy is used to select the optimal parameter in a manifold learning algorithm. A simulated bearing fault signal and two real bearing fault signals are used to validate the improved performance of the proposed method through the comparison with the kurtogram-based methods. The results show that the proposed method outperforms the kurtogram-based methods and is effective in weak signature extraction.

  12. Fault analysis for condition monitoring of induction motors

    NASA Astrophysics Data System (ADS)

    Nandi, Subhasis

    Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down time and improve salability. Prodigious improvement in signal processing hardware and software has made this possible. Primarily, these techniques depend upon locating specific harmonic components in the line current, also known as motor current signature analysis (MCSA). These harmonic components are usually different for different types of faults. However, with multiple faults or different varieties of drive schemes, MCSA can become an onerous task as different types of faults and time harmonics may end up generating similar signatures. Thus, other signals such as speed, torque, noise, vibration, etc., are also explored for their frequency contents. Sometimes, altogether different techniques such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. It is indeed evident that this area is vast in scope. Going by the present trend, human involvement in the actual fault detection decision making is slowly being replaced by automated tools such as expert systems, neural networks, fuzzy logic based systems; to name a few. However, this cannot be achieved without detailed fault analysis and subsequent recognition of the fault pattern. Keeping this in mind, simulation studies of the broken bar and eccentricity related faults using MCSA have been taken up. Also, a common theoretical basis for the different types (static, dynamic and mixed) of eccentricity related faults which give different signatures for different pole and rotor bar combinations has been developed. This will be of great importance both from fault diagnosis as well as sensorless drive applications' viewpoint. Finally, the insight gained from the analysis of eccentricity related faults leads to a novel detection technique of stator inter-turn faults by analyzing the frequency content of the transient line to line voltage, after the motor is switched off.

  13. Envelope order tracking for fault detection in rolling element bearings

    NASA Astrophysics Data System (ADS)

    Guo, Yu; Liu, Ting-Wei; Na, Jing; Fung, Rong-Fong

    2012-12-01

    An envelope order tracking analysis scheme is proposed in the paper for the fault detection of rolling element bearing (REB) under varying-speed running condition. The developed method takes the advantages of order tracking, envelope analysis and spectral kurtosis. The fast kurtogram algorithm is utilized to obtain both optimal center frequency and bandwidth of the band-pass filter based on the maximum spectral kurtosis. The envelope containing vibration features of the incipient REB fault can be extracted adaptively. The envelope is re-sampled by the even-angle sampling scheme, and thus the non-stationary signal in the time domain is represented as a quasi-stationary signal in the angular domain. As a result, the frequency-smear problem can be eliminated in order spectrum and the fault diagnosis of REB in the varying-speed running condition of the rotating machinery is achieved. Experiments are conducted to verify the validity of the proposed method.

  14. 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.

  15. Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization

    NASA Astrophysics Data System (ADS)

    Gao, Huizhong; Liang, Lin; Chen, Xiaoguang; Xu, Guanghua

    2015-01-01

    Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, the time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classify the high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution(TFD) with non-negative matrix factorization(NMF), and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform(STFT). Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network(ANN), NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

  16. 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

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

    PubMed

    Santos, Pedro; Villa, Luisa F; Reones, Anbal; Bustillo, Andres; Maudes, Jess

    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

  18. Study on the precession orbit shape analysis-based linear fault qualitative identification method for rotating machinery

    NASA Astrophysics Data System (ADS)

    Lang, Genfeng; Liao, Yuhe; Liu, Qingcheng; Lin, Jing

    2015-01-01

    The vibration responses of different linear faults all possess some common features, which make fault diagnosis very difficult. Based on the multi-sensor information fusion theory, this paper presents a new qualitative identification method for the diagnosis of linear faults. The excitation-response dynamic equation is constructed and system balancing response with full consideration of system anisotropy is analyzed. Through discussion of the precession orbit shape difference and its dispersive situation, the orbit shape average difference coefficient and the corresponding dispersion term are estimated to obtain the theoretical balancing effect. Finally, the qualitative identification of linear fault can be done according to whether the calculated balancing effect meets the safe operation requirement or not. The dynamic characteristic of the system difference coefficients is verified by a simulation experiment and the case study further testifies the capability and reliability of the proposed method.

  19. 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.

  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

    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. 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.

  3. Enhanced detection of rolling element bearing fault based on stochastic resonance

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaofei; Hu, Niaoqing; Cheng, Zhe; Hu, Lei

    2012-11-01

    Early bearing faults can generate a series of weak impacts. All the influence factors in measurement may degrade the vibration signal. Currently, bearing fault enhanced detection method based on stochastic resonance(SR) is implemented by expensive computation and demands high sampling rate, which requires high quality software and hardware for fault diagnosis. In order to extract bearing characteristic frequencies component, SR normalized scale transform procedures are presented and a circuit module is designed based on parameter-tuning bistable SR. In the simulation test, discrete and analog sinusoidal signals under heavy noise are enhanced by SR normalized scale transform and circuit module respectively. Two bearing fault enhanced detection strategies are proposed. One is realized by pure computation with normalized scale transform for sampled vibration signal, and the other is carried out by designed SR hardware with circuit module for analog vibration signal directly. The first strategy is flexible for discrete signal processing, and the second strategy demands much lower sampling frequency and less computational cost. The application results of the two strategies on bearing inner race fault detection of a test rig show that the local signal to noise ratio of the characteristic components obtained by the proposed methods are enhanced by about 50% compared with the band pass envelope analysis for the bearing with weaker fault. In addition, helicopter transmission bearing fault detection validates the effectiveness of the enhanced detection strategy with hardware. The combination of SR normalized scale transform and circuit module can meet the need of different application fields or conditions, thus providing a practical scheme for enhanced detection of bearing fault.

  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. 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....

  6. Intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization.

    PubMed

    Li, Ke; Chen, Peng

    2011-01-01

    Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called "relative ratio symptom parameters" are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks. PMID:22163833

  7. A frequency-shifted bispectrum for rolling element bearing diagnosis

    NASA Astrophysics Data System (ADS)

    Dong, Guangming; Chen, Jin; Zhao, Fagang

    2015-03-01

    This paper is in the field of vibration based fault diagnosis for rolling element bearings, where one major issue is the ability to detect faults as early as possible. Bispectrum analysis has been applied in bearing diagnosis. In this paper, a frequency-shifted bispectrum is studied for bearing diagnosis. It is found that the frequency-shifted bispectrum is more suitable for analyzing amplitude modulated and frequency modulated signals than the bispectrum, when choosing the carrier frequency as the specific frequency shift value. For bearing vibration signals with amplitude modulation characteristics, in which the carrier frequencies are structural resonant frequencies, one of the structural resonances can be chosen as the specific frequency shift value for the frequency-shifted bispectrum analysis. To increase diagnostic ability of the frequency-shifted bispectrum for bearing faults, firstly, the frequency resolution is increased in the frequency-shifted bispectrum analysis through band pass filtering and frequency zooming; secondly, integration of the frequency-shifted bispectrum along one frequency variable is used to produce a one-dimensional spectral representation. Numerical simulation and experimental results show the feasibility of the proposed method for qualitatively and quantitatively diagnosing bearing faults.

  8. [Cold water immersion test for diagnosis of vibration diseases. Comparison between water at 5 degrees C and 10 degrees C].

    PubMed

    Sakakibara, H; Miyao, M; Kanada, S; Kobayashi, F; Nakagawa, T; Yamada, S

    1982-11-01

    5 degrees C-water 10-minute immersion test, generally used in Japan, is useful to diagnose vibration diseases. But severe pains during the immersion is troublesome. We studied the availability of 10 degrees C-water 10-minute immersion test to reduce the pain during the test. Subjects were forty-nine chainsaw operators, nineteen patients with vibration disease, and twelve controls. The same subject underwent both 5 degrees C and 10 degrees C immersion tests. The following results were obtained. 1) Skin temperatures in the highest score group after the immersion tests both at 5 degrees C and 10 degrees C was lower than that in the control group. Mean skin temperatures for the last five minutes during the immersion and the recovery activity in both the immersion tests showed a similar trend among subjects groups. Skin temperatures in patients under medical treatment (R'group) did not differ from those in the control group. 2) Hyperemia time by nail press test in the R'group and in the high score group after both immersion tests was longer than that in the control group. But this difference between chainsaw operators and the control group after 5 degrees C immersion test was more marked than that after 10 degrees C immersion test. 3) Vibratory sense as well as pain sense in the R'group and in the high score group after both immersion tests were less sharp than those in the control group. 4) Skin temperatures, nail press test, vibratory sense, and pain sense after 5 degrees C immersion test and those after 10 degrees C immersion test showed statistically significant positive correlation. 5) 10 degrees C immersion test is as effective as 5 degrees C immersion test in finding nervous disorders, but 5 degrees C immersion test is more effective than 10 degrees C immersion test in finding circulatory disorders. However patients with Raynaud's phenomena or moderate circulatory disorders can also be found even by 10 degrees C immersion test. 6) Cold water immersion test revealed disorders not only in skin temperature and by nail press test but revealed also disorders in vibratory sense and pain sense, therefore it is desirable that cold water immersion test should be done in the examination of vibration diseases. PMID:6304386

  9. Vibration analysis of a bearing integrated sensor module

    NASA Astrophysics Data System (ADS)

    Suryavanshi, Abhijit P.; Gao, Robert X.

    2001-12-01

    Implementing a reliable condition monitoring system for bearing fault diagnosis and prognosis poses a big challenge to the industry. This challenge stems from the fact that bearing failure is statistical in nature, and thus contains elements of uncertainty and unpredictability. To achieve high accuracy in bearing diagnosis in spite of this inherent variance, reliable data acquisition and analysis techniques are needed. This paper focuses on the vibration analysis of a wireless transmitter module with an integrated sensor that is embedded into the bearing outer raceway for high signal-to-noise ratio data acquisition. A mechatronic design of the sensor module under severe space constraints is presented. The paper also analyses an optimizing scheme for the placement of sensor module substrate supports to reduce vibration transmitted from the bearing to the on-board electronics.

  10. 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.

  11. 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.

  12. On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification

    NASA Astrophysics Data System (ADS)

    Gao, Liang; Li, Fuhai; Thrall, Michael J.; Yang, Yaliang; Xing, Jiong; Hammoudi, Ahmad A.; Zhao, Hong; Massoud, Yehia; Cagle, Philip T.; Fan, Yubo; Wong, Kelvin K.; Wang, Zhiyong; Wong, Stephen T. C.

    2011-09-01

    We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.

  13. Fault detection of mechanical drives under variable operating conditions based on wavelet packet Rnyi entropy signatures

    NASA Astrophysics Data System (ADS)

    Bokoski, Pavle; Juri?i?, ?ani

    2012-08-01

    In this paper we propose a novel approach for the diagnosis of gearboxes in presumably non-stationary and unknown operating conditions. The approach makes use of information indices based on the Rnyi entropy derived from coefficients of the wavelet packet transform of measured vibration records. These indices quantify some statistical properties of instantaneous power of the generated vibration that are largely unaffected by changes in the operating conditions. The analysis is based on probability density of the envelope of a sum of sinusoidal signals with random amplitude and phase. Such an approach requires no a priori information about the operating conditions and no prior data describing physical characteristics of the monitored drive. The fault detection capabilities of the proposed feature set are demonstrated on a two-stage gearbox operating under different rotational speeds and loads with various seeded mechanical faults.

  14. The IEEE eighteenth international symposium on fault-tolerant computing (Digest of Papers)

    SciTech Connect

    Not Available

    1988-01-01

    These proceedings collect papers on fault detection and computers. Topics include: software failure behavior, fault tolerant distributed programs, parallel simulation of faults, concurrent built-in self-test techniques, fault-tolerant parallel processor architectures, probabilistic fault diagnosis, fault tolerances in hypercube processors and cellular automation modeling.

  15. 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

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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.

  4. Improving the Performance of the Structure-Based Connectionist Network for Diagnosis of Helicopter Gearboxes

    NASA Technical Reports Server (NTRS)

    Jammu, Vinay B.; Danai, Koroush; Lewicki, David G.

    1996-01-01

    A diagnostic method is introduced for helicopter gearboxes that uses knowledge of the gear-box structure and characteristics of the 'features' of vibration to define the influences of faults on features. The 'structural influences' in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. The structural influences are then converted to fuzzy variables, to account for the approximate nature of the lumped-mass model, and used as the weights of a connectionist network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal vibration features through the weights of SBCN to obtain fault possibility values for each component in the gearbox. Upon occurrence of misdiagnoses, the SBCN also has the ability to improve its diagnostic performance. For this, a supervised training method is presented which adapts the weights of SBCN to minimize the number of misdiagnoses. For experimental evaluation of the SBCN, vibration data from a OH-58A helicopter gearbox collected at NASA Lewis Research Center is used. Diagnostic results indicate that the SBCN is able to diagnose about 80% of the faults without training, and is able to improve its performance to nearly 100% after training.

  5. 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.

  6. 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.

  7. A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals

    NASA Astrophysics Data System (ADS)

    Guo, Wei; Tse, Peter W.

    2013-01-01

    Today, remote machine condition monitoring is popular due to the continuous advancement in wireless communication. Bearing is the most frequently and easily failed component in many rotating machines. To accurately identify the type of bearing fault, large amounts of vibration data need to be collected. However, the volume of transmitted data cannot be too high because the bandwidth of wireless communication is limited. To solve this problem, the data are usually compressed before transmitting to a remote maintenance center. This paper proposes a novel signal compression method that can substantially reduce the amount of data that need to be transmitted without sacrificing the accuracy of fault identification. The proposed signal compression method is based on ensemble empirical mode decomposition (EEMD), which is an effective method for adaptively decomposing the vibration signal into different bands of signal components, termed intrinsic mode functions (IMFs). An optimization method was designed to automatically select appropriate EEMD parameters for the analyzed signal, and in particular to select the appropriate level of the added white noise in the EEMD method. An index termed the relative root-mean-square error was used to evaluate the decomposition performances under different noise levels to find the optimal level. After applying the optimal EEMD method to a vibration signal, the IMF relating to the bearing fault can be extracted from the original vibration signal. Compressing this signal component obtains a much smaller proportion of data samples to be retained for transmission and further reconstruction. The proposed compression method were also compared with the popular wavelet compression method. Experimental results demonstrate that the optimization of EEMD parameters can automatically find appropriate EEMD parameters for the analyzed signals, and the IMF-based compression method provides a higher compression ratio, while retaining the bearing defect characteristics in the transmitted signals to ensure accurate bearing fault diagnosis.

  8. Interactive Instruction in Solving Fault Finding Problems.

    ERIC Educational Resources Information Center

    Brooke, J. B.; And Others

    1978-01-01

    A training program is described which provides, during fault diagnosis, additional information about the relationship between the remaining faults and the available indicators. An interactive computer program developed for this purpose and the first results of experimental training are described. (Author)

  9. Fault identification and classification of rolling element bearing based on time-varying autoregressive spectrum

    NASA Astrophysics Data System (ADS)

    Wang, Guofeng; Luo, Zhigao; Qin, Xuda; Leng, Yonggang; Wang, Taiyong

    2008-05-01

    Rolling element bearing faults are among the main causes of breakdown of rotating machines and its condition monitoring based on vibration signal has been used extensively. For obtaining more accurate time-frequency spectrum estimation, time-varying autoregressive method based on Kalman smoothing algorithm is utilized to realize parametric modeling of non-stationary signal so as to obtain high resolution time-frequency spectrum. Singular value decomposition (SVD) method is adopted to obtain the first left and right singular vectors of time-frequency spectrum. And by down sampling and preprocessing, these singular vectors are taken as feature vectors of time-frequency spectrum. Moreover, radial basis function (RBF) neural network is adopted to realize the automated classification. By classification of rolling element bearing in four kinds of different status, the results show that algorithm mentioned above can realize the automated and accurate diagnosis of bearing fault.

  10. Dynamic characteristics of multi-degrees of freedom system rotor-bearing system with coupling faults of rub-impact and crack

    NASA Astrophysics Data System (ADS)

    Ren, Zhaohui; Zhou, Shihua; Li, Chaofeng; Wen, Bangchun

    2014-07-01

    Extensive studies on rotor systems with single or coupled multiple faults have been carried out. However these studies are limited to single-span rotor systems. A finite element model for a complex rotor-bearing system with coupled faults is presented. The dynamic responses of the rotor-bearing system are obtained by using the rotor dynamics theory and the modern nonlinear dynamics theory in connection with the continuation-shooting algorithm(commonly used for obtaining a periodic solution for a nonlinear system) for a range of rub-impact clearances and crack depths. The stability and Hopf instability of the periodic motion of the rotor-bearing system with coupled faults are analyzed by using the procedure described. The results indicate that the finite element method is an effective way for determining the dynamic responses of such complex rotor-bearing systems. Further for a rotor system with rub-impact and crack faults, the influences of the clearances are significantly different for different rub-impact stiffness. On the contrary, the influence of crack depths is rather small. The instability speeds of the rotor-bearing system increase due to the presence of the crack fault. The results obtained using the new finite element model, presented for computation and analysis of dynamic responses of the rotor-bearing systems with coupled faults, are in accordance with measurements in experiment. The formulations given can be used for diagnosis of faults, vibration control, and safe and stable operations of real rotor-bearing systems.

  11. Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform

    NASA Astrophysics Data System (ADS)

    Wang, Yanxue; He, Zhengjia; Zi, Yanyang

    2010-01-01

    In order to enhance the desired features related to some special type of machine fault, a technique based on the dual-tree complex wavelet transform (DTCWT) is proposed in this paper. It is demonstrated that DTCWT enjoys better shift invariance and reduced spectral aliasing than second-generation wavelet transform (SGWT) and empirical mode decomposition by means of numerical simulations. These advantages of the DTCWT arise from the relationship between the two dual-tree wavelet basis functions, instead of the matching of the used single wavelet basis function to the signal being analyzed. Since noise inevitably exists in the measured signals, an enhanced vibration signals denoising algorithm incorporating DTCWT with NeighCoeff shrinkage is also developed. Denoising results of vibration signals resulting from a crack gear indicate the proposed denoising method can effectively remove noise and retain the valuable information as much as possible compared to those DWT- and SGWT-based NeighCoeff shrinkage denoising methods. As is well known, excavation of comprehensive signatures embedded in the vibration signals is of practical importance to clearly clarify the roots of the fault, especially the combined faults. In the case of multiple features detection, diagnosis results of rolling element bearings with combined faults and an actual industrial equipment confirm that the proposed DTCWT-based method is a powerful and versatile tool and consistently outperforms SGWT and fast kurtogram, which are widely used recently. Moreover, it must be noted, the proposed method is completely suitable for on-line surveillance and diagnosis due to its good robustness and efficient algorithm.

  12. 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.

  13. Critical fault patterns determination in fault-tolerant computer systems

    NASA Technical Reports Server (NTRS)

    Mccluskey, E. J.; Losq, J.

    1978-01-01

    The method proposed tries to enumerate all the critical fault-patterns (successive occurrences of failures) without analyzing every single possible fault. The conditions for the system to be operating in a given mode can be expressed in terms of the static states. Thus, one can find all the system states that correspond to a given critical mode of operation. The next step consists in analyzing the fault-detection mechanisms, the diagnosis algorithm and the process of switch control. From them, one can find all the possible system configurations that can result from a failure occurrence. Thus, one can list all the characteristics, with respect to detection, diagnosis, and switch control, that failures must have to constitute critical fault-patterns. Such an enumeration of the critical fault-patterns can be directly used to evaluate the overall system tolerance to failures. Present research is focused on how to efficiently make use of these system-level characteristics to enumerate all the failures that verify these characteristics.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection

    NASA Astrophysics Data System (ADS)

    McDonald, Geoff L.; Zhao, Qing; Zuo, Ming J.

    2012-11-01

    In this paper a new deconvolution method is presented for the detection of gear and bearing faults from vibration data. The proposed maximum correlated Kurtosis deconvolution method takes advantage of the periodic nature of the faults as well as the impulse-like vibration behaviour associated with most types of faults. The results are compared to the standard minimum entropy deconvolution method on both simulated and experimental data. The experimental data is from a gearbox with gear chip fault, and the results are compared between healthy and faulty vibrations. The results indicate that the proposed maximum correlated Kurtosis deconvolution method performs considerably better than the traditional minimum entropy deconvolution method, and often performs several times better at fault detection. In addition to this improved performance, deconvolution of separate fault periods is possible; allowing for concurrent fault detection. Finally, an online implementation is proposed and shown to perform well and be computationally achievable on a personal computer.

  19. An online debris sensor system with vibration resistance for lubrication analysis.

    PubMed

    Ding, Yongbin; Wang, Yanxue; Xiang, Jiawei

    2016-02-01

    The health condition of the lubricated systems can be directly indicated by the concentration and material type of the abrasive particles, which may provide very early warnings of faults/failures and benefit the condition based maintenance. Oil debris particle detecting techniques are thus important for machinery condition monitoring and fault diagnosis. This work proposes a new structure of online debris sensor (ODS), which applies the radial magnetic field, different from the traditional axial magnetic field. The designed ODS can effectively reduce the interferences of the background noise and the vibration of the machine in operation. Moreover, in order to optimally determine the number of turns of an inductive coil and the current of the drive coils, two methods are developed respectively in this work which can ensure sensitivity and anti-vibration features of the ODS. The instrumentation circuit system for detecting debris particles and sensing signals has been also designed to extract and to record the signatures of particles. The designed ODS device is then applied to analyze micro debris particles in the lubricating system on a test rig. Experimental results have demonstrated that ODS can successfully detect the 120 μm(H) ferrous particles and 500 μm(H) non-ferrous particles under vibration conditions. PMID:26931893

  20. An online debris sensor system with vibration resistance for lubrication analysis

    NASA Astrophysics Data System (ADS)

    Ding, Yongbin; Wang, Yanxue; Xiang, Jiawei

    2016-02-01

    The health condition of the lubricated systems can be directly indicated by the concentration and material type of the abrasive particles, which may provide very early warnings of faults/failures and benefit the condition based maintenance. Oil debris particle detecting techniques are thus important for machinery condition monitoring and fault diagnosis. This work proposes a new structure of online debris sensor (ODS), which applies the radial magnetic field, different from the traditional axial magnetic field. The designed ODS can effectively reduce the interferences of the background noise and the vibration of the machine in operation. Moreover, in order to optimally determine the number of turns of an inductive coil and the current of the drive coils, two methods are developed respectively in this work which can ensure sensitivity and anti-vibration features of the ODS. The instrumentation circuit system for detecting debris particles and sensing signals has been also designed to extract and to record the signatures of particles. The designed ODS device is then applied to analyze micro debris particles in the lubricating system on a test rig. Experimental results have demonstrated that ODS can successfully detect the 120 μm(H) ferrous particles and 500 μm(H) non-ferrous particles under vibration conditions.

  1. 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.

  2. VIBRATION COMPACTION

    DOEpatents

    Hauth, J.J.

    1962-07-01

    A method of compacting a powder in a metal container is described including the steps of vibrating the container at above and below the resonant frequency and also sweeping the frequency of vibration across the resonant frequency several times thereby following the change in resonant frequency caused by compaction of the powder. (AEC)

  3. Vibrating superconductors

    NASA Astrophysics Data System (ADS)

    Esquinazi, Pablo

    1991-11-01

    This review is concerned with effects in the energy dissipation and elastic modulus of superconductors vibrating in a magnetic field. The physics of superconducting vibrating reeds and reeds made of superconducting suspensions is thoroughly described as well as the main features observed in other oscillators applied to flux pinning studies. It is argued that among the diversity of methods to study superconducting and pinning properties as a function of magnetic field and temperature, the vibrating reed technique is one of the most sensitive due to the accurate measurement of frequency and dissipation with feasible magnetometry applications. Results of the elastic coupling between the flux line lattice and the atomic lattice in high- and low- T c superconductors obtained with the vibrating reed are summarized as well as the behavior of vibrating type II superconductors near their lower critical field. Results from mechanical measurements in high-temperature superconductors are reviewed, which support the model of thermally activated depinning and vortex diffusion.

  4. Fault tolerant operation of switched reluctance machine

    NASA Astrophysics Data System (ADS)

    Wang, Wei

    The energy crisis and environmental challenges have driven industry towards more energy efficient solutions. With nearly 60% of electricity consumed by various electric machines in industry sector, advancement in the efficiency of the electric drive system is of vital importance. Adjustable speed drive system (ASDS) provides excellent speed regulation and dynamic performance as well as dramatically improved system efficiency compared with conventional motors without electronics drives. Industry has witnessed tremendous grow in ASDS applications not only as a driving force but also as an electric auxiliary system for replacing bulky and low efficiency auxiliary hydraulic and mechanical systems. With the vast penetration of ASDS, its fault tolerant operation capability is more widely recognized as an important feature of drive performance especially for aerospace, automotive applications and other industrial drive applications demanding high reliability. The Switched Reluctance Machine (SRM), a low cost, highly reliable electric machine with fault tolerant operation capability, has drawn substantial attention in the past three decades. Nevertheless, SRM is not free of fault. Certain faults such as converter faults, sensor faults, winding shorts, eccentricity and position sensor faults are commonly shared among all ASDS. In this dissertation, a thorough understanding of various faults and their influence on transient and steady state performance of SRM is developed via simulation and experimental study, providing necessary knowledge for fault detection and post fault management. Lumped parameter models are established for fast real time simulation and drive control. Based on the behavior of the faults, a fault detection scheme is developed for the purpose of fast and reliable fault diagnosis. In order to improve the SRM power and torque capacity under faults, the maximum torque per ampere excitation are conceptualized and validated through theoretical analysis and experiments. With the proposed optimal waveform, torque production is greatly improved under the same Root Mean Square (RMS) current constraint. Additionally, position sensorless operation methods under phase faults are investigated to account for the combination of physical position sensor and phase winding faults. A comprehensive solution for position sensorless operation under single and multiple phases fault are proposed and validated through experiments. Continuous position sensorless operation with seamless transition between various numbers of phase fault is achieved.

  5. Faults Discovery By Using Mined Data

    NASA Technical Reports Server (NTRS)

    Lee, Charles

    2005-01-01

    Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees.

  6. SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

    NASA Astrophysics Data System (ADS)

    Golafshan, Reza; Yuce Sanliturk, Kenan

    2016-03-01

    Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

  7. 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.

  8. Tuning of fault tolerant control design parameters.

    PubMed

    DeLima, Pedro G; Yen, Gary G

    2008-01-01

    This paper presents two major contributions in the field of fault tolerant control. First, it gathers points of concern typical to most fault tolerant control applications and translates the chosen performance metrics into a set of six practical design specifications. Second, it proposes initialization and tuning procedures through which a particular fault tolerant control architecture not only can be set to comply with the required specifications, but also can be tuned online to compensate for a total of twelve properties, such as the noise rejection levels for fault detection and diagnosis signals. The proposed design is realized over a powerful architecture that combines the flexibility of adaptive critic designs with the long term memory and learning capabilities of a supervisor. This paper presents a practical design procedure to facilitate the applications of a fundamentally sound fault tolerant control architecture in real-world problems. PMID:18028929

  9. Study on fault diagnose expert system for large astronomy telescope

    NASA Astrophysics Data System (ADS)

    Liu, Jia-jing; Luo, Ming-Cheng; Tang, Peng-yi; Wu, Wen-qing; Zhang, Guang-yu; Zhang, Hong-fei; Wang, Jian

    2014-08-01

    The development of astronomical techniques and telescopes currently entered a new vigorous period. The telescopes have trends of the giant, complex, diversity of equipment and wide span of control despite of optical, radio space telescopes. That means, for telescope observatory, the control system must have these specifications: flexibility, scalability, distributive, cross-platform and real-time, especially the fault locating and fault processing is more important when fault or exception arise. Through the analysis of the structure of large telescopes, fault diagnosis expert system of large telescope based on the fault tree and distributed log service is given.

  10. Application Research of Two Real-Time Fault Diagnostic Methods in the Nuclear Power Plants

    SciTech Connect

    Chun-Li Xie; Yong-Kuo Liu; Hong Xia

    2006-07-01

    In order to guarantee the safety of nuclear power plants (NPP), we built two real-time fault diagnosis systems adopting VISUAL BAS6.0 programming language, which apply neural network technology and data fusion technology respectively. The fault diagnosis systems interchange data with the simulator timely utilizing communication interface. We insert faults on simulator to test the two systems on line. The advantages and disadvantages are illuminated and contrasted through analyzing the faults diagnostic results off- line, which establish the foundation for the further research and application to the fault diagnosis system of the nuclear power plants. (authors)

  11. Vibration disease.

    PubMed

    Kkosy, T

    1989-04-01

    Today, in this age of technology, vibration caused by machinery is an almost universal hazard. Vibration transferred from a machine to the human body may cause discomfort, a reduction of performance, and even injury. Vibratory manual tools may cause damage to the circulatory system of the upper extremities (Raynaud's syndrome), to the peripheral nerves (peripheral neuropathy), and to the bones and joints (aseptic necrosis, fatigue fractures, degenerative joint disease). Vehicles and machines causing floor vibration cause degenerative disc disease of the lumbar spine. The pathogenesis of vibration injuries is still not completely clear and there is no effective treatment. Some of the abnormalities are irreversible and may cause permanent decrease of working ability, and even unemployment. This is why prevention is so important. Prevention is complex, including technical and organizational measures, use of individual protective clothing and footwear, and medical supervision both before and during employment. Workers who are exposed to vibration should be protected against other aggravating factors such as cold and noise, etc. Vibration-induced injuries are recognized in law in many countries as grounds for financial compensation. Their cost to industry is rising and, unless a means of prevention or cure is found, will continue to do so in the foreseeable future. PMID:2661029

  12. 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.

  13. Aiding the operator during novel fault diagnosis

    NASA Technical Reports Server (NTRS)

    Yoon, W. C.; Hammer, J. M.

    1985-01-01

    An aid is proposed for the operator who must deal with a novel failure. A novel failure is one that is not covered by the operator's training or procedures or by an expert system (if present). The aid contains a disaggregated model of the system for reasoning causally about the system. It is to work in parallel with the human and interact at various levels of control. It is designed specifically to mitigate some human suboptimalities and biases during decision making.

  14. An investigation of the effects of measurement noise in the use of instantaneous angular speed for machine diagnosis

    NASA Astrophysics Data System (ADS)

    Gu, Fengshou; Yesilyurt, Isa; Li, Yuhua; Harris, Georgina; Ball, Andrew

    2006-08-01

    In order to discriminate small changes for early fault diagnosis of rotating machines, condition monitoring demands that the measurement of instantaneous angular speed (IAS) of the machines be as accurate as possible. This paper develops the theoretical basis and practical implementation of IAS data acquisition and IAS estimation when noise influence is included. IAS data is modelled as a frequency modulated signal of which the signal-to-noise ratio can be improved by using a high-resolution encoder. From this signal model and analysis, optimal configurations for IAS data collection are addressed for high accuracy IAS measurement. Simultaneously, a method based on analytic signal concept and fast Fourier transform is also developed for efficient and accurate estimation of IAS. Finally, a fault diagnosis is carried out on an electric induction motor driving system using IAS measurement. The diagnosis results show that using a high-resolution encoder and a long data stream can achieve noise reduction by more than 10 dB in the frequency range of interest, validating the model and algorithm developed. Moreover, the results demonstrate that IAS measurement outperforms conventional vibration in diagnosis of incipient faults of motor rotor bar defects and shaft misalignment.

  15. Fault zone fabric and fault weakness.

    PubMed

    Collettini, Cristiano; Niemeijer, Andr; Viti, Cecilia; Marone, Chris

    2009-12-17

    Geological and geophysical evidence suggests that some crustal faults are weak compared to laboratory measurements of frictional strength. Explanations for fault weakness include the presence of weak minerals, high fluid pressures within the fault core and dynamic processes such as normal stress reduction, acoustic fluidization or extreme weakening at high slip velocity. Dynamic weakening mechanisms can explain some observations; however, creep and aseismic slip are thought to occur on weak faults, and quasi-static weakening mechanisms are required to initiate frictional slip on mis-oriented faults, at high angles to the tectonic stress field. Moreover, the maintenance of high fluid pressures requires specialized conditions and weak mineral phases are not present in sufficient abundance to satisfy weak fault models, so weak faults remain largely unexplained. Here we provide laboratory evidence for a brittle, frictional weakening mechanism based on common fault zone fabrics. We report on the frictional strength of intact fault rocks sheared in their in situ geometry. Samples with well-developed foliation are extremely weak compared to their powdered equivalents. Micro- and nano-structural studies show that frictional sliding occurs along very fine-grained foliations composed of phyllosilicates (talc and smectite). When the same rocks are powdered, frictional strength is high, consistent with cataclastic processes. Our data show that fault weakness can occur in cases where weak mineral phases constitute only a small percentage of the total fault rock and that low friction results from slip on a network of weak phyllosilicate-rich surfaces that define the rock fabric. The widespread documentation of foliated fault rocks along mature faults in different tectonic settings and from many different protoliths suggests that this mechanism could be a viable explanation for fault weakening in the brittle crust. PMID:20016599

  16. 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.

  17. 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.

  18. Feature Extraction using Wavelet Transform for Multi-class Fault Detection of Induction Motor

    NASA Astrophysics Data System (ADS)

    Chattopadhyay, P.; Konar, P.

    2014-01-01

    In this paper the theoretical aspects and feature extraction capabilities of continuous wavelet transform (CWT) and discrete wavelet transform (DWT) are experimentally verified from the point of view of fault diagnosis of induction motors. Vertical frame vibration signal is analyzed to develop a wavelet based multi-class fault detection scheme. The redundant and high dimensionality information of CWT makes it computationally in-efficient. Using greedy-search feature selection technique (Greedy-CWT) the redundancy is eliminated to a great extent and found much superior to the widely used DWT technique, even in presence of high level of noise. The results are verified using MLP, SVM, RBF classifiers. The feature selection technique has enabled determination of the most relevant CWT scales and corresponding coefficients. Thus, the inherent limitations of CWT like proper selection of scales and redundant information are eliminated. In the present investigation `db8' is found as the best mother wavelet, due to its long period and higher number of vanishing moments, for detection of motor faults.

  19. 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 effici