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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. Vibration signal models for fault diagnosis of planet bearings

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Ma, Haoqun; Zuo, Ming J.

    2016-05-01

    Rolling element bearings are key components of planetary gearboxes. Among them, the motion of planet bearings is very complex, encompassing spinning and revolution. Therefore, planet bearing vibrations are highly intricate and their fault characteristics are completely different from those of fixed-axis case, making planet bearing fault diagnosis a difficult topic. In order to address this issue, we derive the explicit equations for calculating the characteristic frequency of outer race, rolling element and inner race fault, considering the complex motion of planet bearings. We also develop the planet bearing vibration signal model for each fault case, considering the modulation effects of load zone passing, time-varying angle between the gear pair mesh and fault induced impact force, as well as the time-varying vibration transfer path. Based on the developed signal models, we derive the explicit equations of Fourier spectrum in each fault case, and summarize the vibration spectral characteristics respectively. The theoretical derivations are illustrated by numerical simulation, and further validated experimentally and all the three fault cases (i.e. outer race, rolling element and inner race localized fault) are diagnosed.

  3. 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.

  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. Phenomenological models of vibration signals for condition monitoring and fault diagnosis of epicyclic gearboxes

    NASA Astrophysics Data System (ADS)

    Lei, Yaguo; Liu, Zongyao; Lin, Jing; Lu, Fanbo

    2016-05-01

    Condition monitoring and fault diagnosis of epicyclic gearboxes using vibration signals are not as straightforward as that of fixed-axis gearboxes since epicyclic gearboxes behave quite differently from fixed-axis gearboxes in many aspects, like spectral structures. Aiming to present the spectral structures of vibration signals of epicyclic gearboxes, phenomenological models of vibration signals of epicyclic gearboxes are developed by algebraic equations and spectral structures of these models are deduced using Fourier series analysis. In the phenomenological models, all the possible vibration transfer paths from gear meshing points to a fixed transducer and the effects of angular shifts of planet gears on the spectral structures are considered. Accordingly, time-varying vibration transfer paths from sun-planet/ring-planet gear meshing points to the fixed transducer due to carrier rotation are given by window functions with different amplitudes. And an angular shift in one planet gear position is introduced in the process of modeling. After the theoretical derivations, three experiments are conducted on an epicyclic gearbox test rig and the spectral structures of collected vibration signals are analyzed. As a result, the effects of angular shifts of planet gears are verified, and the phenomenological models of vibration signals when a local fault occurs on the sun gear and the planet gear are validated, respectively. The experiment results demonstrate that the established phenomenological models in this paper are helpful to the condition monitoring and fault diagnosis of epicyclic gearboxes.

  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. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

    PubMed Central

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

    2016-01-01

    Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

  8. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

    PubMed

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

    2016-01-01

    Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

  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

  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

    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. Using the Correlation Dimension for Vibration Fault Diagnosis of Rolling Element BEARINGS—I. Basic Concepts

    NASA Astrophysics Data System (ADS)

    Logan, David; Mathew, Joseph

    1996-05-01

    There is a wide variety of condition monitoring techniques currently in use for the diagnosis and prediction of machinery faults, but little attention has been paid to the occurrence and detection of chaotic behaviour in time series vibration signals. This paper introduces some of the basic concepts of chaos theory, then details a method for quantifying a fractal dimension from a time series, the correlation dimension. Some of the practical difficulties encountered in measuring the correlation dimension from the correlation integral algorithm are also outlined. Finally, some experimental results from a rolling element bearing test rig are presented.

  14. Rotational speed invariant fault diagnosis in bearings using vibration signal imaging and local binary patterns.

    PubMed

    Khan, Sheraz Ali; Kim, Jong-Myon

    2016-04-01

    Structural vibrations of bearing housings are used for diagnosing fault conditions in bearings, primarily by searching for characteristic fault frequencies in the envelope power spectrum of the vibration signal. The fault frequencies depend on the non-stationary angular speed of the rotating shaft. This paper explores an imaging-based approach to achieve rotational speed independence. Cycle length segments of the rectified vibration signal are stacked to construct grayscale images which exhibit unique textures for each fault. These textures show insignificant variation with the rotational speed, which is confirmed by the classification results using their local binary pattern histograms. PMID:27106344

  15. 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.

  16. 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

  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-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

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

    NASA Astrophysics Data System (ADS)

    Mahvash Mohammadi, Ali

    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

  20. Vibration signal analysis for gear fault diagnosis with various crack progression scenarios

    NASA Astrophysics Data System (ADS)

    Mohammed, Omar D.; Rantatalo, Matti; Aidanpää, Jan-Olov; Kumar, Uday

    2013-12-01

    There are different analytical scenarios assumed for crack propagation in the gear tooth root. This paper presents an investigation of the performance of statistical fault detection indicators (the RMS and kurtosis) for three different series of crack propagation scenarios, to compare these scenarios from a fault diagnostics point of view. These scenarios imply different forms of cracks with propagation by a certain step of crack depth. The first scenario assumes a crack being extended through the whole tooth width with a uniform crack depth distribution, while the second scenario assumes the crack being extended through the whole tooth width with a parabolic crack depth distribution, and finally in the third scenario the crack is assumed to be propagating in both the depth and the length directions simultaneously. The time-varying gear mesh stiffness has been investigated using the programme code developed in the present research, and the crack propagation can be modelled with any of the presented crack propagation scenarios. Dynamic simulation has been performed to obtain the residual signals of all the studied cases for each crack propagation scenario. The comparison of the statistical indicators applied to the residual signals shows that in the first scenario the faults are most easily detectable, since in this scenario there is a change in the indicators implying a dramatic decrease in the gear mesh stiffness. The fault detection in the 2nd scenario is more difficult, as the crack propagates with no significant reflection on the mesh stiffness loss. The 3rd proposed scenario should receive more attention in research because it could occur in reality in case of non-uniform load distribution. However, with this scenario it is difficult to perform early fault detection, since there is a very slight change in the statistical indicators at the beginning of the crack propagation. After which, these indicators show a significant change when the crack grows deeper

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Using the Correlation Dimension for Vibration Fault Diagnosis of Rolling Element BEARINGS—II. Selection of Experimental Parameters

    NASA Astrophysics Data System (ADS)

    Logan, David B.; Mathew, Joseph

    1996-05-01

    Determination of the correlation dimension from experimental chaotic time series data involves two steps. First, reconstruction of the attractor using the time delay method from the raw time series, followed by computation of the correlation dimension from the phase space vectors. We demonstrate how this process has been performed experimentally on bearing vibration acceleration times series. The effects on the correlation dimension were noted as each of the relevant parameters was individually varied and the optimum values for each determined.

  6. Vibrational analysis using neural network classifier for motor fault detection

    NASA Astrophysics Data System (ADS)

    Su, Hua; Kim, Yeong Cheol; Lee, Yidong; Chong, Kil To

    2005-12-01

    Early detection and diagnosis of induction machine incipient faults are desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. However, fault detection using analytical method is not always possible because it requires perfect knowledge of a process model. A neural network based expert system was proposed for diagnostic problems of the induction motors using vibration analysis. The short-time Fourier transform (STFT) was used to process the quasi-steady vibration signals, and the neural network was trained and tested using the vibration spectra. The efficiency of the developed neural network expert system was evaluated. The obtained results lead to a conclusion that neural network expert system can be developed based on vibration measurements acquired online from the machine.

  7. Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load

    NASA Astrophysics Data System (ADS)

    Villa, Luisa F.; Reñones, Aníbal; Perán, Jose R.; de Miguel, Luis J.

    2012-05-01

    In this paper the authors are dealing with the detection of different mechanical faults (unbalance and misalignment) under a wide range of working conditions of speed and load. The conditions tested in a test bench are similar to the ones that can be found in different kinds of machines like for example wind turbines. The authors demonstrate how to take advantage of the information on vibrations from the mechanical system under study in a wide range of load and speed conditions. Using such information the prognosis and detection of faults is faster and more reliable than the one obtained from an analysis over a restricted range of working conditions (e.g. nominal).

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as "Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement - Parts 1 and 2"

    NASA Astrophysics Data System (ADS)

    Tse, Peter W.; Wang, Dong

    2013-11-01

    Rolling element bearings are widely used in rotating machines. An early warning of bearing faults helps to prevent machinery breakdown and economic loss. Vibration-based envelope analysis has been proven to be one of the most effective methods for bearing fault diagnosis. The core of an envelope analysis is to find a resonant frequency band for a band-pass filtering for the enhancement of weak bearing fault signals. A new concept called a sparsogram is proposed in Part 1 paper. The aim of the sparsogram is to quickly determine the resonant frequency bands. The sparsogram is constructed using the sparsity measurements of the power spectra from the envelopes of wavelet packet coefficients at different wavelet packet decomposition depths. The optimal wavelet packet node can be selected by visually inspecting the largest sparsity value of the wavelet packet coefficients obtained from all wavelet packet nodes. Then, the wavelet packet coefficients extracted from the selected wavelet packet node is demodulated for envelope analysis. Several case studies including a simulated bearing fault signal mixed with heavy noise and real bearing fault signals collected from a rotary motor were used to validate the sparsogram. The results show that the sparsogram effectively locates the resonant frequency bands, where the bearing fault signature has been magnified in these bands. Several comparison studies with three popular wavelet packet decomposition based methods were conducted to show the superior capability of sparsogram in bearing fault diagnosis.

  16. 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)

  17. 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.

  18. 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.

  19. 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.

  20. Tractable particle filters for robot fault diagnosis

    NASA Astrophysics Data System (ADS)

    Verma, Vandi

    Experience has shown that even carefully designed and tested robots may encounter anomalous situations. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner. Robot fault diagnosis typically requires tracking a very large number of possible faults in complex non-linear dynamic systems with noisy sensors. Traditional methods either ignore the uncertainly or use linear approximations of nonlinear system dynamics. Such approximations are often unrealistic, and as a result faults either go undetected or become confused with non-fault conditions. Probability theory provides a natural representation for uncertainty, but an exact Bayesian solution for the diagnosis problem is intractable. Classical Monte Carlo methods, such as particle filters, suffer from substantial computational complexity. This is particularly true with the presence of rare, yet important events, such as many system faults. The thesis presents a set of complementary algorithms that provide an approach for computationally tractable fault diagnosis. These algorithms leverage probabilistic approaches to decision theory and information theory to efficiently track a large number of faults in a general dynamic system with noisy measurements. The problem of fault diagnosis is represented as hybrid (discrete/continuous) state estimation. Taking advantage of structure in the domain it dynamically concentrates computation in the regions of state space that are currently most relevant without losing track of less likely states. Experiments with a dynamic simulation of a six-wheel rocker-bogie rover show a significant improvement in performance over the classical approach.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. HVAC Fault Detection and Diagnosis Toolkit

    Energy Science and Technology Software Center (ESTSC)

    2004-12-31

    This toolkit supports component-level model-based fault detection methods in commercial building HVAC systems. The toolbox consists of five basic modules: a parameter estimator for model calibration, a preprocessor, an AHU model simulator, a steady-state detector, and a comparator. Each of these modules and the fuzzy logic rules for fault diagnosis are described in detail. The toolbox is written in C++ and also invokes the SPARK simulation program.

  10. 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.

  11. 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.

  12. Monitoring and fault diagnosis of hybrid systems.

    PubMed

    Zhao, Feng; Koutsoukos, Xenofon; Haussecker, Horst; Reich, Jim; Cheung, Patrick

    2005-12-01

    Many networked embedded sensing and control systems can be modeled as hybrid systems with interacting continuous and discrete dynamics. These systems present significant challenges for monitoring and diagnosis. Many existing model-based approaches focus on diagnostic reasoning assuming appropriate fault signatures have been generated. However, an important missing piece is the integration of model-based techniques with the acquisition and processing of sensor signals and the modeling of faults to support diagnostic reasoning. This paper addresses key modeling and computational problems at the interface between model-based diagnosis techniques and signature analysis to enable the efficient detection and isolation of incipient and abrupt faults in hybrid systems. A hybrid automata model that parameterizes abrupt and incipient faults is introduced. Based on this model, an approach for diagnoser design is presented. The paper also develops a novel mode estimation algorithm that uses model-based prediction to focus distributed processing signal algorithms. Finally, the paper describes a diagnostic system architecture that integrates the modeling, prediction, and diagnosis components. The implemented architecture is applied to fault diagnosis of a complex electro-mechanical machine, the Xerox DC265 printer, and the experimental results presented validate the approach. A number of design trade-offs that were made to support implementation of the algorithms for online applications are also described. PMID:16366248

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. Gearbox fault diagnosis based on time-frequency domain synchronous averaging and feature extraction technique

    NASA Astrophysics Data System (ADS)

    Zhang, Shengli; Tang, Jiong

    2016-04-01

    Gearbox is one of the most vulnerable subsystems in wind turbines. Its healthy status significantly affects the efficiency and function of the entire system. Vibration based fault diagnosis methods are prevalently applied nowadays. However, vibration signals are always contaminated by noise that comes from data acquisition errors, structure geometric errors, operation errors, etc. As a result, it is difficult to identify potential gear failures directly from vibration signals, especially for the early stage faults. This paper utilizes synchronous averaging technique in time-frequency domain to remove the non-synchronous noise and enhance the fault related time-frequency features. The enhanced time-frequency information is further employed in gear fault classification and identification through feature extraction algorithms including Kernel Principal Component Analysis (KPCA), Multilinear Principal Component Analysis (MPCA), and Locally Linear Embedding (LLE). Results show that the LLE approach is the most effective to classify and identify different gear faults.

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. Statistical Fault Detection & Diagnosis Expert System

    SciTech Connect

    Wegerich, Stephan

    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.

  6. 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.

  7. Condition Monitoring and Fault Diagnosis of Wet-Shift Clutch Transmission Based on Multi-technology

    NASA Astrophysics Data System (ADS)

    Chen, Man; Wang, Liyong; Ma, Biao

    Based on the construction feature and operating principle of the wet-shift clutch transmission, the condition monitoring and fault diagnosis for the transmission of the tracklayer with wet-shift clutch were implemented with using the oil analysis technology, function parameter test method and vibration analysis technology. The new fault diagnosis methods were proposed, which are to build the gray modeling with the oil analysis data, and to test the function parameter of the clutch press, the rotate speed of each gear, the oil press of the steer system and lubrication system and the hydraulic torque converter. It's validated that the representative function signals were chosen to execute the condition monitoring analysis, when the fault symptoms were found, and the oil analysis data were used to apply the gray modeling to forecast the fault occurs time can satisfy the demand of the condition monitoring and fault diagnosis for the transmission regular work.

  8. 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.

  9. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis

    NASA Astrophysics Data System (ADS)

    Liu, Ruonan; Yang, Boyuan; Zhang, Xiaoli; Wang, Shibin; Chen, Xuefeng

    2016-06-01

    Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti-noise ability and detect incipient fault, a novel fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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.

  15. Transformer fault diagnosis using continuous sparse autoencoder.

    PubMed

    Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou

    2016-01-01

    This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis. PMID:27119052

  16. 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.

  17. 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.

  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. Vibration signature analysis of a faulted gear transmission system

    NASA Astrophysics Data System (ADS)

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

    1994-06-01

    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.

  20. 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.

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

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Liang, Ming

    2014-09-01

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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Aiding the operator during novel fault diagnosis

    SciTech Connect

    Yoon, W.C.

    1987-01-01

    The design and evaluation are presented for knowledge-based aiding for a human operator who must diagnose a novel fault in a dynamic, physical system. Since the operator must employ deep reasoning about system behavior to diagnose such a fault, the performance may be restricted by cognitive limitations and biases. A computer aid based on a qualitative model of the system was built to help the operator overcome some of his/her cognitive limitations. This aid differs from most expert systems in that it operates at several levels of interaction 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 causal 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-H aiding). Three experiments were conducted to evaluate the aiding approaches and to investigate the nature of deep-reasoning diagnosis.

  7. 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.

  8. 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.

  9. 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.

  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. 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.

  13. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs

    NASA Astrophysics Data System (ADS)

    Lei, Yaguo; Zuo, Ming J.

    2009-12-01

    A Hilbert-Huang transform (HHT) is a time-frequency technique and has been widely applied to analyzing vibration signals in the field of fault diagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate this problem. The IMFs generated by EEMD have different sensitivity to faults. Some IMFs are sensitive and closely related to the faults but others are irrelevant. To enhance the accuracy of the HHT in fault diagnosis of rotating machinery, an improved HHT based on EEMD and sensitive IMFs is proposed in this paper. Simulated signals demonstrate the effectiveness of the improved HHT in diagnosing the faults of rotating machinery. Finally, the improved HHT is applied to diagnosing an early rub-impact fault of a heavy oil catalytic cracking machine set, and the application results prove that the improved HHT is superior to the HHT based on all IMFs of EMD.

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

    PubMed

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

    2012-03-01

    Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for fault diagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the 'known-fault' classes while the low similarity points are labeled as 'unknown-faults'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%. PMID:22035775

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  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. Multi-scale autocorrelation via morphological wavelet slices for rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Li, Chuan; Liang, Ming; Zhang, Yi; Hou, Shumin

    2012-08-01

    Fault features of rolling element bearings can be reflected by geometrical structures of the bearing vibration signals. These symptoms, however, often spread over various morphological scales without a known pattern. For this reason, we propose a multi-scale autocorrelation via morphological wavelet slices (MAMWS) approach to detect bearing fault signatures. The vibration measurement of a bearing is decomposed using morphological stationary wavelet with different resolutions of structuring elements. The extracted temporal components are then transformed to form a frequency-domain view of morphological slices by the Fourier transform. Although this three-dimensional representation is more intuitive in terms of fault diagnosis, the existence of the noise may reduce its readability. Hence the autocorrelation function is exploited to produce a multi-scale autocorrelation spectrogram from which the maximal autocorrelation values of all frequencies are aggregated into an ichnographical spectral representation. Accordingly the fault signature is highlighted for easy diagnosis of bearing faults. The effectiveness of the proposed approach has been demonstrated by both the simulation and experimental signal analyses.

  3. 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

  4. Support vector machine in machine condition monitoring and fault diagnosis

    NASA Astrophysics Data System (ADS)

    Widodo, Achmad; Yang, Bo-Suk

    2007-08-01

    Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

  5. Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum

    NASA Astrophysics Data System (ADS)

    Yan, Xiaoan; Jia, Minping; Xiang, Ling

    2016-07-01

    Owing to the character of diversity and complexity, the compound fault diagnosis of rotating machinery under non-stationary operation has turned into a challenging task. In this paper, a novel method based on the optimal variational mode decomposition (OVMD) and 1.5-dimension envelope spectrum is proposed for detecting the compound faults of rotating machinery. In this method, compound fault signals are first decomposed by using OVMD containing optimal decomposition parameters, and several intrinsic mode components are obtained. Then, an adaptive selection method based on the weight factor (WF) is presented to choose two intrinsic mode components that contain the principal fault characteristic information. Finally, the 1.5-dimension envelope spectrum of the selected intrinsic mode components is utilized to extract the compound fault characteristic information of vibration signals. The performance of the proposed method is demonstrated by using the simulation signal and the experimental vibration signals collected from a rolling bearing and a gearbox with compound faults. The analysis results suggest that the proposed method is not only capable of detecting compound faults of a bearing and a gearbox, but can separate the characteristic signatures of compound faults. The research offers a new means for the compound fault diagnosis of rotating machinery.

  6. Fault detection and diagnosis of diesel engine valve trains

    NASA Astrophysics Data System (ADS)

    Flett, Justin; Bone, Gary M.

    2016-05-01

    This paper presents the development of a fault detection and diagnosis (FDD) system for use with a diesel internal combustion engine (ICE) valve train. A novel feature is generated for each of the valve closing and combustion impacts. Deformed valve spring faults and abnormal valve clearance faults were seeded on a diesel engine instrumented with one accelerometer. Five classification methods were implemented experimentally and compared. The FDD system using the Naïve-Bayes classification method produced the best overall performance, with a lowest detection accuracy (DA) of 99.95% and a lowest classification accuracy (CA) of 99.95% for the spring faults occurring on individual valves. The lowest DA and CA values for multiple faults occurring simultaneously were 99.95% and 92.45%, respectively. The DA and CA results demonstrate the accuracy of our FDD system for diesel ICE valve train fault scenarios not previously addressed in the literature.

  7. 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.

  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. 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 ρ 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.

  10. 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.

  11. Fault diagnosis and prognostic of solid oxide fuel cells

    NASA Astrophysics Data System (ADS)

    Wu, XiaoJuan; Ye, Qianwen

    2016-07-01

    One of the major hurdles for solid oxide fuel cell (SOFC) commercialization is poor long-term performance and durability. Accurate fault diagnostic and prognostic technologies are two important tools to improve SOFC durability. In literature, plenty of diagnosis techniques for SOFC systems have been successfully designed. However, no literature studies SOFC fault prognosis approaches. In this paper a unified fault diagnosis and prognosis strategy is presented to identify faults (anode poisoning, cathode humidification or normal) and predict the remaining useful life for SOFC systems. Using a squares support vector machine (LS-SVM) classifier, a diagnosis model is built to identify SOFC different types of faults. After fault detection, two hidden semi-Mark models (HSMMs) are respectively employed to estimate SOFC remaining useful life in the case of anode poisoning and cathode humidification. The simulation results show that the fault recognition rates with the LS-SVM model are at best 97%, and the predicted error of the remaining useful life is within ±20%.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. 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

  19. Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

    A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faults diagnosis. Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox. The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum, and the ANN classification method has achieved high detection accuracy. Hence, the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases, and thus have application importance.

  20. Bond graph modeling and experimental verification of a novel scheme for fault diagnosis of rolling element bearings in special operating conditions

    NASA Astrophysics Data System (ADS)

    Mishra, C.; Samantaray, A. K.; Chakraborty, G.

    2016-09-01

    Vibration analysis for diagnosis of faults in rolling element bearings is complicated when the rotor speed is variable or slow. In the former case, the time interval between the fault-induced impact responses in the vibration signal are non-uniform and the signal strength is variable. In the latter case, the fault-induced impact response strength is weak and generally gets buried in the noise, i.e. noise dominates the signal. This article proposes a diagnosis scheme based on a combination of a few signal processing techniques. The proposed scheme initially represents the vibration signal in terms of uniformly resampled angular position of the rotor shaft by using the interpolated instantaneous angular position measurements. Thereafter, intrinsic mode functions (IMFs) are generated through empirical mode decomposition (EMD) of resampled vibration signal which is followed by thresholding of IMFs and signal reconstruction to de-noise the signal and envelope order tracking to diagnose the faults. Data for validating the proposed diagnosis scheme are initially generated from a multi-body simulation model of rolling element bearing which is developed using bond graph approach. This bond graph model includes the ball and cage dynamics, localized fault geometry, contact mechanics, rotor unbalance, and friction and slip effects. The diagnosis scheme is finally validated with experiments performed with the help of a machine fault simulator (MFS) system. Some fault scenarios which could not be experimentally recreated are then generated through simulations and analyzed through the developed diagnosis scheme.

  1. 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

  2. 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.

  3. 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

  4. Fault Detection of Reciprocating Compressors using a Model from Principles Component Analysis of Vibrations

    NASA Astrophysics Data System (ADS)

    Ahmed, M.; Gu, F.; Ball, A. D.

    2012-05-01

    Traditional vibration monitoring techniques have found it difficult to determine a set of effective diagnostic features due to the high complexity of the vibration signals originating from the many different impact sources and wide ranges of practical operating conditions. In this paper Principal Component Analysis (PCA) is used for selecting vibration feature and detecting different faults in a reciprocating compressor. Vibration datasets were collected from the compressor under baseline condition and five common faults: valve leakage, inter-cooler leakage, suction valve leakage, loose drive belt combined with intercooler leakage and belt loose drive belt combined with suction valve leakage. A model using five PCs has been developed using the baseline data sets and the presence of faults can be detected by comparing the T2 and Q values from the features of fault vibration signals with corresponding thresholds developed from baseline data. However, the Q -statistic procedure produces a better detection as it can separate the five faults completely.

  5. 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.

  6. 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.

  7. A modular neural network approach to fault diagnosis.

    PubMed

    Rodriguez, C; Rementeria, S; Martin, J I; Lafuente, A; Muguerza, J; Perez, J

    1996-01-01

    Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of "toy" alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation. PMID:18255587

  8. 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.

  9. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

  11. 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.

  12. Automated misfire diagnosis in engines using torsional vibration and block rotation

    NASA Astrophysics Data System (ADS)

    Chen, J.; Randall, R. B.; Peeters, B.; Van der Auweraer, H.; Desmet, W.

    2012-05-01

    Even though a lot of research has gone into diagnosing misfire in IC engines, most approaches use torsional vibration of the crankshaft, and only a few use the rocking motion (roll) of the engine block. Additionally, misfire diagnosis normally requires an expert to interpret the analysis results from measured vibration signals. Artificial Neural Networks (ANNs) are potential tools for the automated misfire diagnosis of IC engines, as they can learn the patterns corresponding to various faults. This paper proposes an ANN-based automated diagnostic system which combines torsional vibration and rotation of the block for more robust misfire diagnosis. A critical issue with ANN applications is the network training, and it is improbable and/or uneconomical to expect to experience a sufficient number of different faults, or generate them in seeded tests, to obtain sufficient experimental results for the network training. Therefore, new simulation models, which can simulate combustion faults in engines, were developed. The simulation models are based on the thermodynamic and mechanical principles of IC engines and therefore the proposed misfire diagnostic system can in principle be adapted for any engine. During the building process of the models, based on a particular engine, some mechanical and physical parameters, for example the inertial properties of the engine parts and parameters of engine mounts, were first measured and calculated. A series of experiments were then carried out to capture the vibration signals for both normal condition and with a range of faults. The simulation models were updated and evaluated by the experimental results. Following the signal processing of the experimental and simulation signals, the best features were selected as the inputs to ANN networks. The automated diagnostic system comprises three stages: misfire detection, misfire localization and severity identification. Multi-layer Perceptron (MLP) and Probabilistic Neural Networks were

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. Fault Prognosis and Diagnosis of an Automotive Rear Axle Gear Using a RBF-BP Neural Network

    NASA Astrophysics Data System (ADS)

    Shao, Yimin; Liang, Jie; Gu, Fengshou; Chen, Zaigang; Ball, Andrew

    2011-07-01

    The rear axle gear is one of the key parts of transmission system for automobiles. Its healthy state directly influences the security and reliability of the automotives. However, non-stationary and nonlinear characteristics of gear vibration due to load and speed fluctuations, makes it difficult to detect and diagnosis the faults from the transmission gear. To solve this problem a fault prognosis and diagnosis method based on a combination of radial basis function(RBF) and back-propagation (BP) neural networks is proposed in this paper. Firstly, a moving average pretreatment is used to suppress the time series fluctuation of vibration characteristic parameter tie series and reduce the interference of random noise. Then, the RBF network is applied to the pretreated parameter sequences for fault prognosis. Furthermore, based on self-learning ability of neural networks, characteristic parameters for different common faults are learned by a BP network. Then the trained BP neural network is utilized for fault diagnosis of the rear axle gear. The results show that the proposed method has a good performance in prognosing and diagnosing different faults from the rear axle gear.

  1. 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

  2. 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

  3. Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis

    NASA Astrophysics Data System (ADS)

    Charles, P.; Sinha, Jyoti K.; Gu, F.; Lidstone, L.; Ball, A. D.

    2009-04-01

    Early fault detection and diagnosis for medium-speed diesel engines is important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion related fault detection capability of crankshaft torsional vibration. The encoder signal, often used for shaft speed measurement, has been used to construct the instantaneous angular speed (IAS) waveform, which actually represents the signature of the torsional vibration. Earlier studies have shown that the IAS signal and its fast Fourier transform (FFT) analysis are effective for monitoring engines with less than eight cylinders. The applicability to medium-speed engines, however, is strongly contested due to the high number of cylinders and large moment of inertia. Therefore the effectiveness of the FFT-based approach has further been enhanced by improving the signal processing to determine the IAS signal and subsequently tested on a 16-cylinder engine. In addition, a novel method of presentation, based on the polar coordinate system of the IAS signal, has also been introduced; to improve the discrimination features of the faults compared to the FFT-based approach of the IAS signal. The paper discusses two typical experimental studies on 16- and 20-cylinder engines, with and without faults, and the diagnosis results by the proposed polar presentation method. The results were also compared with the earlier FFT-based method of the IAS signal.

  4. 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.

  5. Bearing fault diagnosis based on variational mode decomposition and total variation denoising

    NASA Astrophysics Data System (ADS)

    Zhang, Suofeng; Wang, Yanxue; He, Shuilong; Jiang, Zhansi

    2016-07-01

    Feature extraction plays an essential role in bearing fault detection. However, the measured vibration signals are complex and non-stationary in nature, and meanwhile impulsive signatures of rolling bearing are usually immersed in stochastic noise. Hence, a novel hybrid fault diagnosis approach is developed for the denoising and non-stationary feature extraction in this work, which combines well with the variational mode decomposition (VMD) and majoriation–minization based total variation denoising (TV-MM). The TV-MM approach is utilized to remove stochastic noise in the raw signal and to enhance the corresponding characteristics. Since the parameter λ is very important in TV-MM, the weighted kurtosis index is also proposed in this work to determine an appropriate λ used in TV-MM. The performance of the proposed hybrid approach is conducted through the analysis of the simulated and practical bearing vibration signals. Results demonstrate that the proposed approach has superior capability to detect roller bearing faults from vibration signals.

  6. 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.

  7. 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.

  8. Detection And Diagnosis Of Ball Bearing Imperfections In Reaction Wheels By Micro-Vibration Test

    NASA Astrophysics Data System (ADS)

    Le, M. P.; van der Heide, E.; Seiler, R.; Cottaar, E. J. E.

    2012-07-01

    The results of micro-vibration test contain information on unbalance level, torque ripples, and most importantly the bearing health status. In this paper, the envelop analysis technique is proposed for localizing imperfections in the bearing. The envelop analysis, which is a powerful method used in ball bearing fault diagnosis, is adapted to the micro-vibration data. This method analyzes the data around the structural resonance: through the amplification of the vibration smaller faults can be detected. The procedure of envelope analysis, its practical issues and robustness are validated with simulated signals. Finally, the envelope analysis is applied to diagnose and evaluate the change in bearing status throughout two environmental tests: sine vibration and full ECSS shock. The result of envelope analysis shows its high sensitivity in revealing the development of small imperfections, makes an initial step in reaction wheel condition monitoring (on-ground and in-flight) and provides insights in design improvement to further lower micro vibration levels of reaction wheels.

  9. Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification

    PubMed Central

    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

  10. 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.

  11. 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

  12. 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

  13. Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

    Order tracking based on time-frequency representation (TFR) is one of the most effective methods for gear fault detection under time-varying rotational speed without using a tachometer. However, for a rolling element bearing, the signal components related to rotational speed usually cannot be directly extracted from the TFR. As such, we propose a new method to solve this problem. This method consists of four main steps: (a) signal filtering via fast spectral kurtosis (SK) analysis - this together with the short time Fourier transform (STFT) leads to a TFR of the filtered signal with clear fault-revealing trend lines, (b) extraction of instantaneous fault characteristic frequency (IFCF) from the TFR using an amplitude-sum based spectral peak search algorithm, (c) signal resampling based on the extracted IFCF to convert the non-stationary time-domain signal into the stationary fault phase angle (FPA) domain signal, and (d) transform of the FPA domain signal into the domain of the fault characteristic order (FCO) and identification of fault type from the FCO spectrum. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals.

  14. 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

  15. 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

  16. 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.

  17. Variogram-based fault diagnosis in an interconnected tank system.

    PubMed

    Kouadri, Abdelmalek; Aitouche, Mohanad Amokrane; Zelmat, Mimoun

    2012-05-01

    We consider in this paper the fault diagnosis problem of a three tank system DTS-200 pilot plant. The presented approach is based on the analysis of the variogram, which is a graphical variance representation that characterizes the distribution of a measured dataset, and is used to extract the sensor fault parameters. These parameters are obtained by determining the best mathematical model that fits the empirical data. Nonlinear regression techniques are used to estimate the model coefficients. Experimental study is provided to illustrate the potential applicability of this method in process monitoring. PMID:22369877

  18. 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

  19. Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

    NASA Astrophysics Data System (ADS)

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

    2011-10-01

    Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and

  20. 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.

  1. 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.

  2. An approach to fault diagnosis of vacuum cleaner motors based on sound analysis

    NASA Astrophysics Data System (ADS)

    Benko, Uros̆; Petrovc̆ic̆, Janko; Juričić, Đani; Tavčar, Joža; Rejec, Jožica

    2005-03-01

    This paper addresses the problem of the detailed quality end-test of vacuum cleaner motors at the end of the manufacturing cycle. For the prototyping purposes a test rig has been constructed and is presented in short. The diagnostic system built hereto takes advantage of vibration, sound and commutation analysis as well as parity relation checks. The paper focuses on the sound analysis module and provides two main contributions. First, an analysis of sound sources is performed and a set of appropriate features is suggested. Second, efficient signal processing algorithms are developed in order to detect and localise bearing faults, defects in fan impeller, improper brush-commutator contacts and rubbing of rotating surfaces. A thorough laboratory study shows that the underlying diagnostic modules provide accurate diagnosis, high sensitivity with respect to faults, and good diagnostic resolution.

  3. 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.

  4. Segmented infrared image analysis for rotating machinery fault diagnosis

    NASA Astrophysics Data System (ADS)

    Duan, Lixiang; Yao, Mingchao; Wang, Jinjiang; Bai, Tangbo; Zhang, Laibin

    2016-07-01

    As a noncontact and non-intrusive technique, infrared image analysis becomes promising for machinery defect diagnosis. However, the insignificant information and strong noise in infrared image limit its performance. To address this issue, this paper presents an image segmentation approach to enhance the feature extraction in infrared image analysis. A region selection criterion named dispersion degree is also formulated to discriminate fault representative regions from unrelated background information. Feature extraction and fusion methods are then applied to obtain features from selected regions for further diagnosis. Experimental studies on a rotor fault simulator demonstrate that the presented segmented feature enhancement approach outperforms the one from the original image using both Naïve Bayes classifier and support vector machine.

  5. Applications of chaotic oscillator in machinery fault diagnosis

    NASA Astrophysics Data System (ADS)

    Li, Chongsheng; Qu, Liangsheng

    2007-01-01

    The forward detecting method is used to detect weak periodic signals by identifying the transformation of the chaotic oscillator from the chaotic state to the large-scale periodic state when a weak external periodic signal is applied. Based on the method above, in this paper, a similar method, which is devised in a reverse way, is presented. The method detects the change of a weak signal by identifying the transformation of the chaotic oscillator from the large-scale periodic state to the chaotic state when a weak external signal is applied. This paper discusses and summarizes the features and scopes of both methods in their application in the field of machinery fault diagnosis. Satisfactory results have been achieved when using both of them in the fault diagnosis of rolling bearings and automobile gearboxes. The paper also presents how to use symbol sequence statistics to automatically identify the state transformation of the chaotic oscillator.

  6. FDSAC-SPICE: fault diagnosis software for analog circuit based on SPICE simulation

    NASA Astrophysics Data System (ADS)

    Cao, Yiqin; Cen, Zhao-Hui; Wei, Jiao-Long

    2009-12-01

    This paper presents a novel fault diagnosis software (called FDSAC-SPICE) based on SPICE simulator for analog circuits. Four important techniques in AFDS-SPICE, including visual user-interface(VUI), component modeling and fault modeling (CMFM), fault injection and fault simulation (FIFS), fault dictionary and fault diagnosis (FDFD), greatly increase design-for-test and diagnosis efficiency of analog circuit by building a fault modeling-injection-simulationdiagnosis environment to get prior fault knowledge of target circuit. AFDS-SPICE also generates accurate fault coverage statistics that are tied to the circuit specifications. With employing a dictionary diagnosis method based on node-signalcharacters and regular BPNN algorithm, more accurate and effective diagnosis results are available for analog circuit with tolerance.

  7. Fault Diagnosis in a Fully Distributed Local Computer Network.

    NASA Astrophysics Data System (ADS)

    Kwag, Hye Keun

    Local computer networks are being installed in diverse application areas. Many of the networks employ a distributed control scheme, which has advantages in performance and reliability over a centralized one. However, distribution of control increases the difficulty in locating faulty hardware elements. Consequently, advantages may not be fully realized unless measures are taken to account for the difficulties of fault diagnosis; yet, not much work has been done in this area. A hardcore is defined as a node or a part of a node which is fault-free and which can diagnose other elements in a system. Faults are diagnosed in most existing distributed local computer networks by assuming that every node, or a part of every node, is a fixed hardcore: a fixed node or a part of a fixed node is always a hardcore. Maintaining such high reliability may not be possible or cost-effective for some systems. A distributed network contains dynamically redundant elements, and it is reasonable to assume that fewer nodes are simultaneously faulty than are fault-free at any point in the life cycle of the network. A diagnostic model is proposed herein which determines bindary evaluation results according to the status of the testing and tested nodes, and which leads the network to dynamically locate a fault-free node (a hardcore). This diagnostic model is, in most cases, simpler to implement and more cost-effective than the fixed hardcore. The selected hardcore can diagnose the other elements and can locate permanent faults. In a hop-by-hop test, the destination node and every intermediate node in a path test the transmitted data. This dissertation presents another method to locate an element with frequent transient faults; it checks data only at the destination, thereby, eliminating the need for a hop-by-hop test.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. 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)

  15. Experimental Investigation on FRA Diagnosis of Transformer Faults

    NASA Astrophysics Data System (ADS)

    Sano, Takahiro; Miyagi, Katsunori

    The need for economical, reliable and effective delivery of electric power leads to the search for new, efficient and effective methods for diagnosing the high voltage equipments in the industries all over the world. As the average usage period of transformers increases, the necessity to know the internal condition of transformers is increasing. It is therefore critically important to establish monitoring and diagnostic techniques that can perform transformer condition assessment. Frequency response analysis, generally known as FRA, is one of the technologies to diagnose transformers. Using case studies, this paper presents the effectiveness of FRA as measurements for detecting transformer faults. This paper introduces the fact that FRA waveforms have useful information about diagnosis of fault on winding shield and core earths, and that the condition outside transformers can affect frequency response characteristics. The FRA measurement results are further investigated through a simulation study using a computer model.

  16. 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.

  17. 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.

  18. EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

    NASA Astrophysics Data System (ADS)

    Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

    2016-05-01

    A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

    DOE PAGESBeta

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; Sheng, Shuangwen

    2014-12-18

    Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

  4. Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

    SciTech Connect

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; Sheng, Shuangwen

    2014-12-18

    Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issue is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.

  5. 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.

  6. 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

  7. Satellite fault diagnosis using support vector machines based on a hybrid voting mechanism.

    PubMed

    Yin, Hong; 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

  8. 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

  9. SOM neural network fault diagnosis method of polymerization kettle equipment optimized by improved PSO algorithm.

    PubMed

    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

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. Induction motor fault diagnosis based on the k-NN and optimal feature selection

    NASA Astrophysics Data System (ADS)

    Nguyen, Ngoc-Tu; Lee, Hong-Hee

    2010-09-01

    The k-nearest neighbour (k-NN) rule is applied to diagnose the conditions of induction motors. The features are extracted from the time vibration signals while the optimal features are selected by a genetic algorithm based on a distance criterion. A weight value is assigned to each feature to help select the best quality features. To improve the classification performance of the k-NN rule, each of the k neighbours are evaluated by a weight factor based on the distance to the test pattern. The proposed k-NN is compared to the conventional k-NN and support vector machine classification to verify the performance of an induction motor fault diagnosis.

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

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

    PubMed

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  10. 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.

  11. Joint amplitude and frequency demodulation analysis based on intrinsic time-scale decomposition for planetary gearbox fault diagnosis

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Lin, Xuefeng; Zuo, Ming J.

    2016-05-01

    Planetary gearbox vibration signals feature complex modulations, thus leading to intricate sideband structure and resulting in difficulty in fault characteristic frequency identification. Intrinsic time-scale decomposition has unique merits, such as high adaptability to changes in signals, low computational complexity, good capability to suppress mode mixing and to preserve temporal information of transients, and excellent suitability for mono-component decomposition of complex multi-component signals. In order to address the issue with planetary gearbox fault diagnosis due to the multiple modulation sources, a joint amplitude and frequency demodulation analysis method is proposed, by exploiting the merits of intrinsic time-scale decomposition. The signal is firstly decomposed into a series of mono-component proper rotational components. Then the one with its instantaneous frequency fluctuating around the gear meshing frequency or its harmonics is selected as the sensitive component. Next, Fourier transformation is applied to the instantaneous amplitude and instantaneous frequency of the sensitive component to obtain the amplitude and frequency demodulated spectra respectively. Finally, a planetary gearbox fault is diagnosed by matching the peaks in the amplitude and frequency demodulated spectra with the theoretical gear fault characteristic frequencies. The proposed method is illustrated by a numerical simulated signal, and further validated by lab experimental signals of a planetary gearbox. The localized faults of sun, planet and ring gears are diagnosed, showing the effectiveness of the method.

  12. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaoyuan; Zhou, Jianzhong

    2013-12-01

    This study presents a novel procedure based on ensemble empirical mode decomposition (EEMD) and optimized support vector machine (SVM) for multi-fault diagnosis of rolling element bearings. The vibration signal is adaptively decomposed into a number of intrinsic mode functions (IMFs) by EEMD. Two types of features, the EEMD energy entropy and singular values of the matrix whose rows are IMFs, are extracted. EEMD energy entropy is used to specify whether the bearing has faults or not. If the bearing has faults, singular values are input to multi-class SVM optimized by inter-cluster distance in the feature space (ICDSVM) to specify the fault type. The proposed method was tested on a system with an electric motor which has two rolling bearings with 8 normal working conditions and 48 fault working conditions. Five groups of experiments were done to evaluate the effectiveness of the proposed method. The results show that the proposed method outperforms other methods both mentioned in this paper and published in other literatures.

  13. Fault self-diagnosis designing method of the automotive electronic control system

    NASA Astrophysics Data System (ADS)

    Ding, Yangyan; Yang, Zhigang; Fu, Xiaolin

    2005-12-01

    The fault self-diagnosis system is an important component of an the automotive electronic control system. Designers of automotive electronic control systems urgently require or need a complete understanding of the self-diagnosis designing method of the control system in order to apply it in practice. Aiming at this exigent need, self-diagnosis methods of designing sensors, electronic control unit (ECU), and actuators, which are the three main parts of automotive electronic control systems, are discussed in this paper. According to the fault types and characteristics of commonly used sensors, self-diagnosis designing methods of the sensors are discussed. Then fault diagnosis techniques of sensors utilizing signal detection and analytical redundancy are analysed and summarized respectively, from the viewpoint of the self-diagnosis designing method. Also, problems about failure self-diagnosis of ECU are analyzed here. For different fault types of an ECU, setting up a circuit monitoring method and a self-detection method of the hardware circuit are adopted respectively. Using these two methods mentioned above, a real-time and on-line technique of failure self-diagnosis is presented. Furthermore, the failure self-diagnosis design method of ECU are summarized. Finally, common faults of actuators are analyzed and the general design method of the failure self-diagnosis system is presented. It is suggested that self-diagnosis design methods relative to the failure of automotive electronic control systems can offer a useful approach to designers of control systems.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines.

    PubMed

    Saidi, Lotfi; Ben Ali, Jaouher; Benbouzid, Mohamed; Bechhoefer, Eric

    2016-07-01

    A critical work of bearing fault diagnosis is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing. However, the main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with the highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In this paper, we present a squared envelope based spectral kurtosis method to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. We have verified the potential of the spectral kurtosis diagnostic strategy in performance improvements for single-defect diagnosis using real laboratory-collected vibration data sets. PMID:27000630

  19. Development of fault diagnosis system for transformer based on multi-class support vector machines

    NASA Astrophysics Data System (ADS)

    Cao, Jian; Qian, Suxiang; Hu, Hongsheng; Yan, Gongbiao

    2007-12-01

    The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and having high generalization ability. It is strong to solve the problem with small sample, nonlinear and high dimension. The fundamental theory of DGA (Dissolved Gas Analysis, DGA) and fault characteristic of transformer is firstly researched in this paper, and then the disadvantages of traditional method of transformer fault diagnosis are analyzed, finally, a new fault diagnosis method using multi-class support vector machines (M-SVMs) based on DGA theory for transformer is put forward. Then the fault diagnosis model based on M-SVMs for transformer is established. At the same time, the fault diagnosis system based on M-SVMs for transformer is developed. The system can realize the acquisition of the dissolving gas in the transformer oil and data timely and low cost transmission by GPRS (General Packet Radio Service, GPRS). And it can identify out the transformer running state according to the acquisition data. The test results show that the method proposed has an excellent performance on correct ratio. And it can overcome the disadvantage of the traditional three-ratio method which lacks of fault coding and no fault types in the existent coding. Combining the wireless communication technology with the monitoring technology, the designed and developed system can greatly improve the real-time and continuity for the transformer' condition monitoring and fault diagnosis.

  20. 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.

  1. A review on empirical mode decomposition in fault diagnosis of rotating machinery

    NASA Astrophysics Data System (ADS)

    Lei, Yaguo; Lin, Jing; He, Zhengjia; Zuo, Ming J.

    2013-02-01

    Rotating machinery covers a broad range of mechanical equipment and plays a significant role in industrial applications. It generally operates under tough working environment and is therefore subject to faults, which could be detected and diagnosed by using signal processing techniques. Empirical mode decomposition (EMD) is one of the most powerful signal processing techniques and has been extensively studied and widely applied in fault diagnosis of rotating machinery. Numerous publications on the use of EMD for fault diagnosis have appeared in academic journals, conference proceedings and technical reports. This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics. First, the EMD method is briefly introduced, the usefulness of the method is illustrated and the problems and the corresponding solutions are listed. Then, recent applications of EMD to fault diagnosis of rotating machinery are summarized in terms of the key components, such as rolling element bearings, gears and rotors. Finally, the outstanding open problems of EMD in fault diagnosis are discussed and potential future research directions are identified. It is expected that this review will serve as an introduction of EMD for those new to the concepts, as well as a summary of the current frontiers of its applications to fault diagnosis for experienced researchers.

  2. 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.

  3. 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.

  4. 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.

  5. 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%.

  6. 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.

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

    PubMed

    Zhao, Jinsong; Huang, Jianchao; Sun, Wei

    2008-11-01

    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. PMID:18255276

  8. Actuator fault tolerant multi-controller scheme using set separation based diagnosis

    NASA Astrophysics Data System (ADS)

    Seron, María M.; De Doná, José A.

    2010-11-01

    We present a fault tolerant control strategy based on a new principle for actuator fault diagnosis. The scheme employs a standard bank of observers which match the different fault situations that can occur in the plant. Each of these observers has an associated estimation error with distinctive dynamics when an estimator matches the current fault situation of the plant. Based on the information from each observer, a fault detection and isolation (FDI) module is able to reconfigure the control loop by selecting the appropriate control law from a bank of controllers, each of them designed to stabilise and achieve reference tracking for one of the given fault models. The main contribution of this article is to propose a new FDI principle which exploits the separation of sets that characterise healthy system operation from sets that characterise transitions from healthy to faulty behaviour. The new principle allows to provide pre-checkable conditions for guaranteed fault tolerance of the overall multi-controller scheme.

  9. 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

  10. 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

  11. 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.

  12. 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

  13. 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.

  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. 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.

  16. 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.

  17. 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

  18. 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

  19. 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.

  20. 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.

  1. Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Zhang, Shangbin; Lu, Siliang; He, Qingbo; Kong, Fanrang

    2016-09-01

    For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis.

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

    PubMed Central

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

    2013-01-01

    Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox. PMID:23334609

  3. 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

  4. Remote Fault Information Acquisition and Diagnosis System of the Combine Harvester Based on LabVIEW

    NASA Astrophysics Data System (ADS)

    Chen, Jin; Wu, Pei; Xu, Kai

    Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.

  5. 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.

  6. 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.

  7. 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.

  8. 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

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

    PubMed Central

    Yu, Bing; Liu, Dongdong; Zhang, Tianhong

    2011-01-01

    Sensor fault diagnosis is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can’t be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a fault diagnosis method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient. PMID:22163734

  10. 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.

  11. Development of model-based fault diagnosis algorithms for MASCOTTE cryogenic test bench

    NASA Astrophysics Data System (ADS)

    Iannetti, A.; Marzat, J.; Piet-Lahanier, H.; Ordonneau, G.; Vingert, L.

    2014-12-01

    This article describes the on-going results of a fault diagnosis benchmark for a cryogenic rocket engine demonstrator. The benchmark consists in the use of classical model- based fault diagnosis methods to monitor the status of the cooling circuit of the MASCOTTE cryogenic bench. The algorithms developed are validated on real data from the last 2014 firing campaign (ATAC campaign). The objective of this demonstration is to find practical diagnosis alternatives to classical redline providing more flexible means of data exploitation in real time and for post processing.

  12. 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

  13. 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

  14. 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

  15. Fault detection and diagnosis using neural network approaches

    NASA Technical Reports Server (NTRS)

    Kramer, Mark A.

    1992-01-01

    Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

  16. 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.

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

    NASA Astrophysics Data System (ADS)

    Li, Chuan; Liang, Ming

    2012-01-01

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

  18. Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration signals emanated from defective rolling element bearings

    NASA Astrophysics Data System (ADS)

    Paliwal, Deepak; Choudhur, Achintya; Govandhan, T.

    2014-06-01

    Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform (FFT) and continuous wavelet transform (CWT) of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.

  19. 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.

  20. Fault diagnosis based on signed directed graph and support vector machine

    NASA Astrophysics Data System (ADS)

    Han, Xiaoming; Lv, Qing; Xie, Gang; Zheng, Jianxia

    2011-12-01

    Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

  1. Fault diagnosis based on signed directed graph and support vector machine

    NASA Astrophysics Data System (ADS)

    Han, Xiaoming; Lv, Qing; Xie, Gang; Zheng, Jianxia

    2012-01-01

    Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

    NASA Astrophysics Data System (ADS)

    Zhen, D.; Zhao, H. L.; Gu, F.; Ball, A. D.

    2012-05-01

    Dynamic time warping (DTW) is a time-domain-based method and widely used in various similar recognition and data mining applications. This paper presents a phase-compensation-based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align two signals for dissimilarity analysis. However, it has drawbacks such as singularities and high computational demands that limit its application in processing motor current signals for obtaining modulation characteristics accurately in diagnosing compressor faults. Therefore, a phase compensation approach is developed to reduce the singularity effect and a sliding window is designed to improve computing efficiency. Based on the proposed method, the motor current signals measured from the compressor induced with different common faults are analysed for fault diagnosis. Results show that residual signal analysis using the phase-compensation-based DTW allows the fault-related sideband features to be resolved more accurately for obtaining reliable fault detection and diagnosis. It provides an effective and easy approach to the analysis of motor current signals for better diagnosis in the time domain in comparison with conventional Fourier-transform-based methods.

  7. 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.

  8. Fault detection, isolation, and diagnosis of self-validating multifunctional sensors.

    PubMed

    Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen

    2016-06-01

    A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors. PMID:27370486

  9. Fault detection, isolation, and diagnosis of self-validating multifunctional sensors

    NASA Astrophysics Data System (ADS)

    Yang, Jing-li; Chen, Yin-sheng; Zhang, Li-li; Sun, Zhen

    2016-06-01

    A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

  10. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

    Fault diagnosis is an effective tool to guarantee safe operations in gearboxes. Acoustic and vibratory measurements in such mechanical devices are all sensitive to the existence of faults. This work addresses the use of a deep random forest fusion (DRFF) technique to improve fault diagnosis performance for gearboxes by using measurements of an acoustic emission (AE) sensor and an accelerometer that are used for monitoring the gearbox condition simultaneously. The statistical parameters of the wavelet packet transform (WPT) are first produced from the AE signal and the vibratory signal, respectively. Two deep Boltzmann machines (DBMs) are then developed for deep representations of the WPT statistical parameters. A random forest is finally suggested to fuse the outputs of the two DBMs as the integrated DRFF model. The proposed DRFF technique is evaluated using gearbox fault diagnosis experiments under different operational conditions, and achieves 97.68% of the classification rate for 11 different condition patterns. Compared to other peer algorithms, the addressed method exhibits the best performance. The results indicate that the deep learning fusion of acoustic and vibratory signals may improve fault diagnosis capabilities for gearboxes.

  11. 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.

  12. Investigation of the synthetic experiment system of machine equipment fault diagnosis

    NASA Astrophysics Data System (ADS)

    Liu, Hongyu; Xu, Zening; Yu, Xiaoguang

    2008-12-01

    The invention and manufacturing of the synthetic experiment system of machine equipment fault diagnosis filled in the blank of this kind of experiment equipment in China and obtained national practical new type patent. By the motor speed regulation system, machine equipment fault imitation system, measuring and monitoring system and analysis and diagnosis system of the synthetic experiment system, students can regulate motor speed arbitrarily, imitate multi-kinds of machine equipment parts fault, collect the signals of acceleration, speed, displacement, force and temperature and make multi-kinds of time field, frequency field and figure analysis. The application of the synthetic experiment system in our university's teaching practice has obtained good effect on fostering professional eligibility in measuring, monitoring and fault diagnosis of machine equipment. The synthetic experiment system has the advantages of short training time, quick desirable result and low test cost etc. It suits for spreading in university extraordinarily. If the systematic software was installed in portable computer, user can fulfill measuring, monitoring, signal processing and fault diagnosis on multi-kinds of field machine equipment conveniently. Its market foreground is very good.

  13. Fan fault diagnosis based on symmetrized dot pattern analysis and image matching

    NASA Astrophysics Data System (ADS)

    Xu, Xiaogang; Liu, Haixiao; Zhu, Hao; Wang, Songling

    2016-07-01

    To detect the mechanical failure of fans, a new diagnostic method based on the symmetrized dot pattern (SDP) analysis and image matching is proposed. Vibration signals of 13 kinds of running states are acquired on a centrifugal fan test bed and reconstructed by the SDP technique. The SDP pattern templates of each running state are established. An image matching method is performed to diagnose the fault. In order to improve the diagnostic accuracy, the single template, multiple templates and clustering fault templates are used to perform the image matching.

  14. Induction machine fault diagnosis using an external radial flux sensor

    NASA Astrophysics Data System (ADS)

    Romary, R.; Corton, R.; Thailly, D.; Brudny, J. F.

    2005-11-01

    This paper presents a method for detecting faults in the induction machine using a flux sensor which measures the external radial leakage magnetic field of the machine. The procedure uses the spectral lines that correspond to the slotting effect which causes harmonics in the machine air gap. These harmonic frequencies, which are about a few kHz occur in a part of the spectra which is not very dense compared to the one that corresponds to the low frequencies due to the supply or the rotor revolution speed. The considered components can also be detected in the radial magnetic field flowing out of the machine. It can be shown that specific faults lead to additional spectral lines which appear clearly near the original ones due to the slotting effect. Two faults, which can be detected with the same magnetic flux sensor, are presented in this paper: the first one corresponds to a broken rotor bar, the second one is a stator inter turn short circuit.

  15. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants.

    PubMed

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-01-01

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements. PMID:27556472

  16. 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.

  17. Detection and diagnosis of bearing and cutting tool faults using hidden Markov models

    NASA Astrophysics Data System (ADS)

    Boutros, Tony; Liang, Ming

    2011-08-01

    Over the last few decades, the research for new fault detection and diagnosis techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the fault seeded in two different engine bearings. The success rate obtained in our tests for fault severity classification was above 95%. In addition to the fault severity, a location index was developed to determine the fault location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing fault with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.

  18. 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

  19. 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.

  20. 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.

  1. Distributed multisensor fusion for machine condition monitoring fault diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Xue; Zhao, Guohua; Xie, Xin

    2001-09-01

    This paper presents a new general framework for multisensor fusion based on a distributed detection. Parallel processing and distributed multisensor fusion, as rapidly emerging and promising technologies, provides powerful tools for solving this difficult problem, The distribution and parallelism of proposing and confirming of hypothesis in condition and diagnostic is prosed. A combination serial and parallel reconfiguration of n sensors for decision fusion is analyzed. It shows the result for a real-time parallel distributed complex machine condition monitor and fault diagnostic system.

  2. 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.

  3. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis

    NASA Astrophysics Data System (ADS)

    He, Shuilong; Chen, Jinglong; Zhou, Zitong; Zi, Yanyang; Wang, Yanxue; Wang, Xiaodong

    2016-08-01

    Compound-fault diagnosis of mechanical equipment is still challenging at present because of its complexity, multiplicity and non-stationarity. In this work, an adaptive redundant multiwavelet packet (ARMP) method is proposed for the compound-fault diagnosis. Multiwavelet transform has two or more base functions and many excellent properties, making it suitable for detecting all the features of compound-fault simultaneously. However, on the other hand, the fixed basis function used in multiwavelet transform may decrease the accuracy of fault extraction; what's more, the multi-resolution analysis of multiwavelet transform in low frequency band may also leave out the useful features. Thus, the minimum sum of normalized multifractal entropy is adopted as the optimization criteria for the proposed ARMP method, while the relative energy ratio of the characteristic frequency is utilized as an effective way in automatically selecting the sensitive frequency bands. Then, The ARMP technique combined with Hilbert transform demodulation analysis is then applied to detect the compound-fault of bevel gearbox and planetary gearbox. The results verify that the proposed method can effectively identify and detect the compound-fault of mechanical equipment.

  4. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model

    NASA Astrophysics Data System (ADS)

    Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Ran

    2016-05-01

    Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis.

  5. 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.

  6. A Comparison of Different Techniques for Induction Motor Rotor Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    Alwodai, A.; Gu, F.; Ball, A. D.

    2012-05-01

    The problem of failures in induction motors is a large concern due to its significant influence over industrial production. Therefore a large number of detection techniques were presented to avoid this problem. This paper presents the comparison results of induction motor rotor fault detection using three methods: motor current signature analysis (MCSA), surface vibration (SV), and instantaneous angular speed (IAS). These three measurements were performed under different loads with three rotor conditions: baseline, one rotor bar broken and two rotor bar broken. The faults can be detected and diagnosed based on the amplitude difference of the characteristic frequency components of power spectrum. However IAS may be the best technique because it gives the clearest spectrum representation in which the largest amplitude change is observed due to the faults.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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 + +.

  12. 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.

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

    PubMed

    Seera, Manjeevan; Lim, Chee Peng

    2014-04-01

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

  14. Fault diagnosis for stator winding bar hollow strand blockage of turbogenerators based on data fusion

    NASA Astrophysics Data System (ADS)

    Wang, Xianpei; Dai, Zheng Y.; Liu, Zhenxing; Chen, Yalin

    2003-09-01

    Stator Winding Bar Hollow Strand Blockage (SWBHSB) is one of the main faults for large turbo-generators with water and hydrogen cooling system. It will lead to increasing water temperature at the bar exit which may cause hidden troubles for turbo-generator's security. According to a three-layer-structural model of data fusion, this paper presents a fault diagnosis method for turbo-generators based on data fusion technology. Firstly, a bp network on pixel level fusion is set up, in which several temperature parameters at the bar exit are accurately computed. Then in feature level fusion, the fingerprints are distilled from the result of pixel level fusion. Finally, decision level fusion gives a fault diagnosis for the measuring channels and thermometric components. This method can effectively avoid problems such as misinformation and fake report.

  15. 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.

  16. 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.

  17. 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.

  18. 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…

  19. 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.

  20. 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.

  1. 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

  2. Fault detection, diagnosis, and data-driven modeling in HVAC chillers

    NASA Astrophysics Data System (ADS)

    Namburu, Setu M.; Luo, Jianhui; Azam, Mohammad; Choi, Kihoon; Pattipati, Krishna R.

    2005-05-01

    Heating, Ventilation and Air Conditioning (HVAC) systems constitute the largest portion of energy consumption equipment in residential and commercial facilities. Real-time health monitoring and fault diagnosis is essential for reliable and uninterrupted operation of these systems. Existing fault detection and diagnosis (FDD) schemes for HVAC systems are only suitable for a single operating mode with small numbers of faults, and most of the schemes are systemspecific. A generic real-time FDD scheme, applicable to all possible operating conditions, can significantly reduce HVAC equipment downtime, thus improving the efficiency of building energy management systems. This paper presents a FDD methodology for faults in centrifugal chillers. The FDD scheme compares the diagnostic performance of three data-driven techniques, namely support vector machines (SVM), principal component analysis (PCA), and partial least squares (PLS). In addition, a nominal model of a chiller that can predict system response under new operating conditions is developed using PLS. We used the benchmark data on a 90-ton real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables under nominal and eight fault conditions of different severities at twenty seven operating modes.

  3. 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).

  4. 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.

  5. Joint envelope and frequency order spectrum analysis based on iterative generalized demodulation for planetary gearbox fault diagnosis under nonstationary conditions

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Chen, Xiaowang; Liang, Ming

    2016-08-01

    Planetary gearbox vibration signals under nonstationary conditions are characterized by time-varying nature and complex multi-components, making it very difficult to extract features for fault diagnosis. Order spectrum analysis is one of the effective approaches for nonstationary signal analysis of rotating machinery. The main idea of order analysis is to map the time-varying frequency components into constant ones. Inspired by this idea, we propose a new order spectrum analysis method to exploit the unique property of iterative generalized demodulation in converting arbitrary instantaneous frequency trajectories of multi-component signals into constant frequency lines on the time-frequency plane. This new method is completely algorithm-based and tachometer/encoder-free, thus easy to implement. It does not involve equi-angular resampling commonly required by most order tracking methods and is hence free from the decimation and/or interpolation error. The proposed order analysis method can eliminate the time-variation effect of frequency and thus can effectively reveal the harmonic order constituents of nonstationary multi-component signals. However, the planetary gearbox vibration signals also lead to complex sideband orders. As such, we further propose to analyze the order spectrum of amplitude envelope. This will eliminate the complex sideband orders in the order spectrum of original signals, leading to a substantially simplified and more reliable gear characteristic frequency identification process. Nevertheless, the gear and/or planet carrier rotating frequency orders, which are irrelevant to gear fault, may still exist. To avoid possible misleading results due to such frequency orders, we also propose to analyze the order spectrum of instantaneous frequency. Theoretically, the peaks present in frequency order spectrum directly correspond to the gear characteristic frequency orders, which can be used to extract gear fault signature more explicitly. The proposed

  6. 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.

  7. 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

  8. 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.

  9. 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

  10. A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

    NASA Astrophysics Data System (ADS)

    Al-Bugharbee, Hussein; Trendafilova, Irina

    2016-05-01

    This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis, which uses a pattern-recognition process. The first stage is principally based on linear time invariant autoregressive modelling. One of the main contributions of this investigation is the development of a pretreatment signal analysis procedure which subjects the signal to noise cleaning by singular spectrum analysis and then stationarisation by differencing. So the signal is transformed to bring it close to a stationary one, rather than complicating the model to bring it closer to the signal. This type of pretreatment allows the use of a linear time invariant autoregressive model and improves its performance when the original signals are non-stationary. This contribution is at the heart of the proposed method, and the high accuracy of the diagnosis is a result of this procedure. The methodology emphasises the importance of preliminary noise cleaning and stationarisation. And it demonstrates that the information needed for fault identification is contained in the stationary part of the measured signal. The methodology is further validated using three different experimental setups, demonstrating very high accuracy for all of the applications. It is able to correctly classify nearly 100 percent of the faults with regard to their type and size. This high accuracy is the other important contribution of this methodology. Thus, this research suggests a highly accurate methodology for rolling element bearing fault diagnosis which is based on relatively simple procedures. This is also an advantage, as the simplicity of the individual processes ensures easy application and the possibility for automation of the entire process.

  11. 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.

  12. Wavelet transform-based fault diagnosis and line selection method of small current grounding system

    NASA Astrophysics Data System (ADS)

    Yang, Ni; Zhang, Shuqing; Zhang, Liguo; Zhang, Kexin; Sun, Lingyun

    2008-12-01

    Small current grounding system is the system that the neutral point doesn't ground or grounds across the arc suppressing coils, which has been applied commonly in distribution system of many countries. As the grounding fault occurs, current is the one caused by capacity of circuit to ground only and it is rather small. The status of fault is complexity, e.g., the electromagnet interferes together with the amplified impact of zero-order loops to high-order singularity waves and various temporary variables. All these result in the lower ratio of the fault element signal to noise caused by zero-order current. In this paper, the position of signal singularity and the magnitude of the singularity degree are analyzed based on the variable focus character of wavelet, and the time fault occurs is then determined. The series db wavelet with close sustain is adopted, and the line selection is according to the zero-order voltage of the generatrix and the current of various outlet line. It is proved by the experiment that the fault circuit diagnosis method based on wavelet analysis to the character of temporary status of single-phase grounding fault plays an important role to a finer line selection.

  13. Vibration diagnosis and remediation design for an x-ray optics stitching interferometer system.

    SciTech Connect

    Preissner, C.; Assoufid, L.; Shu, D.; Experimental Facilities Division

    2004-01-01

    The Advanced Photon Source (APS) x-ray optics Metrology Laboratory currently operates a small-aperture Wyko laser interferometer in a stitching configuration. While the stitching configuration allows for easier surface characterization of long x-ray substrates and mirrors, the addition of mechanical components for optic element translation can compromise the ultimate measurement performance of the interferometer. A program of experimental vibration measurements, quantifying the laboratory vibration environment and identifying interferometer support-system behavior, has been conducted. Insight gained from the ambient vibration assessment and modal analysis has guided the development of a remediation technique. Discussion of the problem diagnosis and possible solutions are presented in this paper.

  14. 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.

  15. A multi-fault diagnosis method for sensor systems based on principle component analysis.

    PubMed

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

    2010-01-01

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

  16. 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

  17. 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.

  18. 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

  19. 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

  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. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

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

    NASA Astrophysics Data System (ADS)

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

    2011-07-01

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

  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. 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.

  10. [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

  11. 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.

  12. 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.

  13. 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.

  14. Novel Gauss-Hermite integration based Bayesian inference on optimal wavelet parameters for bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Dong; Tsui, Kwok-Leung; Zhou, Qiang

    2016-05-01

    Rolling element bearings are commonly used in machines to provide support for rotating shafts. Bearing failures may cause unexpected machine breakdowns and increase economic cost. To prevent machine breakdowns and reduce unnecessary economic loss, bearing faults should be detected as early as possible. Because wavelet transform can be used to highlight impulses caused by localized bearing faults, wavelet transform has been widely investigated and proven to be one of the most effective and efficient methods for bearing fault diagnosis. In this paper, a new Gauss-Hermite integration based Bayesian inference method is proposed to estimate the posterior distribution of wavelet parameters. The innovations of this paper are illustrated as follows. Firstly, a non-linear state space model of wavelet parameters is constructed to describe the relationship between wavelet parameters and hypothetical measurements. Secondly, the joint posterior probability density function of wavelet parameters and hypothetical measurements is assumed to follow a joint Gaussian distribution so as to generate Gaussian perturbations for the state space model. Thirdly, Gauss-Hermite integration is introduced to analytically predict and update moments of the joint Gaussian distribution, from which optimal wavelet parameters are derived. At last, an optimal wavelet filtering is conducted to extract bearing fault features and thus identify localized bearing faults. Two instances are investigated to illustrate how the proposed method works. Two comparisons with the fast kurtogram are used to demonstrate that the proposed method can achieve better visual inspection performances than the fast kurtogram.

  15. Research on test techniques of fault forewarning and diagnosis for high-end CNC machine tool

    NASA Astrophysics Data System (ADS)

    Ren, Bin; Xu, Xiaoli

    2010-12-01

    With the progress of modern science and technique, the manufacturing industry becomes more and more complex and intelligent. It is the challenge for stable, safe running and economical efficiency of machining equipment such as high-quality numerical control because of its complex structure and integrated functions, and the potential faults are easy to happen. How to ensure the equipment runs stably and reliably becomes the key problem to improve the machining precision and efficiency. In order to prolong the average no-fault time, stable running and machining precision of numerical control, it is very important to make relative test and research on acquisition of data of numerical control sample and establishment of sample database. Take high-end CNC Machine Tool for example, the research on test techniques for data acquisition of sample of typical functional parts in CNC Machine Tool will be made and test condition will be set up; the test methods for sample acquisition on running state monitoring and fault forewarning and diagnosis of numerical control is determined; the test platform for typical functional parts of numerical control is established; the sample database is designed and the sample base and knowledge mode is made. The test and research provide key test techniques to disclosure dynamic performance of fault and precision degeneration, and analyze the impact factors to fault.

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

    NASA Astrophysics Data System (ADS)

    Germen, Emin; Başaran, 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.

  17. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment

    NASA Astrophysics Data System (ADS)

    Gu, F.; Shao, Y.; Hu, N.; Naid, A.; Ball, A. D.

    2011-01-01

    This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor based on bispectrum analysis. The theoretical basis is developed to understand the nonlinear characteristics of current signals when the motor undertakes a varying load under different faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is then adopted to combine both lower sidebands and higher sidebands simultaneously and hence characterise the current signal more accurately. Based on this new bispectrum analysis a more effective diagnostic feature, namely normalised bispectral peak, is developed for fault classification. In association with the kurtosis value of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from the other fault cases and different degrees of discharge valve leakage and inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment.

  18. Pumping system fault detection and diagnosis utilizing pattern recognition and fuzzy inference techniques

    SciTech Connect

    Singer, R.M.; Gross, K.C. ); Humenik, K.E. . Dept. of Computer Science)

    1991-01-01

    An integrated fault detection and diagnostic system with a capability of providing extremely early detection of disturbances in a process through the analysis of the stochastic content of dynamic signals is described. The sequential statistical analysis of the signal noise (a pattern-recognition technique) that is employed has been shown to provide the theoretically shortest sampling time to detect disturbances and thus has the potential of providing incipient fault detection information to operators sufficiently early to avoid forced process shutdowns. This system also provides a diagnosis of the cause of the initiating fault(s) by a physical-model-derived rule-based expert system in which system and subsystem state uncertainties are handled using fuzzy inference techniques. This system has been initially applied to the monitoring of the operational state of the primary coolant pumping system on the EBR-II nuclear reactor. Early validation studies have shown that a rapidly developing incipient fault on centrifugal pumps can be detected well in advance of any changes in the nominal process signals. 17 refs., 6 figs.

  19. EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine

    NASA Astrophysics Data System (ADS)

    Li, Yujun; Tse, Peter W.; Yang, Xin; Yang, Jianguo

    2010-01-01

    The accuracy of fault diagnostic systems for diesel engine-type generators relies on a comparison of the currently extracted sensory features with those captured during normal operation or the so-called "baseline." However, the baseline is not easily obtained without the required expertise. Even worse, in an attempt to save costs, many of the diesel engine generators in manufacturing plants are second hand or have been purchased from unknown suppliers, meaning that the baseline is unknown. In this paper, a novel vibration-based fault diagnostic method is developed to identify the vital components of a diesel engine that have abnormal clearance. The advantage of this method is that it does not require the comparison of current operating parameters to those collected as the baseline. First, the nominal baseline is obtained via theoretical modeling rather than being actually captured from the sensory signals in a healthy condition. The abnormal clearance is then determined by inspecting the timing of impacts created by the components that had abnormal clearance during operation. To detect the timing of these impacts from vibration signals accurately, soft-re-sampling and empirical mode decomposition (EMD) techniques are employed. These techniques have integrated with our proposed ranged angle (RA) analysis to form a new ranged angle-empirical mode decomposition method (RA-EMD). To verify the effectiveness of the RA-EMD in detecting the impacts and their times of occurrence, their induced vibrations are collected from a series of generators under normal and faulty engine conditions. The results show that this method is capable of extracting the impacts induced by vibrations and is able to determine their times of occurrence accurately even when the impacts have been overwhelmed by other unrelated vibration signals. With the help of the RA-EMD, clearance-related faults, such as incorrect open and closed valve events, worn piston rings and liners, etc., become detectable

  20. Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment

    NASA Astrophysics Data System (ADS)

    Pan, Jun; Chen, Jinglong; Zi, Yanyang; Li, Yueming; He, Zhengjia

    2016-05-01

    Due to the multi-modulation feature in most of the vibration signals, the extraction of embedded fault information from condition monitoring data for mechanical fault diagnosis still is not a relaxed task. Despite the reported achievements, Wavelet transform follows the dyadic partition scheme and would not allow a data-driven frequency partition. And then Empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing signal into mono-components under an orthogonal basis and non-dyadic partition scheme. However, the pre-defined segment way of Fourier spectrum without dependence on analyzed signals may result in inaccurate mono-component identification. In this paper, the modified EWT (MEWT) method via data-driven adaptive Fourier spectrum segment is proposed for mechanical fault identification. First, inner product is calculated between the Fourier spectrum of analyzed signal and Gaussian function for scale representation. Then, adaptive spectrum segment is achieved by detecting local minima of the scale representation. Finally, empirical modes can be obtained by adaptively merging mono-components based on their envelope spectrum similarity. The adaptively extracted empirical modes are analyzed for mechanical fault identification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.

  1. 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.

  2. An enhanced Kurtogram method for fault diagnosis of rolling element bearings

    NASA Astrophysics Data System (ADS)

    Wang, Dong; Tse, Peter W.; Tsui, Kwok Leung

    2013-02-01

    The Kurtogram is based on the kurtosis of temporal signals that are filtered by the short-time Fourier transform (STFT), and has proved useful in the diagnosis of bearing faults. To extract transient impulsive signals more effectively, wavelet packet transform is regarded as an alternative method to STFT for signal decomposition. Although kurtosis based on temporal signals is effective under some conditions, its performance is low in the presence of a low signal-to-noise ratio and non-Gaussian noise. This paper proposes an enhanced Kurtogram, the major innovation of which is kurtosis values calculated based on the power spectrum of the envelope of the signals extracted from wavelet packet nodes at different depths. The power spectrum of the envelope of the signals defines the sparse representation of the signals and kurtosis measures the protrusion of the sparse representation. This enhanced Kurtogram helps to determine the location of resonant frequency bands for further demodulation with envelope analysis. The frequency signatures of the envelope signal can then be used to determine the type of fault that has affected a bearing by identifying its characteristic frequency. In many cases, discrete frequency noise always exists and may mask the weak bearing faults. It is usually preferable to remove such discrete frequency noise by using autoregressive filtering before the enhanced Kurtogram is performed. At last, we used a number of simulated bearing fault signals and three real bearing fault signals obtained from an experimental motor to validate the efficiency of these proposed modifications. The results show that both the proposed method and the enhanced Kurtogram are effective in the detection of various bearing faults.

  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. 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

  5. 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

  6. 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.

  7. Stochastic resonance with Woods-Saxon potential for rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Lu, Siliang; He, Qingbo; Kong, Fanrang

    2014-04-01

    This paper proposes a weak signal detection strategy for rolling element bearing fault diagnosis by investigating a new mechanism to realize stochastic resonance (SR) based on the Woods-Saxon (WS) potential. The WS potential has the distinct structure with smooth potential bottom and steep potential wall, which guarantees a stable particle motion within the potential and avoids the unexpected noises for the SR system. In the Woods-Saxon SR (WSSR) model, the output signal-to-noise ratio (SNR) can be optimized just by tuning the WS potential's parameters, which delivers the most significant merit that the limitation of small parameter requirement of the classical bistable SR can be overcome, and thus a wide range of driving frequencies can be detected via the SR model. Furthermore, the proposed WSSR model is also insensitive to the noise, and can detect the weak signals with different noise levels. Additionally, the WS potential can be designed accurately due to its parameter independence, which implies that the proposed method can be matched to different input signals adaptively. With these properties, the proposed weak signal detection strategy is indicated to be beneficial to rolling element bearing fault diagnosis. Both the simulated and the practical bearing fault signals verify the effectiveness and efficiency of the proposed WSSR method in comparison with the traditional bistable SR method.

  8. 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

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

    PubMed

    Flores, Agustín; Quiles, Eduardo; García, 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

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis.

    PubMed

    Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

    2016-01-01

    Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods. PMID:27092509

  17. 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.

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

    SciTech Connect

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

    2014-09-01

    This research addresses how alarm systems can increase operator performance within nuclear power plant operations. The experiment examined the effects of two types of alarm systems (two-state and three-state alarms) on alarm compliance and diagnosis for two types of faults differing in complexity. We hypothesized the use of three-state alarms would improve performance in alarm recognition and fault diagnoses over that of two-state alarms. Sensitivity and criterion based on the Signal Detection Theory were used to measure performance. We further hypothesized that operator trust would be highest when using three-state alarms. The findings from this research showed participants performed better and had more trust in three-state alarms compared to two-state alarms. Furthermore, these findings have significant theoretical implications and practical applications as they apply to improving the efficiency and effectiveness of nuclear power plant operations.

  19. 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

    2016-04-01

    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.

  20. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

    PubMed Central

    Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

    2016-01-01

    Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods. PMID:27092509

  1. 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

  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. 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

  4. A logic based expert system (LBES) for fault diagnosis of power system

    SciTech Connect

    Park, Y.M.; Kim, G.W.; Sohn, J.M.

    1997-02-01

    This paper proposes an expert system for fault diagnosis of power system using a new inference method. Expertise is, in this paper, represented by logical implications and converted into a Boolean function. Unlike conventional rule-based expert systems, the expertise is converted into Prime Implicants (PIs) which are logically complete and sound. Therefore, off-line inference is possible by off-line identification of PIs, which reduces the on-line inference time considerably and makes it possible to utilize the proposed expert system in real-time environment. This paper also presents alarm verification and correction method for relay and circuit breaker (CB) using pre-identified PIs.

  5. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

    NASA Astrophysics Data System (ADS)

    Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

    2016-05-01

    Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

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

    PubMed

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

    2014-01-01

    A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals' separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

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

    SciTech Connect

    Subramanian, S.; Mooney, R.J.

    1996-12-31

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

  8. Vibrations on the Roll - MANA, a Roll Along Array Experiment to map Local Site Effects Across a Fault System

    NASA Astrophysics Data System (ADS)

    Ohrnberger, M.; Scherbaum, F.; Hinzen, K. G.; Reamer, S. K.; Weber, B.

    2001-12-01

    The effects of surficial geology on seismic motion (site effects) are considered one of the major controlling factors to the damage distribution during earthquakes. Qualitative and quantitative estimates of local site amplifications provide important information for the identification of potential high risk areas. In this context, the analysis of ambient vibrations is an attractive tool for the mapping of site conditions. It is a low-cost alternative to expensive active seismic experiments or geophysical well-logging and especially well suited for the use within urban areas. Within the MANA experiment we conducted ambient vibration measurements at roughly 100 sites in the Lower Rhine Embayment (NW-Germany) to test various aspects of site effect determination, especially the feasibility of a roll along technique. A total of 13 three-component seismometers (5s corner period) have been used in a linear array configuration (station distance ~100 m). At all times during the roll-along experiment at least 8 stations (mostly 10) were operating simultaneously, meanwhilst the other stations were moved from the rear to the front of the line and re-installed. Thus, a total progress of almost 10 km could be obtained within two days. The line stretched across the NW-SE striking Erft fault system, one of the major faults in the eastern part of the Lower Rhine Embayment. The thickness of cenozoic soft-sediments overlying the basement of paleozoic age increases at the individual branches of the fault in abrupt steps of uncertain magnitude from around 200 m in the east to almost 1000 m in the west. The results of single station horizontal to vertical spectral ratios (HVSR) along the line are presented as well as the spatial evolution of local dispersion curves obtained from a slantstack analysis (SSA). The spatial variation of features along the line in both the HVSR and SSA are discussed in terms of sedimentary thickness and modifications of the wavefield properties of the ambient

  9. 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

  10. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

    Empirical methods for fault diagnosis usually entail a process of supervised training based on a set of examples of signal evolutions "labeled" with the corresponding, known classes of fault. However, in practice, the signals collected during plant operation may be, very often, "unlabeled", i.e., the information on the corresponding type of occurred fault is not available. To cope with this practical situation, in this paper we develop a methodology for the identification of transient signals showing similar characteristics, under the conjecture that operational/faulty transient conditions of the same type lead to similar behavior in the measured signals evolution. The methodology is founded on a feature extraction procedure, which feeds a spectral clustering technique, embedding the unsupervised fuzzy C-means (FCM) algorithm, which evaluates the functional similarity among the different operational/faulty transients. A procedure for validating the plausibility of the obtained clusters is also propounded based on physical considerations. The methodology is applied to a real industrial case, on the basis of 148 shut-down transients of a Nuclear Power Plant (NPP) steam turbine.

  12. 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

  13. 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.

  14. 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.

  15. Time-frequency manifold for nonlinear feature extraction in machinery fault diagnosis

    NASA Astrophysics Data System (ADS)

    He, Qingbo

    2013-02-01

    Time-frequency feature is beneficial to representation of non-stationary signals for effective machinery fault diagnosis. The time-frequency distribution (TFD) is a major tool to reveal the synthetic time-frequency pattern. However, the TFD will also face noise corruption and dimensionality reduction issues in engineering applications. This paper proposes a novel nonlinear time-frequency feature based on a time-frequency manifold (TFM) technique. The new TFM feature is generated by mainly addressing manifold learning on the TFDs in a reconstructed phase space. It combines the non-stationary information and the nonlinear information of analyzed signals, and hence exhibits valuable properties. Specifically, the new feature is a quantitative low-dimensional representation, and reveals the intrinsic time-frequency pattern related to machinery health, which can effectively overcome the effects of noise and condition variance issues in sampling signals. The effectiveness and the merits of the proposed TFM feature are confirmed by case study on gear wear diagnosis, bearing defect identification and defect severity evaluation. Results show the value and potential of the new feature in machinery fault pattern representation and classification.

  16. 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.

  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. 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.

  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. Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach

    NASA Astrophysics Data System (ADS)

    Shi, Huaitao; Liu, Jianchang; Wu, Yuhou; Zhang, Ke; Zhang, Lixiu; Xue, Peng

    2016-04-01

    It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.

  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 detection, isolation, and diagnosis of status self-validating gas sensor arrays

    NASA Astrophysics Data System (ADS)

    Chen, Yin-sheng; Xu, Yong-hui; Yang, Jing-li; Shi, Zhen; Jiang, Shou-da; Wang, Qi

    2016-04-01

    The traditional gas sensor array has been viewed as a simple apparatus for information acquisition in chemosensory systems. Gas sensor arrays frequently undergo impairments in the form of sensor failures that cause significant deterioration of the performance of previously trained pattern recognition models. Reliability monitoring of gas sensor arrays is a challenging and critical issue in the chemosensory system. Because of its importance, we design and implement a status self-validating gas sensor array prototype to enhance the reliability of its measurements. A novel fault detection, isolation, and diagnosis (FDID) strategy is presented in this paper. The principal component analysis-based multivariate statistical process monitoring model can effectively perform fault detection by using the squared prediction error statistic and can locate the faulty sensor in the gas sensor array by using the variables contribution plot. The signal features of gas sensor arrays for different fault modes are extracted by using ensemble empirical mode decomposition (EEMD) coupled with sample entropy (SampEn). The EEMD is applied to adaptively decompose the original gas sensor signals into a finite number of intrinsic mode functions (IMFs) and a residual. The SampEn values of each IMF and the residual are calculated to reveal the multi-scale intrinsic characteristics of the faulty sensor signals. Sparse representation-based classification is introduced to identify the sensor fault type for the purpose of diagnosing deterioration in the gas sensor array. The performance of the proposed strategy is compared with other different diagnostic approaches, and it is fully evaluated in a real status self-validating gas sensor array experimental system. The experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID of status self-validating gas sensor arrays.

  4. Fault detection, isolation, and diagnosis of status self-validating gas sensor arrays.

    PubMed

    Chen, Yin-Sheng; Xu, Yong-Hui; Yang, Jing-Li; Shi, Zhen; Jiang, Shou-da; Wang, Qi

    2016-04-01

    The traditional gas sensor array has been viewed as a simple apparatus for information acquisition in chemosensory systems. Gas sensor arrays frequently undergo impairments in the form of sensor failures that cause significant deterioration of the performance of previously trained pattern recognition models. Reliability monitoring of gas sensor arrays is a challenging and critical issue in the chemosensory system. Because of its importance, we design and implement a status self-validating gas sensor array prototype to enhance the reliability of its measurements. A novel fault detection, isolation, and diagnosis (FDID) strategy is presented in this paper. The principal component analysis-based multivariate statistical process monitoring model can effectively perform fault detection by using the squared prediction error statistic and can locate the faulty sensor in the gas sensor array by using the variables contribution plot. The signal features of gas sensor arrays for different fault modes are extracted by using ensemble empirical mode decomposition (EEMD) coupled with sample entropy (SampEn). The EEMD is applied to adaptively decompose the original gas sensor signals into a finite number of intrinsic mode functions (IMFs) and a residual. The SampEn values of each IMF and the residual are calculated to reveal the multi-scale intrinsic characteristics of the faulty sensor signals. Sparse representation-based classification is introduced to identify the sensor fault type for the purpose of diagnosing deterioration in the gas sensor array. The performance of the proposed strategy is compared with other different diagnostic approaches, and it is fully evaluated in a real status self-validating gas sensor array experimental system. The experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID of status self-validating gas sensor arrays. PMID:27131696

  5. 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

  6. 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

  7. 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.

  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. 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

  10. 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.

  11. Roller bearing fault diagnosis based on nonlinear redundant lifting wavelet packet analysis.

    PubMed

    Gao, Lixin; Yang, Zijing; Cai, Ligang; Wang, Huaqing; Chen, Peng

    2011-01-01

    A nonlinear redundant lifting wavelet packet algorithm was put forward in this study. For the node signals to be decomposed in different layers, predicting operators and updating operators with different orders of vanishing moments were chosen to take norm l(p) of the scale coefficient and wavelet coefficient acquired from decomposition, the predicting operator and updating operator corresponding to the minimal norm value were used as the optimal operators to match the information characteristics of a node. With the problems of frequency alias and band interlacing in the analysis of redundant lifting wavelet packet being investigated, an improved algorithm for decomposition and node single-branch reconstruction was put forward. The normalized energy of the bottommost decomposition node coefficient was calculated, and the node signals with the maximal energy were extracted for demodulation. The roller bearing faults were detected successfully with the improved analysis on nonlinear redundant lifting wavelet packet being applied to the fault diagnosis of the roller bearings of the finishing mills in a plant. This application proved the validity and practicality of this method. PMID:22346576

  12. 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

  13. 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

  14. 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

  15. Vibration syndrome diagnosis using a cooling test verified by computerized photoplethysmography.

    PubMed

    Dyszkiewicz, Andrzej; Tendera, Michał

    2006-04-01

    This study addresses the problem of vibration syndrome diagnosis by means of a cooling test verified by photoplethysmography. Measurement was taken on a small area on the fingertip plexus in which many arterio-venous anastomoses are present. In the opinion of many authors, flow disorders in this area are more typical for developing vibration syndrome than changes in the micro vessels. The study group comprised 128 subjects (58 women aged 40.9 +/- 5.4 years and 70 men aged 38.7 +/- 8.8 years) exposed to vibration. The control group consisted of 41 people (20 women aged 39.6 +/- 7.3 years and 21 men aged 39.3 +/- 6.4 years) who were not exposed to vibration. The patients were examined by a questionnaire and then a vibration perception threshold test and a cooling test were performed. The cooling test was verified both visually and using the computer method. Measurement data (S1, S2 and A) for each patient were obtained from averaging three pulse graphs. We departed from an average of 60 graphs (and more), the standard established in the literature, because of the cooling test specification, which causes huge thermodynamic parameter changeability in the plexus mass of the small finger under pulse waves coming one after another. A longer measurement time will reflect the thermal drift of the tested area in a direction to compensate for the reduced temperature. In the control group, all subjects showed an increase in planimetric indicators during the cooling test verified by computerized photoplethysmography. In the study group visual verification of the cooling test was positive in eight cases (6.2%) and the vibration perception threshold test was positive in seven cases (5.5%), but in computerized photoplethysmography the planimetric indicators decreased after cooling in 87 (67.4%) cases. Computer photoplethysmography is highly specific and shows greater sensitivity in detecting preclinical forms of vascular-type vibration syndrome when compared with palesthesiometry

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

    PubMed

    Aydin, Ilhan; Karakose, Mehmet; Akin, Erhan

    2014-03-01

    Although reconstructed phase space is one of the most powerful methods for analyzing a time series, it can fail in fault diagnosis of an induction motor when the appropriate pre-processing is not performed. Therefore, boundary analysis based a new feature extraction method in phase space is proposed for diagnosis of induction motor faults. The proposed approach requires the measurement of one phase current signal to construct the phase space representation. Each phase space is converted into an image, and the boundary of each image is extracted by a boundary detection algorithm. A fuzzy decision tree has been designed to detect broken rotor bars and broken connector faults. The results indicate that the proposed approach has a higher recognition rate than other methods on the same dataset. PMID:24296116

  17. 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

  18. Generalized stepwise demodulation transform and synchrosqueezing for time-frequency analysis and bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Shi, Juanjuan; Liang, Ming; Necsulescu, Dan-Sorin; Guan, Yunpeng

    2016-04-01

    for bearing condition monitoring under variable speed conditions include: (a) it can simultaneously improve energy concentration level of signals of interest and remove interferences in the TFR, (b) it is resampling-free and hence can avoid the resampling related errors, and (c) it yields instantaneous frequencies for fault and shaft rotation and thus can carry out both fault detection and diagnosis tasks.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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%.

  4. 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

  5. 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.

  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. [Application of ICP-AES in automotive hydraulic power steering system fault diagnosis].

    PubMed

    Chen, Li-Dan

    2013-01-01

    The authors studied the innovative applications of the inductively coupled plasma-atomic emission spectrometry in automotive hydraulic power steering system fault diagnosis. After having determined Fe, Cu and Al content in the four groups of Buick Regal 2.4 main metal power-steering fluid whose travel course was respectively 2-9 thousand kilometers, 11-18 thousand kilometers, 22-29 thousandkilometers, and 31-40 thousand kilometers, and the database of primary metal content in the Buick Regal 2.4 different mileage power-steering fluid was established. The research discovered that the main metal content increased with increasing mileage and its normal level is between the two trend lines. Determination of the power-steering fluid main metal content and comparison with its database value can not only judge the wear condition of the automotive hydraulic power steering system and maintain timely to avoid the traffic accident, but also help the automobile detection and maintenance personnel to diagnose failure reasons without disintegration. This reduced vehicle maintenance costs, and improved service quality. PMID:23586258

  8. Knowledge-based approach to fault diagnosis and control in distributed process environments

    NASA Astrophysics Data System (ADS)

    Chung, Kwangsue; Tou, Julius T.

    1991-03-01

    This paper presents a new design approach to knowledge-based decision support systems for fault diagnosis and control for quality assurance and productivity improvement in automated manufacturing environments. Based on the observed manifestations, the knowledge-based diagnostic system hypothesizes a set of the most plausible disorders by mimicking the reasoning process of a human diagnostician. The data integration technique is designed to generate error-free hierarchical category files. A novel approach to diagnostic problem solving has been proposed by integrating the PADIKS (Pattern-Directed Knowledge-Based System) concept and the symbolic model of diagnostic reasoning based on the categorical causal model. The combination of symbolic causal reasoning and pattern-directed reasoning produces a highly efficient diagnostic procedure and generates a more realistic expert behavior. In addition, three distinctive constraints are designed to further reduce the computational complexity and to eliminate non-plausible hypotheses involved in the multiple disorders problem. The proposed diagnostic mechanism, which consists of three different levels of reasoning operations, significantly reduces the computational complexity in the diagnostic problem with uncertainty by systematically shrinking the hypotheses space. This approach is applied to the test and inspection data collected from a PCB manufacturing operation.

  9. 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.

  10. Fault diagnosis strategy for incompletely described samples and its application to refrigeration system

    NASA Astrophysics Data System (ADS)

    Ren, Neng; Liang, Jun; Gu, Bo; Han, Hua

    2008-02-01

    Fault diagnosis (FD) plays a very important role in the operation and maintenance of mechanical system and equipment. Existing FD methods are not capable of effectively dealing with incompletely described samples. In this paper, a strategy for FD using the incompletely described samples is presented. It is actualized in two steps, namely the determination of the values of unknown features which is the key step of the presented FD strategy, and the utilization of the regenerated completely described samples to diagnose the system based on support vector machine (SVM) classifiers. And the first step is mainly implemented by the following three sub-steps: (1) with the help of domain knowledge, the similarity transformation matrix of partial problem description (PPD)—problems with incomplete feature description—is generated based on the historical database; (2) the unknown features of the samples are transformed to related known features, through which generates a new retrieval feature vector; (3) the values of unknown features are assigned by the optimal cases which can be retrieved by measuring and comparing similarities between the retrieval feature vector and the completely described samples in the historical database. Finally, the presented FD strategy was applied to a real refrigeration system, and achieved satisfying results.

  11. 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.

  12. 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.

  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. Intermittent chaos and sliding window symbol sequence statistics-based early fault diagnosis for hydraulic pump on hydraulic tube tester

    NASA Astrophysics Data System (ADS)

    Zhao, Zhen; Jia, Mingxing; Wang, Fuli; Wang, Shu

    2009-07-01

    To ensure the safety, continuity of production, make a reasonable maintenance plan, save the cost of maintenance for hydraulic tube tester, it is needed to quickly identify an assignable cause of a fault. This paper is concerned with early fault diagnosis of hydraulic pump which are the heart of hydraulic tube tester. Considering that the signal of the hydraulic pump early fault is a periodic weak signal, an intermittent chaos, sliding window symbol sequence statistics-based method is proposed to detect the early fault of one single piston loose shoes of hydraulic pump on a hydraulic tube tester. The approach presented is based on the insight that the phase transition of chaos oscillator, for example, the Duffing oscillator, is very sensitive to a periodic weak signal having little angular frequency difference with the referential signal of the oscillator. While observing the intermittent chaos phenomenon through figure is not easy for computer, a sliding window symbol sequence statistics is developed to realize real-time computer observation of this phenomenon. Rather more, this paper takes a trick to decreasing the computational complexity of the sliding window symbol sequence statistics method, also analyzes the influences of different window size, depths of the symbol tree on the information entropy. At last, a control limit is introduced to realize automatic early fault alarm. The resultant approach is experimented with data simulated from an AMESim model of hydraulic tube tester. The results indicate that the proposed approach is capable of detecting the signal of hydraulic pump early fault on hydraulic tube tester.

  16. Detection of bearing damage by statistic vibration analysis

    NASA Astrophysics Data System (ADS)

    Sikora, E. A.

    2016-04-01

    The condition of bearings, which are essential components in mechanisms, is crucial to safety. The analysis of the bearing vibration signal, which is always contaminated by certain types of noise, is a very important standard for mechanical condition diagnosis of the bearing and mechanical failure phenomenon. In this paper the method of rolling bearing fault detection by statistical analysis of vibration is proposed to filter out Gaussian noise contained in a raw vibration signal. The results of experiments show that the vibration signal can be significantly enhanced by application of the proposed method. Besides, the proposed method is used to analyse real acoustic signals of a bearing with inner race and outer race faults, respectively. The values of attributes are determined according to the degree of the fault. The results confirm that the periods between the transients, which represent bearing fault characteristics, can be successfully detected.

  17. Application of classification methods in fault detection and diagnosis of inverter fed induction machine drive: a trend towards reliability

    NASA Astrophysics Data System (ADS)

    Delpha, C.; Diallo, D.; El Hachemi Benbouzid, M.; Marchand, C.

    2008-08-01

    The aim of this paper is to present a method of detection and isolation of intermittent misfiring in power switches of a three phase inverter feeding an induction machine drive. The detection and diagnosis procedure is based solely on the output currents of the inverter flowing into the machine windings. The measured currents are transformed in the two dimensional frame obtained with the Concordia transform. The data are then treated by a time-average method. The results even promising lack of accuracy mainly in the fault isolation step. To enhance the fault detection and diagnosis by the use of the information enclosed in the data, a Principal Component Analysis classifier is applied. The detection of a fault occurrence is made by a two-class classifier. The isolation is a two-step approach which uses the Linear Discriminant Analysis; the first is to identify the faulty leg with a three-class classifier and the second one discriminates the faulty power switch. Both methods are evaluated with experimental data and pattern recognition method proves its effectiveness and accuracy in the faulty leg detection and isolation. This article has been submitted as part of “IET Colloquium on Reliability in Electromagnetic Systems”, 24 and 25 May 2007, Paris

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

    PubMed

    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

  19. Diagnosis of Stator-Winding-Turn Faults of Induction Motor by Direct Detection of Negative-Sequence Currents

    NASA Astrophysics Data System (ADS)

    Kato, Toshiji; Inoue, Kaoru; Yoshida, Keisuke

    In an AC motor, the quick detection of an initially small fault is important for preventing any consequent large fault. Various detection approaches have been proposed in previous papers, for example, by the Park vector (PV), AI techniques, wavelet analysis, and negative-sequence analysis. This paper proposes a method for diagnosing the stator-winding faults of an induction motor by the direct detection of its negative-sequence current. Before starting the diagnosis, the asymmetry admittances for the considered fault cases are obtained by analysis or simulation. The amplitude and phase of the positive-sequence voltage, Vp, and of the positive-sequence current, Ip, are extracted from the voltage PV and current PV, respectively. The amplitude and phase of the negative-sequence, In, are extracted from the residue. The asymmetry admittance, Ya, is calculated from In and Vp. When the positive-sequence admittance is known, Ya can also be calculated from Yp, Ip, and In. These steps are repeated for each sample time and the motor condition is diagnosed according to the variations in the Ya values. The simulation and experimental results are also shown and the proposed method is investigated and validated.

  20. 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

  1. 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.

  2. 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).

  3. Fault diagnosis in induction motors using radial magnetic field measurement with an antenna

    NASA Astrophysics Data System (ADS)

    Belkhayat, D.; Romary, R.; El Adnani, M.; Corton, R.; Brudny, J. F.

    2003-09-01

    In this paper the emf induced in a search coil is measured in order to detect faults in an induction motor. Anomalous operations caused by a broken rotor bar or a faulty stator cutting phase are analysed. Starting from a theoretical analysis of the radial field spectrum associated with these faults, the measurement of the corresponding emf in the search antenna is examined. The saturation and harmonic components of the permeance produced by the slotting effect are taken into account. Their interactions are analysed, allowing the identification of the frequencies which are of interest for the detection of stator cutting phase faults in a working induction motor.

  4. [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

  5. 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.

  6. 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

  7. 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

  8. The technique of entropy optimization in motor current signature analysis and its application in the fault diagnosis of gear transmission

    NASA Astrophysics Data System (ADS)

    Chen, Xiaoguang; Liang, Lin; Liu, Fei; Xu, Guanghua; Luo, Ailing; Zhang, Sicong

    2012-05-01

    Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the fault diagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the fault diagnosis and the condition monitoring of machine tools.

  9. 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.

  10. 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 αβ 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

  11. Appropriate IMFs associated with cepstrum and envelope analysis for ball-bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Tsao, Wen-Chang; Pan, Min-Chun

    2014-03-01

    The traditional envelope analysis is an effective method for the fault detection of rolling bearings. However, all the resonant frequency bands must be examined during the bearing-fault detection process. To handle the above deficiency, this paper proposes using the empirical mode decomposition (EMD) to select a proper intrinsic mode function (IMF) for the subsequent detection tools; here both envelope analysis and cepstrum analysis are employed and compared. By virtue of the band-pass filtering nature of EMD, the resonant frequency bands of structure to be measured are captured in the IMFs. As impulses arising from rolling elements striking bearing faults modulate with structure resonance, proper IMFs potentially enable to characterize fault signatures. In the study, faulty ball bearings are used to justify the proposed method, and comparisons with the traditional envelope analysis are made. Post the use of IMFs highlighting faultybearing features, the performance of using envelope analysis and cepstrum analysis to single out bearing faults is objectively compared and addressed; it is noted that generally envelope analysis offers better performance.

  12. 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 αβ 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

  13. Fault diagnosis of a vacuum cleaner motor by means of sound analysis

    NASA Astrophysics Data System (ADS)

    Benko, U.; Petrovčič, J.; Juričić, Đ.; Tavčar, J.; Rejec, J.; Stefanovska, A.

    2004-09-01

    Achieving high quality standards and 100% defect-free deliverables is becoming a trend among manufacturers of household appliances. In that respect, thorough and reliable end-tests represent an important step towards this goal. This paper deals with the design of end-test procedures for vacuum cleaner motors based on sound analysis. It is well known that sound carries important information about the condition of contact surfaces in rotating parts. The paper aims first to provide a thorough analysis of sound sources within the motor. Second, by using simple yet effective signal processing tools, it is shown that with sound analysis alone it is possible to clearly distinguish fault-free motors from those with mechanical faults. Moreover, the proposed algorithm exhibits a certain isolation capability, i.e., it is able to distinguish three clusters of faults. Finally, a summary of experimental results obtained on a sample of 75 motors is provided.

  14. 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

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

    SciTech Connect

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

    1995-05-01

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

  16. 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

  17. Distributed fault diagnosis in a class of interconnected nonlinear uncertain systems

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaodong; Zhang, Qi

    2012-11-01

    In this article, a distributed fault detection and isolation (FDI) method is developed for a class of interconnected nonlinear uncertain systems. In the distributed FDI architecture, a FDI component is designed for each subsystem in the interconnected system. For each subsystem, its corresponding local FDI component is designed by utilising local measurements and certain communicated information from neighbouring FDI components associated with subsystems that are directly interconnected to the particular subsystem under consideration. Under certain assumptions, adaptive thresholds for distributed FDI in each subsystem are derived, ensuring robustness with respect to interactions among subsystems and system modelling uncertainty. Moreover, the fault detectability and isolability conditions are rigorously investigated, characterising the class of faults in each subsystem that are detectable and isolable by the proposed distributed FDI method. Additionally, the stability and learning capability of the local adaptive fault isolation estimators designed for each subsystem is established. A simulation example of interconnected inverted pendulums mounted on carts is used to illustrate the effectiveness of the method.

  18. Reliability of measured data for pH sensor arrays with fault diagnosis and data fusion based on LabVIEW.

    PubMed

    Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

    2013-01-01

    Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study. PMID:24351636

  19. Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

    PubMed Central

    Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

    2013-01-01

    Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study. PMID:24351636

  20. 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.

  1. 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

  2. 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.

  3. Vibration response mechanism of faulty outer race rolling element bearings for quantitative analysis

    NASA Astrophysics Data System (ADS)

    Cui, Lingli; Zhang, Yu; Zhang, Feibin; Zhang, Jianyu; Lee, Seungchul

    2016-03-01

    For the quantitative fault diagnosis of rolling element bearings, a nonlinear vibration model for fault severity assessment of rolling element bearings is established in this study. The outer race defect size parameter is introduced into the dynamic model, and vibration response signals of rolling element bearings under different fault sizes are simulated. The signals are analyzed quantitatively to observe the relationship between vibration responses and fault sizes. The impact points when the ball rolls onto and away from the defect are identified from the vibration response signals. Next, the impact characteristic that reflects the fault severity in rolling element bearings is obtained from the time interval between two impact points. When the width of the bearing fault is small, the signals are presented as clear single impact. The signals gradually become double impacts with increasing size of defects. The vibration signals of a rolling element bearings test rig are measured for different outer race fault sizes. The experimental results agree well with the results from simulations. These results are useful for understanding the vibration response mechanism of rolling element bearings under various degrees of fault severity.

  4. Cutting State Diagnosis for Shearer through the Vibration of Rocker Transmission Part with an Improved Probabilistic Neural Network

    PubMed Central

    Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Zhang, Lin

    2016-01-01

    In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN) and fruit fly optimization algorithm (FOA). The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy. PMID:27058540

  5. Cutting State Diagnosis for Shearer through the Vibration of Rocker Transmission Part with an Improved Probabilistic Neural Network.

    PubMed

    Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Zhang, Lin

    2016-01-01

    In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN) and fruit fly optimization algorithm (FOA). The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy. PMID:27058540

  6. 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.

  7. Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP.

    PubMed

    Ruiz, Magda; Sin, Gürkan; Berjaga, Xavier; Colprim, Jesús; Puig, Sebastià; Colomer, Joan

    2011-01-01

    The main idea of this paper is to develop a methodology for process monitoring, fault detection and predictive diagnosis of a WasteWater Treatment Plant (WWTP). To achieve this goal, a combination of Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) is proposed. First, MPCA is used to reduce the multi-dimensional nature of online process data, which summarises most of the variance of the process data in a few (new) variables. Next, the outputs of MPCA (t-scores, Q-statistic) are provided as inputs (descriptors) to the CBR method, which is employed to identify problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology is a promising tool for automatic diagnosis and real-time warning, which can be used for daily management of plant operation. PMID:22335109

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. Fault Diagnosis of Cantilever Beam Using Finite Element Analysis: A Case Study

    NASA Astrophysics Data System (ADS)

    Murthy, B. S. N.; Ratnam, C.; Kumar, K. A.

    2013-10-01

    Damage prediction in mechanical and structural systems is establishing a prominent role in modern engineering. Vibration based damage methods give ample flexibility to understand the extent of expected damages in the system. Measurement of vibration characteristics like natural frequencies and mode shapes, Fourier responses and transient responses can help in comprehending the present status of a system either by comparing with their baseline equivalents or by formulating residual functions and minimizing them. The minimization of residues is carried out using non-conventional optimization techniques like genetic algorithms. Genetic algorithms being a meta-heuristic method obtain global minimum values with implicitly defined constraints and objective. In all the residual functions considered in this paper, it is assumed that only the stiffness parameters are reduced individually in each element due to the damage. The amount of reduction in each element is an unknown parameter. The approach is attempted with a structural member like beam. Experimental analysis is carried out to test the natural frequencies and mode shapes of the damaged beams from finite element model considered. A cantilever beam with central slot of desired depth is selected and impact hammer analysis is performed to know the variation in modes when compared to undamaged counter part. Results are presented in the form of table and graphs.

  14. Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis

    NASA Astrophysics Data System (ADS)

    Tran, Van Tung; Yang, Bo-Suk; Gu, Fengshou; Ball, Andrew

    2013-07-01

    In this study, a novel fault diagnosis system for rotating machinery using thermal imaging is proposed. This system consists of bi-dimensional empirical mode decomposition (BEMD) for image enhancement, a generalized discriminant analysis (GDA) for feature reduction, and a relevance vector machine (RVM) for fault classification. Firstly, the thermal image obtained from machine conditions is decomposed into intrinsic mode functions (IMFs) by using BEMD. At each decomposed level, the IMF is expanded and fused with the residue by gray-scale transformation and principal component analysis fusion technique, respectively. The enhanced image is then formed by the improved IMFs in reconstruction process. Subsequently, feature extraction is applied for the enhanced images to obtain histogram features which characterize the thermal image and contain useful information for diagnosis. The high dimensionality of the achieved feature set can be reduced by GDA implementation. Moreover, GDA also assists in the increase of the feature cluster separation. Finally, the diagnostic results are performed by RVM. The proposed system is applied and validated with the thermal images of a fault simulator. A comparative study of the classification results obtained from RVM, support vector machines, and adaptive neuro-fuzzy inference system is also performed to appraise the accuracy of these models. The results show that the proposed diagnosis system is capable of improving the classification accuracy and efficiently assisting in rotating machinery fault diagnosis.

  15. 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.

  16. 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

  17. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings

    NASA Astrophysics Data System (ADS)

    Gan, Meng; Wang, Cong; Zhu, Chang`an

    2016-05-01

    A novel hierarchical diagnosis network (HDN) is proposed by collecting deep belief networks (DBNs) by layer for the hierarchical identification of mechanical system. The deeper layer in HDN presents a more detailed classification of the result generated from the last layer to provide representative features for different tasks. A two-layer HDN is designed for a two-stage diagnosis with the wavelet packet energy feature. The first layer is intended to identify fault types, while the second layer is developed to further recognize fault severity ranking from the result of the first layer. To confirm the effectiveness of HDN, two similar networks constructed by support vector machine and back propagation neuron networks (BPNN) are employed to present a comprehensive comparison. The experimental results show that HDN is highly reliable for precise multi-stage diagnosis and can overcome the overlapping problem caused by noise and other disturbances.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2005-12-01

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

  4. An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

    Vibration analysis has been proved to be an effective and powerful tool for the condition monitoring and fault diagnosis of rolling bearings. 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. In recent years, order tracking methods based on time-frequency representation have been proposed for bearing fault detection under speed variation operating conditions. However, the methods are only applicable for offline bearing fault detection. Aiming at the shortcomings of the current tacholess order tracking techniques, an online tacholess order tracking method is proposed in this paper. The proposed method is on the basis of extracting the instantaneous tachometer information from the collected vibration signal itself continuously, and resampling the original signal with equal angle increment. The envelope order spectrum is used for bearing fault identification. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals.

  5. Vibration analysis to improve reliability and reduce failure; Proceedings of the Design Automation Conference, Cincinnati, OH, September 10-13, 1985

    SciTech Connect

    Niskode, P.M.; Doepker, P.E.

    1985-01-01

    Among the topics discussed are: turbomachinery tip rubs and interactive casting resonances; the transverse vibrational characteristics of an externally damaged pipe and performance of vibration monitoring for the prevention of gas turbine airfoil failures. Consideration is also given to: velocity response analysis of a spherical roller bearing; vibration monitoring of large pumps via a remote satellite stations; dynamic edge strain prediction in stiffened honeycomb panels; and fault-diagnosis for turbo-machines by means of vibration monitoring. Additional topics discussed include: early detection and diagnosis of faults rolling element bearings; spectral analysis of damped vibration by means of a modified version of the Prony method and guidelines for forced vibration in machine tools for use in protective maintenance and analysis.

  6. 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.

  7. 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

  8. Accelerometer having integral fault null

    NASA Technical Reports Server (NTRS)

    Bozeman, Richard J., Jr. (Inventor)

    1995-01-01

    An improved accelerometer is introduced. It comprises a transducer responsive to vibration in machinery which produces an electrical signal related to the magnitude and frequency of the vibration; and a decoding circuit responsive to the transducer signal which produces a first fault signal to produce a second fault signal in which ground shift effects are nullified.

  9. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

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

  11. 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.

  12. Fault detection in rotor bearing systems using time frequency techniques

    NASA Astrophysics Data System (ADS)

    Chandra, N. Harish; Sekhar, A. S.

    2016-05-01

    Faults such as misalignment, rotor cracks and rotor to stator rub can exist collectively in rotor bearing systems. It is an important task for rotor dynamic personnel to monitor and detect faults in rotating machinery. In this paper, the rotor startup vibrations are utilized to solve the fault identification problem using time frequency techniques. Numerical simulations are performed through finite element analysis of the rotor bearing system with individual and collective combinations of faults as mentioned above. Three signal processing tools namely Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Hilbert Huang Transform (HHT) are compared to evaluate their detection performance. The effect of addition of Signal to Noise ratio (SNR) on three time frequency techniques is presented. The comparative study is focused towards detecting the least possible level of the fault induced and the computational time consumed. The computation time consumed by HHT is very less when compared to CWT based diagnosis. However, for noisy data CWT is more preferred over HHT. To identify fault characteristics using wavelets a procedure to adjust resolution of the mother wavelet is presented in detail. Experiments are conducted to obtain the run-up data of a rotor bearing setup for diagnosis of shaft misalignment and rotor stator rubbing faults.

  13. Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network

    NASA Astrophysics Data System (ADS)

    Ahmed, M.; Gu, F.; Ball, A.

    2011-07-01

    Reciprocating compressors are widely used in industry for various purposes and faults occurring in them can degrade their performance, consume additional energy and even cause severe damage to the machine. Vibration monitoring techniques are often used for early fault detection and diagnosis, but it is difficult to prescribe a given set of effective diagnostic features because of the wide variety of operating conditions and the complexity of the vibration signals which originate from the many different vibrating and impact sources. This paper studies the use of genetic algorithms (GAs) and neural networks (NNs) to select effective diagnostic features for the fault diagnosis of a reciprocating compressor. A large number of common features are calculated from the time and frequency domains and envelope analysis. Applying GAs and NNs to these features found that envelope analysis has the most potential for differentiating three common faults: valve leakage, inter-cooler leakage and a loose drive belt. Simultaneously, the spread parameter of the probabilistic NN was also optimised. The selected subsets of features were examined based on vibration source characteristics. The approach developed and the trained NN are confirmed as possessing general characteristics for fault detection and diagnosis.

  14. 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

  15. Sparsity-based algorithm for detecting faults in rotating machines

    NASA Astrophysics Data System (ADS)

    He, Wangpeng; Ding, Yin; Zi, Yanyang; Selesnick, Ivan W.

    2016-05-01

    This paper addresses the detection of periodic transients in vibration signals so as to detect faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings. The processed results show that the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect.

  16. 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.

  17. A Doppler transient model based on the laplace wavelet and spectrum correlation assessment for locomotive bearing fault diagnosis.

    PubMed

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

    2013-01-01

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

  18. 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.

  19. 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.

  20. 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.

  1. A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation

    NASA Astrophysics Data System (ADS)

    Huang, Haifeng; Ouyang, Huajiang; Gao, Hongli; Guo, Liang; Li, Dan; Wen, Juan

    2016-06-01

    Detection of incipient degradation demands extracting sensitive features accurately when signal-to-noise ratio (SNR) is very poor, which appears in most industrial environments. Vibration signals of rolling bearings are widely used for bearing fault diagnosis. In this paper, we propose a feature extraction method that combines Blind Source Separation (BSS) and Spectral Kurtosis (SK) to separate independent noise sources. Normal, and incipient fault signals from vibration tests of rolling bearings are processed. We studied 16 groups of vibration signals (which all display an increase in kurtosis) of incipient degradation after they are processed by a BSS filter. Compared with conventional kurtosis, theoretical studies of SK trends show that the SK levels vary with frequencies and some experimental studies show that SK trends of measured vibration signals of bearings vary with the amount and level of impulses in both vibration and noise signals due to bearing faults. It is found that the peak values of SK increase when vibration signals of incipient faults are processed by a BSS filter. This pre-processing by a BSS filter makes SK more sensitive to impulses caused by performance degradation of bearings.

  2. Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals

    NASA Astrophysics Data System (ADS)

    Chen, Jian; Randall, Robert Bond; Peeters, Bart

    2016-06-01

    Artificial Neural Networks (ANNs) have the potential to solve the problem of automated diagnostics of piston slap faults, but the critical issue for the successful application of ANN is the training of the network by a large amount of data in various engine conditions (different speed/load conditions in normal condition, and with different locations/levels of faults). On the other hand, the latest simulation technology provides a useful alternative in that the effect of clearance changes may readily be explored without recourse to cutting metal, in order to create enough training data for the ANNs. In this paper, based on some existing simplified models of piston slap, an advanced multi-body dynamic simulation software was used to simulate piston slap faults with different speeds/loads and clearance conditions. Meanwhile, the simulation models were validated and updated by a series of experiments. Three-stage network systems are proposed to diagnose piston faults: fault detection, fault localisation and fault severity identification. Multi Layer Perceptron (MLP) networks were used in the detection stage and severity/prognosis stage and a Probabilistic Neural Network (PNN) was used to identify which cylinder has faults. Finally, it was demonstrated that the networks trained purely on simulated data can efficiently detect piston slap faults in real tests and identify the location and severity of the faults as well.

  3. 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.

  4. Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals

    PubMed Central

    Tang, Gang; Luo, Ganggang; Zhang, Weihua; Yang, Caijin; Wang, Huaqing

    2016-01-01

    In the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation for the vibration sources estimation, which makes it difficult to extract fault features exactly by ordinary methods in running tests. To improve the effectiveness of compound fault diagnosis in roller bearings, the present paper proposes a new method to solve the underdetermined problem and to extract fault features based on variational mode decomposition. In order to surmount the shortcomings of inadequate signals collected through limited sensors, a vibration signal is firstly decomposed into a number of band-limited intrinsic mode functions by variational mode decomposition. Then, the demodulated signal with the Hilbert transform of these multi-channel functions is used as the input matrix for independent component analysis. Finally, the compound faults are separated effectively by carrying out independent component analysis, which enables the fault features to be extracted more easily and identified more clearly. Experimental results validate the effectiveness of the proposed method in compound fault separation, and a comparison experiment shows that the proposed method has higher adaptability and practicability in separating strong noise signals than the commonly-used ensemble empirical mode decomposition method. PMID:27322268

  5. Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals.

    PubMed

    Tang, Gang; Luo, Ganggang; Zhang, Weihua; Yang, Caijin; Wang, Huaqing

    2016-01-01

    In the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation for the vibration sources estimation, which makes it difficult to extract fault features exactly by ordinary methods in running tests. To improve the effectiveness of compound fault diagnosis in roller bearings, the present paper proposes a new method to solve the underdetermined problem and to extract fault features based on variational mode decomposition. In order to surmount the shortcomings of inadequate signals collected through limited sensors, a vibration signal is firstly decomposed into a number of band-limited intrinsic mode functions by variational mode decomposition. Then, the demodulated signal with the Hilbert transform of these multi-channel functions is used as the input matrix for independent component analysis. Finally, the compound faults are separated effectively by carrying out independent component analysis, which enables the fault features to be extracted more easily and identified more clearly. Experimental results validate the effectiveness of the proposed method in compound fault separation, and a comparison experiment shows that the proposed method has higher adaptability and practicability in separating strong noise signals than the commonly-used ensemble empirical mode decomposition method. PMID:27322268

  6. 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

  7. 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

  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. 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

  10. 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.

  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. 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.

  13. Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data

    NASA Astrophysics Data System (ADS)

    Ocak, Hasan; Loparo, Kenneth A.

    2004-05-01

    This paper presents two separate algorithms for estimating the running speed and the bearing key frequencies of an induction motor using vibration data. Bearing key frequencies are frequencies at which roller elements pass over a defect point. Most frequency domain-based bearing fault detection and diagnosis techniques (e.g. envelope analysis) rely on vibration measurements and the bearing key frequencies. Thus, estimation of the running speed and the bearing key frequencies are required for failure detection and diagnosis. The paper also incorporates the estimation algorithms with the most commonly used bearing fault detection technique, high-frequency demodulation, to detect bearing faults. Experimental data were used to verify the validity of the algorithms. Data were collected through an accelerometer measuring the vibration from the drive-end ball bearing of an induction motor (Reliance Electric 2HP IQPreAlert)-driven mechanical system. Both inner and outer race defects were artificially introduced to the bearing using electrical discharge machining. A linear vibration model was also developed for generating simulated vibration data. The simulated data were also used to validate the performance of the algorithms. The test results proved the algorithms to be very reliable.

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

    PubMed

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

    2015-01-01

    Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets. PMID:25760051

  15. 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

  16. 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/μm 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.

  17. 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.

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

    PubMed Central

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

    2013-01-01

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

  19. 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

  20. 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