These are representative sample records from Science.gov related to your search topic.
For comprehensive and current results, perform a real-time search at Science.gov.
1

Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals  

Microsoft Academic Search

An investigation of the fault diagnosis technique in internal combustion engines based on the visual dot pattern of acoustic and vibration signals is presented in this paper. Acoustic emissions and vibration signals are well known as being able to be used for monitoring the conditions of rotating machineries. Most of the conventional methods for fault diagnosis using acoustic and vibration

Jian-Da Wu; Chao-Qin Chuang

2005-01-01

2

Motor Fault Diagnosis Based on the Vibration Signal Testing and Analysis  

Microsoft Academic Search

Motor's abnormal vibration signal includes its fault information, effective testing and analysis of its vibration signal is the key to realize motor fault diagnosis. Vibration testing and analysis system was put up to realize the acquisition, display, analysis, storage and replay of vibration signal by using NI-WLS9234 Data Acquisition Card in this paper. Call the analysis function of wavelet in

Wu Zhaoxia; Li Fen; Yan Shujuan; Wang Bin

2009-01-01

3

A Study of Rolling-Element Bearing Fault Diagnosis Using Motor's Vibration and  

E-print Network

the powerful capability of vibration analysis in the bearing point-defect fault diagnosis under stationary operation. The current analysis showed a subtle capability in diagnosis of point- defect faults depending features from measured sta- tionary time-domain data. Theoretically, the single-point fault characteristic

Yang, Zhenyu

4

Vibration Condition Monitoring Techniques for Fault Diagnosis of Electromotor with 1.5 Kw Power  

NASA Astrophysics Data System (ADS)

Vibration analysis is the main conditions monitoring techniques for machinery maintenance and fault diagnosis. This technique has its unique advantages and disadvantages associated with the monitoring and fault diagnosis of machinery. When this technique is conducted independently, only a portion of machine faults are typically diagnosed. However, practical experience has shown that this technique in a machine condition monitoring program provides useful reliable information, bringing significant cost benefits to industry. The objective of this research is to investigate the correlation between vibration analysis and fault diagnosis. This was achieved by vibration analysis and investigating different operating conditions of an experimental electromotor. The electromotor was initially run under normal operating conditions as a comparative test. A series of tests were then conducted corresponding to different operating condition. Our varieties were speed of electromotor at three levels, respectively 500, 1000 and 1500 rpm. We did three faults in our electromotor; there were misalignment, looseness and bad bearing. We coupled our electromotor to the variable blade fan and applied several load on that by changing the number of blade of fan. We have chosen 2, 6 and 10 blades fan to apply three different loads on our electromotor. Vibration data was regularly collected. Numerical data produced by vibration analysis were compared with vibration spectra in normal condition of healthy machine, in order to quantify the effectiveness of the vibration condition monitoring technique. The results from this paper have given more understanding on the dependent roles of vibration analysis in predicting and diagnosing machine faults.

Mohamadi Monavar, H.; Ahmadi, H.; Mohtasebi, S. S.; Hasani, S.

5

Fault Diagnosis of Diesel Engine Using Vibration Signals  

NASA Astrophysics Data System (ADS)

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

Wang, Fengli; Duan, Shulin

6

Vibration analysis with lifting scheme and generalized cross validation in fault diagnosis of water hydraulic system  

Microsoft Academic Search

This paper presents a novel method to analyze the vibration signals in the fault diagnosis of water hydraulic motor. The method of feature extraction from the vibration signals of the water hydraulic motor based on the second-generation wavelet is investigated. The second-generation wavelet consists of a lifting scheme. The algorithm and method of multi-decomposition based on the lifting scheme for

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

2007-01-01

7

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

NASA Technical Reports Server (NTRS)

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.

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

1996-01-01

8

Fault Diagnosis of Diesel Engine Using Vibration Signals  

Microsoft Academic Search

\\u000a Aiming at the characteristics of the surface vibration signals measured from the diesel engine, a novel method combining local\\u000a wave decomposition (LWD) and lifting wavelet denoising is proposed, and is used for feature extraction and condition evaluation\\u000a of diesel engine vibration signals. Firstly, the original data is preprocessed using the lifting wavelet transformation to\\u000a suppress abnormal interference of noise, and

Fengli Wang; Shulin Duan

2011-01-01

9

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

NASA Astrophysics Data System (ADS)

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.

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

2013-04-01

10

Fault diagnosis  

NASA Technical Reports Server (NTRS)

The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

Abbott, Kathy

1990-01-01

11

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

NASA Astrophysics Data System (ADS)

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.

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

2014-02-01

12

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

NASA Astrophysics Data System (ADS)

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

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

2007-02-01

13

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

PubMed Central

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

Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

2014-01-01

14

Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor  

Microsoft Academic Search

There has been an increasing application of water hydraulics in industries due to growing concern on the environmental, health and safety issues. The fault diagnosis of water hydraulic motor is important for improving water hydraulic system reliability and performance. In this paper, fault diagnosis of water hydraulic motor in water hydraulic system is investigated based on adaptive wavelet analysis. A

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

2006-01-01

15

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

E-print Network

in the combustion process of an internal combus­ tion piston engine, malfunctions, this malfunction may be reflected way of meeting this challenge is to physically induce faults in a diesel engine, to collect data normal and four fault conditions were obtained by physically inducing subtle faults in a diesel engine

Sharkey, Amanda

16

APPLICATION OF WAVELETS TO GEARBOX VIBRATION SIGNALS FOR FAULT DETECTION  

Microsoft Academic Search

The wavelet transform is used to represent all possible types of transients in vibration signals generated by faults in a gearbox. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal from a helicopter gearbox is used to demonstrate the application of the suggested wavelet by a simple computer algorithm. The

W. J. Wang; P. D. McFadden

1996-01-01

17

Isolability of faults in sensor fault diagnosis  

NASA Astrophysics Data System (ADS)

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.

Sharifi, Reza; Langari, Reza

2011-10-01

18

Fault diagnosis of analog circuits  

SciTech Connect

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.

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

1985-08-01

19

Fault Diagnosis utilizing Structural Analysis  

Microsoft Academic Search

When designing model-based fault-diagnosis systems, the use of consistency relations (also called e.g. parity relations) is a common choice. Dierent subsets are sensi- tive to dierent subsets of faults, and thereby isolation can be achieved. This paper presents an algorithm for nding a small set of submodels that can be used to derive con- sistency relations with highest possible diagnosis

Mattias Krysander; Mattias Nyberg

20

Applications of Fault Detection in Vibrating Structures  

NASA Technical Reports Server (NTRS)

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.

Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.

2012-01-01

21

Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines  

Microsoft Academic Search

A fault signal diagnosis technique for internal combustion engines that uses a continuous wavelet transform algorithm is presented in this paper. The use of mechanical vibration and acoustic emission signals for fault diagnosis in rotating machinery has grown significantly due to advances in the progress of digital signal processing algorithms and implementation techniques. The conventional diagnosis technology using acoustic and

Jian-Da Wu; Jien-Chen Chen

2006-01-01

22

Layered clustering multi-fault diagnosis for hydraulic piston pump  

NASA Astrophysics Data System (ADS)

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

Du, Jun; Wang, Shaoping; Zhang, Haiyan

2013-04-01

23

Underground distribution cable incipient fault diagnosis system  

E-print Network

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

Jaafari Mousavi, Mir Rasoul

2007-04-25

24

Fault tree analysis is widely used in industry for fault diagnosis. The diagnosis of incipient or `soft' faults is  

E-print Network

Fault tree analysis is widely used in industry for fault diagnosis. The diagnosis of incipient results based on a neural network approach. INTRODUCTION Fault tree analysis (FTA) and fault tree used in systems safety analysis for over 30 years. During this time the fault tree method has been used

Madden, Michael

25

Fault diagnosis for magnetic bearing systems  

NASA Astrophysics Data System (ADS)

A full fault diagnosis for active magnetic bearing (AMB) and rotor systems to monitor the closed-loop operation and analyze fault patterns on-line in case any malfunction occurs is proposed in this paper. Most traditional approaches for fault diagnosis are based on actuator or sensor diagnosis individually and can solely detect a single fault at a time. This research combines two diagnosis methodologies by using both state estimators and parameter estimators to detect, identify and analyze actuators and sensors faults in AMB/rotor systems. The proposed fault diagnosis algorithm not only enhances the diagnosis accuracy, but also illustrates the capability to detect multiple sensors faults which occur concurrently. The efficacy of the presented algorithm has been verified by computer simulations and intensive experiments. The test rig for experiments is equipped with AMB, interface module (dSPACE DS1104), data acquisition unit MATLAB/Simulink simulation environment. At last, the fault patterns, such as bias, multiplicative loop gain variation and noise addition, can be identified by the algorithm presented in this work. In other words, the proposed diagnosis algorithm is able to detect faults at the first moment, find which sensors or actuators under failure and identify which fault pattern the found faults belong to.

Tsai, Nan-Chyuan; King, Yueh-Hsun; Lee, Rong-Mao

2009-05-01

26

Roller element bearing fault diagnosis using singular spectrum analysis  

NASA Astrophysics Data System (ADS)

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.

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

2013-02-01

27

Training for Skill in Fault Diagnosis  

ERIC Educational Resources Information Center

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)

Turner, J. D.

1974-01-01

28

Sensor Fault Diagnosis Using Principal Component Analysis  

E-print Network

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

Sharifi, Mahmoudreza

2010-07-14

29

Fault diagnosis of analog circuits  

Microsoft Academic Search

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.

J. W. Bandler; A. E. Salama

1985-01-01

30

Vibration-based fault detection of sharp bearing faults in helicopters  

E-print Network

Vibration-based fault detection of sharp bearing faults in helicopters Victor Girondin , Herve the context of helicopter imposes a limited sampling frequency regarding the observed phenomena, many noisy their efficiency. Keywords: vibration, helicopter, health monitoring, frequency estimation, bearing, HUMS

Paris-Sud XI, Université de

31

Multiple sensor fault diagnosis for dynamic processes.  

PubMed

Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology. PMID:20542268

Li, Cheng-Chih; Jeng, Jyh-Cheng

2010-10-01

32

Integrated Condition Monitoring and Fault Diagnosis for Modern Manufacturing Systems  

Microsoft Academic Search

A multi-sensor and multi-parameter condition monitoring and fault diagnosis system is designed and implemented for modern manufacturing systems, such as flexible manufacturing cells and systems. The overall hardware and software designs of this system, together with the functional sub-systems, are presented. This implemented system monitors power, vibration, temperature and pressure of the drives and spindles with a total of 72

Z. D. Zhou; Y. P. Chen; J. Y. H. Fuh; A. Y. C. Nee

2000-01-01

33

Measurement selection for parametric IC fault diagnosis  

NASA Technical Reports Server (NTRS)

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.

Wu, A.; Meador, J.

1991-01-01

34

On-line diagnosis of unrestricted faults  

NASA Technical Reports Server (NTRS)

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.

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

1974-01-01

35

Cooperative human-machine fault diagnosis  

NASA Technical Reports Server (NTRS)

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.

Remington, Roger; Palmer, Everett

1987-01-01

36

On-line diagnosis of unrestricted faults  

NASA Technical Reports Server (NTRS)

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.

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

1975-01-01

37

Efficient fault diagnosis of helicopter gearboxes  

NASA Technical Reports Server (NTRS)

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.

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

1993-01-01

38

Efficient fault diagnosis of helicopter gearboxes  

NASA Astrophysics Data System (ADS)

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.

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

1993-07-01

39

DIAGNOSIS USING FAULT TREES INDUCED FROM SIMULATED INCIPIENT FAULT CASE DATA  

Microsoft Academic Search

Fault tree analysis is widely used in industry for fault diagnosis. The diagnosis of incipient or 'soft' faults is considerably more difficult than that of 'hard' faults, which is the case considered normally. A detailed fault tree model reflecting signal variations over a wide range is required in the case of soft faults. This paper presents comprehensive results describing the

P J Nolan; M G Madden; P Muldoon

1994-01-01

40

Vibration-based fault detection of accelerometers in helicopters  

E-print Network

Vibration-based fault detection of accelerometers in helicopters Victor Girondin , Mehena Loudahi-based monitoring is an approach for health analysis of helicopters. However, accelerometers and other sub than standard indicators. Keywords: accelerometers; vibration; helicopter; monitoring; skewness; HUMS

Boyer, Edmond

41

Efficient fault diagnosis of helicopter gearboxes  

Microsoft Academic Search

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

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

1993-01-01

42

Fault diagnosis in sparse multiprocessor systems  

NASA Technical Reports Server (NTRS)

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.

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

1988-01-01

43

Monitoring and fault diagnosis of hybrid systems.  

PubMed

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

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

2005-12-01

44

Fault Diagnosis in HVAC Chillers  

NASA Technical Reports Server (NTRS)

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.

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

2005-01-01

45

A new condition monitoring and fault diagnosis system of induction motors using artificial intelligence algorithms  

Microsoft Academic Search

In this paper, a condition monitoring and fault diagnosis system for induction motors is proposed by integrating artificial intelligence algorithms: principal component analysis (PCA), genetic algorithm (GA) and an artificial neural network (ANN). As main diagnosis media of fault motor, three-direction vibration signals and three-phase stator current signals are selected to measure. Multi-sensor measurement results in lots of data transfer

Tian Han; Bo-Suk Yang; Jong Moon Lee

2005-01-01

46

Fault diagnosis system based on Dynamic Fault Tree Analysis of power transformer  

Microsoft Academic Search

Firstly, this research paper introduced the process of transformer fault diagnosis and the theory of DFTA and then we attempt to apply DFTA to the field of transformer faults diagnosis. By establishing the fault tree of transformer, a practical, easily-extended, interactive and self-learning enabled fault diagnosis system based on DFTA for transformer is designed and developed. With the implementation and

Jiang Guo; Kefei Zhang; Lei Shi; Kaikai Gu; Weimin Bai; Bing Zeng; Yajin Liu

2012-01-01

47

Sequential Testing Algorithms for Multiple Fault Diagnosis  

NASA Technical Reports Server (NTRS)

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.

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

1997-01-01

48

Fault detection and diagnosis of photovoltaic systems  

NASA Astrophysics Data System (ADS)

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

Wu, Xing

49

A Dynamic Integrated Fault Diagnosis Method for Power Transformers  

PubMed Central

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

Gao, Wensheng; Liu, Tong

2015-01-01

50

A dynamic integrated fault diagnosis method for power transformers.  

PubMed

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

Gao, Wensheng; Bai, Cuifen; Liu, Tong

2015-01-01

51

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

PubMed Central

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

Qu, Yongzhi; He, David; Yoon, Jae; Van Hecke, Brandon; Bechhoefer, Eric; Zhu, Junda

2014-01-01

52

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

NASA Astrophysics Data System (ADS)

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

Zhang, Jinyu; Huang, Xianxiang

53

Fault Diagnosis Using Consensus of Markov Chains Dejan P. Jovanovi  

E-print Network

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

Pollett, Phil

54

A roller bearing fault diagnosis method based on EMD energy entropy and ANN  

NASA Astrophysics Data System (ADS)

According to the non-stationary characteristics of roller bearing fault vibration signals, a roller bearing fault diagnosis method based on empirical mode decomposition (EMD) energy entropy is put forward in this paper. Firstly, original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs), then the concept of EMD energy entropy is proposed. The analysis results from EMD energy entropy of different vibration signals show that the energy of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to identify roller bearing fault patterns, energy feature extracted from a number of IMFs that contained the most dominant fault information could serve as input vectors of artificial neural network. The analysis results from roller bearing signals with inner-race and out-race faults show that the diagnosis approach based on neural network by using EMD to extract the energy of different frequency bands as features can identify roller bearing fault patterns accurately and effectively and is superior to that based on wavelet packet decomposition and reconstruction.

Yu, Yang; Yu, Dejie; Cheng, Junsheng

2006-06-01

55

Advanced Fault Diagnosis Methods in Molecular Networks  

PubMed Central

Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally. PMID:25290670

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

2014-01-01

56

A PC based fault diagnosis expert system  

NASA Technical Reports Server (NTRS)

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.

Marsh, Christopher A.

1990-01-01

57

Fault-Trajectory Approach for Fault Diagnosis on Analog Circuits Carlos Eduardo Savioli,  

E-print Network

Fault-Trajectory Approach for Fault Diagnosis on Analog Circuits Carlos Eduardo Savioli, Claudio C Mesquita@coe.ufrj.br Abstract This issue discusses the fault-trajectory approach suitability for fault on this concept for ATPG for diagnosing faults on analog networks. Such method relies on evolutionary techniques

Paris-Sud XI, Université de

58

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

NASA Astrophysics Data System (ADS)

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.

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

2015-01-01

59

Planetary Gearbox Fault Detection Using Vibration Separation Techniques  

NASA Technical Reports Server (NTRS)

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.

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

2011-01-01

60

Research of industrial furnace fault diagnosis expert system  

Microsoft Academic Search

In order to realize fast location and detection of abnormal status during running of industrial furnace, especially abnormal status of firing, this article studies and designs a fault diagnosis expert system based on fault tree theory. Firstly, formalized definition of industrial furnace fault diagnosis expert system is given in the paper, then all component elements of the expert system are

Shengquan Yang; Bailin Liu

2010-01-01

61

Industrial applications of the intelligent fault diagnosis system  

Microsoft Academic Search

Process monitoring and fault diagnosis have been widely studied in recent years, and a large number of industrial applications are reviewed. For further improvement of the reliability and safety of the process and the process equipment, the automatic early detection and localisation of faults is of high interest. This paper presents an intelligent process fault diagnosis system. The system is

S.-L. Jamsa-Jounela; M. Vermasvuori; S. Haavisto; J. Kampe

2001-01-01

62

Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion  

NASA Astrophysics Data System (ADS)

Vibration data of faulty rolling bearings are usually nonstationary and nonlinear, and contain fairly weak fault features. As a result, feature extraction of rolling bearing fault data is always an intractable problem and has attracted considerable attention for a long time. This paper introduces multifractal detrended fluctuation analysis (MF-DFA) to analyze bearing vibration data and proposes a novel method for fault diagnosis of rolling bearings based on MF-DFA and Mahalanobis distance criterion (MDC). MF-DFA, an extension of monofractal DFA, is a powerful tool for uncovering the nonlinear dynamical characteristics buried in nonstationary time series and can capture minor changes of complex system conditions. To begin with, by MF-DFA, multifractality of bearing fault data was quantified with the generalized Hurst exponent, the scaling exponent and the multifractal spectrum. Consequently, controlled by essentially different dynamical mechanisms, the multifractality of four heterogeneous bearing fault data is significantly different; by contrast, controlled by slightly different dynamical mechanisms, the multifractality of homogeneous bearing fault data with different fault diameters is significantly or slightly different depending on different types of bearing faults. Therefore, the multifractal spectrum, as a set of parameters describing multifractality of time series, can be employed to characterize different types and severity of bearing faults. Subsequently, five characteristic parameters sensitive to changes of bearing fault conditions were extracted from the multifractal spectrum and utilized to construct fault features of bearing fault data. Moreover, Hilbert transform based envelope analysis, empirical mode decomposition (EMD) and wavelet transform (WT) were utilized to study the same bearing fault data. Also, the kurtosis and the peak levels of the EMD or the WT component corresponding to the bearing tones in the frequency domain were carefully checked and used as the bearing fault features. Next, MDC was used to classify the bearing fault features extracted by EMD, WT and MF-DFA in the time domain and assess the abilities of the three methods to extract fault features from bearing fault data. The results show that MF-DFA seems to outperform each of envelope analysis, statistical parameters, EMD and WT in feature extraction of bearing fault data and then the proposed method in this paper delivers satisfactory performances in distinguishing different types and severity of bearing faults. Furthermore, to further ascertain the nature causing the multifractality of bearing vibration data, the generalized Hurst exponents of the original bearing vibration data were compared with those of the shuffled and the surrogated data. Consequently, the long-range correlations for small and large fluctuations of data seem to be chiefly responsible for the multifractality of bearing vibration data.

Lin, Jinshan; Chen, Qian

2013-07-01

63

Adaptive multiwavelets via two-scale similarity transforms for rotating machinery fault diagnosis  

NASA Astrophysics Data System (ADS)

Fault diagnosis of rotating machinery is very important and critical to avoid serious accidents. However, the complex and non-stationary vibration signals with a large amount of noise make the fault detection to be challenging, especially at the early stage. Based on the inner product principle, fault detection using wavelet transforms is to match fault features most correlative to basis functions, and its effectiveness is determined by the construction and choice of wavelet basis function. In this paper, a new method based on adaptive multiwavelets via two-scale similarity transforms (TSTs) is proposed. Multiwavelets can offer multiple wavelet basis functions and so have the possibility of matching various fault features preferably. TSTs are simple and straightforward methods to design a series of new biorthogonal multiwavelets with some desirable properties. Using TSTs, a changeable and adaptive multiwavelet library is established so as to provide various ascendant multiple basis functions for inner product operation. By the rule of kurtosis maximization principle, optimal multiwavelets most similar to the fault features of a given signal are searched for. The applications to a rolling bearing of outer-race fault and a flue gas turbine unit of rub-impact fault show that the proposed method is an effective approach to detecting the impulse feature components hidden in vibration signals and performs well for rotating machinery fault diagnosis.

Yuan, Jing; He, Zhengjia; Zi, Yanyang; Lei, Yaguo; Li, Zhen

2009-07-01

64

Fault diagnosis of natural gas compressor based on EEMD and Hilbert marginal spectrum  

Microsoft Academic Search

The paper utilizes ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for the fault diagnosis of the reciprocating compressor on the offshore platform of WZ12-1, aiming at the non-stationary and nonlinear characteristics of vibration signals collected from the faulty compressor. First, the EEMD algorithm self-adaptively anti-aliasing decomposes the vibration signal into a set of intrinsic mode function of different

Jinshan Lin

2010-01-01

65

Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution  

Microsoft Academic Search

Based on the Morlet wavelet transformation and Wigner-Ville distribution (WVD), we present a wind turbine fault diagnosis method in this paper. Wind turbine can be damaged by moisture absorption, fatigue, wind gusts or lightening strikes. Due to this reason, there is an increasing need to monitor the health of these structures. Vibration analysis is the best-known technology applied in wind

Baoping Tang; Wenyi Liu; Tao Song

2010-01-01

66

Two Similarity Measure Approaches to Whole Building Fault Diagnosis  

E-print Network

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

Lin, G.; Claridge, D.

2012-01-01

67

Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis  

NASA Astrophysics Data System (ADS)

At constant rotating speed, localized faults in rotating machine tend to result in periodic shocks and thus arouse periodic transients in the vibration signal. The transient feature analysis has always been a crucial problem for localized fault detection, and the key aim for transient feature analysis is to identify the model and its parameters (frequency, damping ratio and time index) of the transient, and the time interval, i.e. period, between transients. Based on wavelet and correlation filtering, a technique incorporating transient modeling and parameter identification is proposed for rotating machine fault feature detection. With the proposed method, both parameters of a single transient and the period between transients can be identified from the vibration signal, and localized faults can be detected based on the parameters, especially the period. First, a simulation signal is used to test the performance of the proposed method. Then the method is applied to the vibration signals of different types of bearings with localized faults in the outer race, the inner race and the rolling element, respectively, and all the results show that the period between transients, representing the localized fault characteristic, is successfully detected. The method is also utilized in gearbox fault diagnosis and the effectiveness is verified through identifying the parameters of the transient model and the period. Moreover, it can be drawn that for bearing fault detection, the single-side wavelet model is more suitable than double-side one, while the double-side model for gearbox fault detection. This research proposed an effective method of localized fault detection for rotating machine fault diagnosis through transient modeling and parameter detection.

Wang, Shibin; Huang, Weiguo; Zhu, Z. K.

2011-05-01

68

Supervision, fault-detection and fault-diagnosis methods — An introduction  

Microsoft Academic Search

The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This paper gives an introduction to the field of fault detection and diagnosis. It begins with a consideration of a knowledge-based procedure that is based on analytical and heuristic information. Then different methods of fault detection are considered, which extract features from

R. Isermann

1997-01-01

69

Machine Learning Methods for Industrial Systems Fault Diagnosis  

Microsoft Academic Search

\\u000a Machine learning methods can be of particular interest for fault diagnosis of systems that exhibit event-driven dynamics.\\u000a For this type of systems fault diagnosis based on automata and finite state machine models has to be performed. In this chapter\\u000a the application of fuzzy automata for fault diagnosis is analyzed. The output of the monitored system is partitioned into\\u000a linear segments

Gerasimos G. Rigatos

70

Embedded Fault Diagnosis Expert System Based on CLIPS and ANN  

Microsoft Academic Search

Embedded fault diagnosis technology requires high pertinency and small occupation space. Traditional fault diagnosis system\\u000a can not satisfy the demand above. Aiming at this problem, a kind of embedded fault diagnosis expert system (E-FDES) based\\u000a on CLIPS and ANN was brought forward. FDES and its relative technology were discussed, developing environment and design tool\\u000a chain for E-FDES was established in

Tan Dapeng; Li Peiyu; Pan Xiaohong

2007-01-01

71

An integrated approach to helicopter planetary gear fault diagnosis and failure prognosis  

Microsoft Academic Search

This paper introduces the design of an integrated framework for on-board fault diagnosis and failure prognosis of a helicopter transmission component, and describes briefly its main modules. It suggests means to (1) validate statistically and pre-process sensor data (vibration), (2) integrate model-based diagnosis and prognosis, (3) extract useful features or condition indicators from data de-noised by blind deconvolution, and (4)

Romano Patrick; Marcos E. Orchard; Bin Zhang; Michael D. Koelemay; Gregory J. Kacprzynski; Aldo A. Ferri; George J. Vachtsevanos

2007-01-01

72

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

NASA Astrophysics Data System (ADS)

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.

Feng, Zhipeng; Liang, Ming

2014-09-01

73

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

NASA Astrophysics Data System (ADS)

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.

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

2013-07-01

74

Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD-fractal dimension  

NASA Astrophysics Data System (ADS)

In this paper a novel method for de-noising nonstationary vibration signal and diagnosing diesel engine faults is presented. The method is based on the adaptive wavelet threshold (AWT) de-noising, ensemble empirical mode decomposition (EEMD) and correlation dimension (CD). A new adaptive wavelet packet (WP) thresholding function for vibration signal de-noising is used in this paper. To alleviate the mode mixing problem occurring in EMD, ensemble empirical mode decomposition (EEMD) is presented. With EEMD, the components with truly physical meaning can be extracted from the signal. Utilizing the advantage of EEMD, this paper proposes a new AWT-EEMD-based method for fault diagnosis of diesel engine. A study of correlation dimension in engine condition monitoring is reported also. Some important influencing factors relating directly to the computational precision of correlation dimension are discussed. Industrial engine normal and fault vibration signals measured from different operating conditions are analyzed using the above method.

Wang, Xia; Liu, Changwen; Bi, Fengrong; Bi, Xiaoyang; Shao, Kang

2013-12-01

75

Fault progression modeling: An application to bearing diagnosis and prognosis  

Microsoft Academic Search

The successful implementation of fault diagnosis and failure prognosis algorithms to safety critical systems requires the definitions and applications of mathematically rigorous modules. These modules, including data preprocessing, feature extraction, diagnostic and prognostic algorithms, performance metrics definition, and a fault progression model, form an integrated architecture for system health monitoring and management. In these modules, the fault progression model is

Bin Zhang; Chris Sconyers; Marcos Orchard; Romano Patrick; George Vachtsevanos

2010-01-01

76

NEURO FUZZY SYSTEM FOR INDUSTRIAL PROCESSES FAULT DIAGNOSIS  

Microsoft Academic Search

In the last decade considerable research efforts have been spent to seek for systematic approaches to Fault Diagnosis (FD) in dynamical systems The problem of fault detection consists in detecting faults in a physical system by monitoring its inputs and outputs .Recently, the research has focused on non-linear systems FDI. Traditionally, the FD problem for non-linear dynamic systems has been

Marius Pislaru; Alexandru Trandabat; Marius Olariu

77

Fault diagnosis for the Space Shuttle main engine  

NASA Technical Reports Server (NTRS)

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.

Duyar, Ahmet; Merrill, Walter

1992-01-01

78

Fault diagnosis of induction motors with dynamical neural networks  

Microsoft Academic Search

The paper studies the fault diagnosis of induction motors using neural network time-series models. The problem has been widely discussed in the literature and neural networks have been used in the fault diagnosis of induction motors. However, the neural network models have been mostly static - dynamical neural networks have been overlooked and have not received enough attention in this

Jarmo Lehtoranta; Heikki N. Koivo

2005-01-01

79

Current Issues in Vibration-Based Fault Diagnostics and Prognostics Victor Giurgiutiu  

E-print Network

1 Current Issues in Vibration-Based Fault Diagnostics and Prognostics Victor Giurgiutiu Mechanical Current issues in vibration-based fault diagnostics and prognostics are: (a) fault/damage identification and then classification. Keywords: structural health monitoring, active sensors, vibrations, signal analysis; smart

Giurgiutiu, Victor

80

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

NASA Astrophysics Data System (ADS)

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.

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

2013-12-01

81

Detection of signal transients based on wavelet and statistics for machine fault diagnosis  

NASA Astrophysics Data System (ADS)

This paper presents a transient detection method that combines continuous wavelet transform (CWT) and Kolmogorov-Smirnov (K-S) test for machine fault diagnosis. According to this method, the CWT represents the signal in the time-scale plane, and the proposed "step-by-step detection" based on K-S test identifies the transient coefficients. Simulation study shows that the transient feature can be effectively identified in the time-scale plane with the K-S test. Moreover, the transients can be further transformed back into the time domain through the inverse CWT. The proposed method is then utilized in the gearbox vibration transient detection for fault diagnosis, and the results show that the transient features both expressed in the time-scale plane and re-constructed in the time domain characterize the gearbox condition and fault severity development more clearly than the original time domain signal. The proposed method is also applied to the vibration signals of cone bearings with the localized fault in the inner race, outer race and the rolling elements, respectively. The detected transients indicate not only the existence of the bearing faults, but also the information about the fault severity to a certain degree.

Zhu, Z. K.; Yan, Ruqiang; Luo, Liheng; Feng, Z. H.; Kong, F. R.

2009-05-01

82

Fault Diagnosis of Asynchronous Induction Motor Based on BP Neural Network  

Microsoft Academic Search

For asynchronous induction motor, it is necessary to carry out fault diagnosis in time. The traditional fault diagnosis methods have the shortcomings such as the diagnosis slow speed, low accuracy. In this paper, for the common fault characteristics of asynchronous induction motor, the fault diagnosis method based on improved BP algorithm, by using of the diagnosis model, is adopted to

Zhao Xiaodong; Tang Xinliang; Zhao Juan; Zhang Yubin

2010-01-01

83

Expert systems for real-time monitoring and fault diagnosis  

NASA Technical Reports Server (NTRS)

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.

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

1989-01-01

84

Adaptive PCA based fault diagnosis scheme in imperial smelting process.  

PubMed

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

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

2014-09-01

85

Solar Dynamic Power System Fault Diagnosis  

NASA Technical Reports Server (NTRS)

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.

Momoh, James A.; Dias, Lakshman G.

1996-01-01

86

Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis  

NASA Astrophysics Data System (ADS)

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

Cui, Lingli; Wang, Jing; Lee, Seungchul

2014-05-01

87

Fault diagnosis based on continuous simulation models  

NASA Technical Reports Server (NTRS)

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.

Feyock, Stefan

1987-01-01

88

Robust model-based fault diagnosis for chemical process systems  

E-print Network

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

Rajaraman, Srinivasan

2006-08-16

89

CAPRI: A Common Architecture for Distributed Probabilistic Internet Fault Diagnosis  

E-print Network

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

Lee, George J.

2007-06-04

90

CAPRI : a common architecture for distributed probabilistic Internet fault diagnosis  

E-print Network

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

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

2007-01-01

91

Data Mining for Building Rule-based Fault Diagnosis Systems  

Microsoft Academic Search

This paper aims at developing rule-based fault diagnosis (RBFD) systems using data mining techniques, where we address a problem of generating rules for faults with low probability of occurrence but considerable conceptual importance. Main technical contributions include a multilayer structure of rule generation and use, and a regularization model embedding some information on recognition rate, coverage rate and generalization capability

Dianhui Wang

2006-01-01

92

Study on Fault Diagnosis Strategy of Radio Fuze Microsystem  

Microsoft Academic Search

The radio fuze will be out of order easily because of small size and high integration. In order to improve the testability, an effective method to achieve rapid detection and fault isolation is to determine reasonable test-sets and a best diagnosis strategy. Based on the functional block diagram, a fault message matrix among every module of inner radio fuze is

Xiaopeng Yan; Li Ping; Ruili Jia; Yongqiang Wang

2009-01-01

93

Wavelet neural network based fault diagnosis of asynchronous motor  

Microsoft Academic Search

According to asynchronous motor's complex fault characteristics, and the combination of wavelet transform technique, an improved wavelet neural network for fault diagnosis of asynchronous motor is proposed in this paper. Taking wavelet transform technique as wavelet neural network (WNN) the input vector of picking up asynchronous motor's the characteristic signal, and wavelet neural network algorithm is optimized, The self-adaptive wavelet

Bo Hu; Wen-hua Tao; Bo Cui; Yi-tong Bai; Xu Yin

2009-01-01

94

Real-time fault diagnosis for propulsion systems  

NASA Technical Reports Server (NTRS)

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.

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

1991-01-01

95

Intelligent fault isolation and diagnosis for communication satellite systems  

NASA Technical Reports Server (NTRS)

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.

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

1992-01-01

96

Composite Bending Box Section Modal Vibration Fault Detection  

NASA Technical Reports Server (NTRS)

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.

Werlink, Rudy

2002-01-01

97

A fault diagnosis prototype for a bioreactor for bioinsecticide production  

Microsoft Academic Search

The objective of this work is to develop an algorithm for fault diagnosis in a process of animal cell cultivation, for bioinsecticide production. Generally, these processes are batch processes. It is a fact that the diagnosis for a batch process involves a division of the process evolution (time horizon) into partial processes, which are defined as pseudocontinuous blocks. Therefore, a

Enrique E. Tarifa; Nicolás J. Scenna

1995-01-01

98

IMPLEMENTATION OF STOCHASTIC METHODS FOR INDUSTRIAL GAS TURBINE FAULT DIAGNOSIS  

Microsoft Academic Search

Implementation of stochastic diagnostic methods for diagnosis of sensor or component faults is presented. Two industrial gas turbines are considered as test cases, one twin and one single shaft arrangement. Methods based on Probabilistic Neural Networks (PNN) and Bayesian Belief Networks (BBN), are implemented. The ability for successful diagnosis is demonstrated on specific cases of sensor malfunctions, as well as

C. Romessis; K. Mathioudakis

2005-01-01

99

Fault diagnosis of analog electronic circuits with tolerances in mind  

Microsoft Academic Search

This paper presents analysis of components toler- ance influence on fault diagnosis efficiency of analog electronic circuits. There has been proposed method of finding optimal fre- quency of input periodic excitation with simultaneous maximiza- tion of components tolerances in order to keep assumed level of diagnosis efficiency. There has been also proposed departure from classical \\

Lukasz Chruszczyk

2011-01-01

100

Observer-Based Fault Diagnosis of Power Electronics Systems  

E-print Network

the true state of the system, and by appropriately choosing the filter gain, the filter residual has, upon information provided by the diagnosis system, removes faulty components and usually substitutesObserver-Based Fault Diagnosis of Power Electronics Systems Kieran T. Levin, Eric M. Hope

Liberzon, Daniel

101

Fault Diagnosis and Fault Tolerant Control for Wheeled Mobile Robots under Unknown Environments: A Survey  

Microsoft Academic Search

Fault detection and diagnosis (FDD) and fault tolerant control (FTC) are increasingly important for wheeled mobile robots (WMRs), especially those in unknown environments such as planetary exploration. Due to the importance of reliability and safe operation of WMRs, this paper presents a survey of state-of-the-art in FDD & FTC of WMRs under unknown environments. Firstly, we briefly introduce main components,

Duan Zhuo-hua; Cai Zi-xing; Yu Jin-xia

2005-01-01

102

A survey of an introduction to fault diagnosis algorithms  

NASA Technical Reports Server (NTRS)

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.

Mathur, F. P.

1972-01-01

103

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

NASA Astrophysics Data System (ADS)

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.

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

2013-05-01

104

Damage diagnosis of steel girder bridges using ambient vibration data  

Microsoft Academic Search

This paper presents an effective method for damage estimation of steel girder bridges using ambient vibration data. Modal parameters were identified from the ambient vibration data using the frequency domain decomposition technique, and were utilized as the feature vectors for damage diagnosis. Conventional back-propagation neural networks (BPNNs) were incorporated to assess damage locations and damage severities based on the modal

Jong Jae Lee; Chung Bang Yun

2006-01-01

105

Sensor fault diagnosis with a probabilistic decision process  

NASA Astrophysics Data System (ADS)

In this paper a probabilistic approach to sensor fault diagnosis is presented. The proposed method is applicable to systems whose dynamic can be approximated with only few active states, especially in process control where we usually have a relatively slow dynamics. Unlike most existing probabilistic approaches to fault diagnosis, which are based on Bayesian Belief Networks, in this approach the probabilistic model is directly extracted from a parity equation. The relevant parity equation can be found using a model of the system or through principal component analysis of data measured from the system. In addition, a sensor detectability index is introduced that specifies the level of detectability of sensor faults in a set of analytically redundant sensors. This index depends only on the internal relationships of the variables of the system and noise level. The method is tested on a model of the Tennessee Eastman process and the result shows a fast and reliable prediction of fault in the detectable sensors.

Sharifi, Reza; Langari, Reza

2013-01-01

106

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

NASA Astrophysics Data System (ADS)

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.

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

2015-02-01

107

On the Diagnosis of Byzantine Faults  

Microsoft Academic Search

The class of evidence-based diagnosis algorithms is developed to identify Byzantine (and any other faulty) processors. Such algorithms are said to be fair if they identify no failure-free processor as faulty. This paper makes two significant contributions: (i) it introduces a very general and simple formal model of the evidence-based diagnosis algorithms; and (ii) it derives a simple fair diagnosis

K. V. S. Ramarao; Joel C. Adams

1988-01-01

108

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

NASA Astrophysics Data System (ADS)

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.

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

2015-02-01

109

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

NASA Technical Reports Server (NTRS)

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.

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

1992-01-01

110

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

NASA Astrophysics Data System (ADS)

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.

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

2011-07-01

111

Application of an improved kurtogram method for fault diagnosis of rolling element bearings  

NASA Astrophysics Data System (ADS)

Kurtogram, due to the superiority of detecting and characterizing transients in a signal, has been proved to be a very powerful and practical tool in machinery fault diagnosis. Kurtogram, based on the short time Fourier transform (STFT) or FIR filters, however, limits the accuracy improvement of kurtogram in extracting transient characteristics from a noisy signal and identifying machinery fault. Therefore, more precise filters need to be developed and incorporated into the kurtogram method to overcome its shortcomings and to further enhance its accuracy in discovering characteristics and detecting faults. The filter based on wavelet packet transform (WPT) can filter out noise and precisely match the fault characteristics of noisy signals. By introducing WPT into kurtogram, this paper proposes an improved kurtogram method adopting WPT as the filter of kurtogram to overcome the shortcomings of the original kurtogram. The vibration signals collected from rolling element bearings are used to demonstrate the improved performance of the proposed method compared with the original kurtogram. The results verify the effectiveness of the method in extracting fault characteristics and diagnosing faults of rolling element bearings.

Lei, Yaguo; Lin, Jing; He, Zhengjia; Zi, Yanyang

2011-07-01

112

Display interface concepts for automated fault diagnosis  

NASA Technical Reports Server (NTRS)

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.

Palmer, Michael T.

1989-01-01

113

Multiscale envelope manifold for enhanced fault diagnosis of rotating machines  

NASA Astrophysics Data System (ADS)

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.

Wang, Jun; He, Qingbo; Kong, Fanrang

2015-02-01

114

Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures  

E-print Network

Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures Ian Harris and Russell Tessier. Cluster-based FPGA architectures, in which several logic blocks are grouped together into a coarse-grained logic block, are rapidly becoming the architecture of choice for major FPGA manufacturers. The high

Harris, Ian G.

115

An approach to model based fault diagnosis of industrial robots  

Microsoft Academic Search

A method for the incipient fault diagnosis of industrial robot mechanics is proposed. It is based on mathematical models expressed in terms of nonlinear differential equations for a robot's different axes. The parameters of these models directly represent characteristic physical quantities (process coefficients), which are calculated by a suitable parameter estimation procedure. Additionally, a simple but efficient approach to the

Bernd Freyermuth

1991-01-01

116

Motion control - Fault diagnosis in Machines using VHDL  

Microsoft Academic Search

The machines which have become a part of present day life, even in some cases it overcome human working ways. So for there is majo r concern in functioning of motor drives because a minute faulty function of motor may lead to drastic damage in working environment. Thus before entering into the fault diagnosis of induction motor the speed control

K. M. Krishnan; N. Rajeswaran; P. G Student

2012-01-01

117

The new fault diagnosis method of wavelet packet neural network on pump valves of reciprocating pumps  

Microsoft Academic Search

Two key issues of fault diagnosis for the pump valves of reciprocating pump are extracting the fault feature information of nonstationary time variation process efficiently from system feature signals and classifying the faults feature correctly. A new method of fault feature is proposed by ordinary pressure signal (pressure in pump cylinder) as system feature signals. A diagnosis method based on

Duan Yu-bo; Wang Xing-zhu; Han Xue-song

2009-01-01

118

Gate Level Fault Diagnosis in Scan-Based BIST Ismet Bayraktaroglu  

E-print Network

that is capable of locating stuck-at faults within small neighborhoods through utilization of both fault embeddingGate Level Fault Diagnosis in Scan-Based BIST Ismet Bayraktaroglu Computer Science & Engineering@cs.ucsd.edu Abstract A gate level, automated fault diagnosis scheme is pro- posed for scan-based BIST designs

Orailoglu, Alex

119

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

E-print Network

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

Daigle, Matthew

120

SBT soft fault diagnosis in analog electronic circuits: a sensitivity-based approach by randomized algorithms  

Microsoft Academic Search

This paper addresses the fault diagnosis issue based on a simulation before test philosophy in analog electronic circuits. Diagnosis, obtained by comparing signatures measured at the test nodes with those contained in a fault dictionary, allows for sub-systems testing and fault isolation within the circuit. A novel method for constructing the fault dictionary under the single faulty component\\/unit hypothesis is

Cesare Alippi; Marcantonio Catelani; Ada Fort; Marco Mugnaini

2002-01-01

121

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

PubMed Central

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

Li, Pengfei; Jiang, Yongying; Xiang, Jiawei

2014-01-01

122

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

NASA Astrophysics Data System (ADS)

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.

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

2015-01-01

123

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

NASA Astrophysics Data System (ADS)

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 experimental studies.

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

2011-10-01

124

Development of Wind Turbine Gearbox Data Analysis and Fault Diagnosis System  

Microsoft Academic Search

Application of modern signal processing technology, wireless sensor technology, virtual instrument technology, and database technology, the subsystem of remote condition monitoring and fault diagnosis system - data analysis and fault diagnosis system is developed. The overall scheme of wind turbine gearbox remote condition monitoring and fault diagnosis system is designed, and the function of each module in the system is

Fengtao Wang; Liang Zhang; Bin Zhang; Yangyang Zhang; Liang He

2011-01-01

125

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

PubMed Central

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

Wang, Huaqing; Chen, Peng

2009-01-01

126

DUKF-based GTM UAV fault detection and diagnosis with nonlinear and LPV models  

Microsoft Academic Search

Fault tolerant control system (FTCS) is to maintain system stability in the presence of fault occurred in the actuators, sensors or other system components. When a fault\\/failure occurs either in an actuator, sensor or plant, the fault detection and diagnosis (FDD) as the central part of a FTCS will detect and diagnose the source and the magnitude of the fault.

Ling Ma; Youmin Zhang

2010-01-01

127

Digraph and fault-tree technique for online process diagnosis  

SciTech Connect

Available diagnostic systems and existing diagnostic techniques illustrate the need for a comprehensive, model-based theory for on-line process diagnosis. This thesis classifies the diagnostic task into two functions, problem recognition and fault detection. The thesis hypothesizes the need for goal-directed, causal models to perform these functions in chemical process systems. A diagnostic theory is developed comprising goal-directed, causal models and a method to use these models to perform the diagnostic functions. Digraph and fault trees form the causal model base. The fault tree's primal events are verified with available on-line indications using probabilistic theory and the digraph's causal structure. Problem recognition is disclosed through monitoring the on-line unreliability of the process goal. Fault detection is accomplished through ranking the failure rates of potential process failures using on-line indications and design data. The theory provides a basis for performing comprehensive, robust system diagnosis. Examples illustrate the technique's ability to diagnose single and multiple system faults including equipment failures, disturbances, and indicator failures in linear and control loop systems. Limitations to application of the theory are discussed.

Ulerich, N.H.

1989-01-01

128

Variogram-based fault diagnosis in an interconnected tank system.  

PubMed

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

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

2012-05-01

129

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

Microsoft Academic Search

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

Anand P. Narayan

1998-01-01

130

Fault Indicators for the Diagnosis of Rotor Faults in FOC Induction Motor Drives  

Microsoft Academic Search

In this paper, different diagnostic techniques are proposed for the diagnosis of rotor faults in field oriented controlled (FOC) induction motor drives. In particular, the applicability of the estimated rotor flux, the current error signals and the outputs of the current controllers of a FOC drive are analyzed. Some block diagrams are presented which can be used to establish useful

S. M. A. Cruz; A. J. M. Cardoso

2007-01-01

131

A fault diagnosis scheme of rolling element bearing based on near-field acoustic holography and gray level co-occurrence matrix  

NASA Astrophysics Data System (ADS)

Vibration signal analysis is the most widely used technique in 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 rolling element bearing fault diagnosis based on near-field acoustic holography (NAH) and gray level co-occurrence matrix (GLCM) is presented in this paper. It focuses on applying the distribution information of sound field to bearing fault diagnosis. A series of rolling element bearings with different types of fault are experimentally studied. Sound fields and corresponding acoustic images in different bearing conditions are obtained by fast Fourier transform (FFT) based NAH. GLCM features are extracted for capturing fault pattern information underlying sound fields. The optimal feature subset selected by improved F-score is fed into multi-class support vector machine (SVM) for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with the traditional ABD method. Considering test cost, the quantized level and the number of GLCM features for each characteristic frequency is suggested to be 4 and 32, respectively, with the satisfactory accuracy rate 97.5%.

Lu, Wenbo; Jiang, Weikang; Wu, Haijun; Hou, Junjian

2012-07-01

132

Fault diagnosis in orbital refueling operations  

NASA Technical Reports Server (NTRS)

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.

Boy, Guy A.

1988-01-01

133

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

PubMed Central

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

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

2013-01-01

134

Hierarchical Fault Diagnosis and Health Monitoring in Satellites Formation Flight  

Microsoft Academic Search

Current spacecraft health monitoring and fault- diagnosis practices involve around-the-clock limit-checking and trend analysis on large amount of telemetry data. They do not scale well for future multiplatform space missions due the size of the telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the operations team personnel. The need for efficient utilization of telemetry

Amitabh Barua; Khashayar Khorasani

2011-01-01

135

Distributed quantitative and qualitative fault diagnosis: railway junction case study  

Microsoft Academic Search

The paper develops a novel, comprehensive, reasoned approach to fault diagnosis in a class of reciprocating, electro-mechanical equipment referred to as single throw mechanical equipment (STME). STMEs are widely used in many industrial applications—examples of which include automatic doors, mechanical presses and barrier systems. A formal definition of the STME is initially presented. In this paper, an electro-pneumatic railway point

C. Roberts; H. P. B. Dassanayake; N. Lehrasab; C. J. Goodman

2002-01-01

136

Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis  

NASA Astrophysics Data System (ADS)

Generally, the vibration signals of faulty machinery are non-stationary and nonlinear under complicated operating conditions. Therefore, it is a big challenge for machinery fault diagnosis to extract optimal features for improving classification accuracy. This paper proposes semi-supervised kernel Marginal Fisher analysis (SSKMFA) for feature extraction, which can discover the intrinsic manifold structure of dataset, and simultaneously consider the intra-class compactness and the inter-class separability. Based on SSKMFA, a novel approach to fault diagnosis is put forward and applied to fault recognition of rolling bearings. SSKMFA directly extracts the low-dimensional characteristics from the raw high-dimensional vibration signals, by exploiting the inherent manifold structure of both labeled and unlabeled samples. Subsequently, the optimal low-dimensional features are fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories and severities of bearings. The experimental results demonstrate that the proposed approach improves the fault recognition performance and outperforms the other four feature extraction methods.

Jiang, Li; Xuan, Jianping; Shi, Tielin

2013-12-01

137

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

PubMed

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

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

2013-01-01

138

Research on fault diagnosis technology of inverter based on associative memory neural network  

Microsoft Academic Search

A modified associative memory model of artificial neural network (ANN) is introduced into the fault diagnosis of inverter. Firstly, the diagnosis algorithm is derived and the fault table is concluded. Secondly, the fault sample vectors are preprocessed by HADAMARD transform, and learned by the model. Finally, the samples and non-samples are parallel associatively recalled based on this network. Furthermore, the

Mulan Wang; Chongwei Zhang; Shenggu Gu

2004-01-01

139

Deterministic Partitioning Techniques for Fault Diagnosis in Scan-Based BIST Ismet Bayraktaroglu  

E-print Network

of the signature is utilized through a fault-free sequence generator [1]. Even though the scheme is capableDeterministic Partitioning Techniques for Fault Diagnosis in Scan-Based BIST Ismet Bayraktaroglu, CA 92093 alex@cs.ucsd.edu Abstract A deterministic partitioning technique for fault diagnosis in Scan

Bayraktaroglu, Ismet

140

Application of genetic algorithms to fault diagnosis in nuclear power plants  

Microsoft Academic Search

A nuclear power plant (NPP) is a complex and highly reliable special system. Without expert knowledge, fault confirmation in the NPP can be prevented by illusive and real-time signals. A new method of fault diagnosis, based on genetic algorithms (GAs) has been developed to resolve this problem. This NPP fault diagnosis method combines GAs and classical probability with an expert

Zhou Yangping; Zhao Bingquan; Wu DongXin

2000-01-01

141

Trends in the application of model-based fault detection and diagnosis of technical processes  

Microsoft Academic Search

After a short overview of the historical development of model-based fault detection, some proposals for the terminology in the field of supervision, fault detection and diagnosis are stated, based on the work within the IFAC SAFEPROCESS Technical Committee. Some basic fault-detection and diagnosis methods are briefly considered. Then, an evaluation of publications during the last 5 years shows some trends

R. Isermann; P. Ballé

1997-01-01

142

NEURO-FUZZY BASED FAULT DIAGNOSIS APPLIED TO AN ELECTRO-PNEUMATIC VALVE  

Microsoft Academic Search

The early detection of faults (just beginning and still developing) can help avoid system shutdown, breakdown and even catastrophes involving hu man fatalities and material damage. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the properties of the TSK\\/Mamdani approaches and neuro-fuzzy (NF) fault diagnosis within an application study of an

Faisel J Uppal; Ron J Patton; Vasile Palade

143

Development and research on fault diagnosis system of solar power tower plants  

Microsoft Academic Search

According to the system configuration and operating characteristic of a constructing solar power tower (SPT) plant in China in this paper, the fault diagnosis system (FDS) was researched and developed. Furthermore, evaluation system of fault grade was established by the method of fuzzy comprehensive evaluation. In this FDS, the fault diagnosis structure was designed to adopt the expert system for

D. Y. Liu; T. Z. Guo; S. Guo; D. S. Wan; C. Xu; W. Huang

2009-01-01

144

On-Line Fault Diagnosis of Dynamic Systems via Robust Parameter Identification Gerard Blocha  

E-print Network

On-Line Fault Diagnosis of Dynamic Systems via Robust Parameter Identification G´erard Blocha of faults, their isolation and their identification is presented. The systems considered are MISO systems knowledge of the faults which can occur is used. The faults modeled here are outliers, biases or drifts

Paris-Sud XI, Université de

145

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

NASA Astrophysics Data System (ADS)

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

Narayan, Anand P.

1998-07-01

146

Advanced fault diagnosis techniques and their role in preventing cascading blackouts  

E-print Network

frequency signal, the new scheme can solve the difficulty of the traditional method to differentiate the internal faults from the external using one end transmis- sion line data only. The fault diagnosis based on synchronized sampling utilizes the Global...

Zhang, Nan

2007-04-25

147

Hypothetical Scenario Generator for Fault-Tolerant Diagnosis  

NASA Technical Reports Server (NTRS)

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.

James, Mark

2007-01-01

148

Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert Huang transform  

NASA Astrophysics Data System (ADS)

A number of techniques for detection of faults in rolling element bearing using frequency domain approach exist today. For analysing non-stationary signals arising out of defective rolling element bearings, use of conventional discrete Fourier transform (DFT) has been known to be less efficient. One of the most suited time-frequency approach, wavelet transform (WT) has inherent problems of large computational time and fixed-scale frequency resolution. In view of such constraints, the Hilbert-Huang Transform (HHT) technique provides multi-resolution in various frequency scales and takes the signal's frequency content and their variation into consideration. HHT analyses the vibration signal using intrinsic mode functions (IMFs), which are extracted using the process of empirical mode decomposition (EMD). However, use of Hilbert transform (HT)-based time domain approach in HHT for analysis of bearing vibration signature leads to scope for subjective error in calculation of characteristic defect frequencies (CDF) of the rolling element bearings. The time resolution significantly affects the calculation of corresponding frequency content of the signal. In the present work, FFT of IMFs from HHT process has been incorporated to utilise efficiency of HT in frequency domain. The comparative analysis presented in this paper indicates the effectiveness of using frequency domain approach in HHT and its efficiency as one of the best-suited techniques for bearing fault diagnosis (BFD).

Rai, V. K.; Mohanty, A. R.

2007-08-01

149

Study on Power Transformer Fault Diagnosis Based on Niche Genetic Algorithm  

Microsoft Academic Search

Power transformer fault diagnosis is the key technology of electric power system. Niche genetic algorithm (NGA) was introduced to optimize adaptive-learning-rate-momentum back propagation (BP) network. NGA-BP model was established and applied to power transformer fault diagnosis. Compared with BP network, NGA-BP network has a better performance on convergent speed and stability. The experimental results demonstrate that fault diagnosis precision of

Jiyin Zhao; Ruirui Zheng; Haihong Dong

2009-01-01

150

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

NASA Astrophysics Data System (ADS)

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.

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

2015-02-01

151

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

PubMed Central

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

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

2014-01-01

152

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

NASA Astrophysics Data System (ADS)

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

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

2013-02-01

153

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

PubMed Central

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

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

2014-01-01

154

Fault Detection and Diagnosis System for the Air-conditioning  

NASA Astrophysics Data System (ADS)

The fault detection and diagnosis system, the FDD system, for the HVAC was initiated around the middle of 1970s in Japan but it still remains at the elementary stage. The HVAC is really one of the most complicated and large scaled system for the FDD system. Besides, the maintenance engineering was never focussed as the target of the academic study since after the war, but the FDD system for some kinds of the components and subsystems has been developed for the sake of the practical industrial needs. Recently, international cooperative study in the IEA Annex 25 on the energy conservation for the building and community targetted on the BOFD, the building optimization, fault detection and diagnosis. Not a few academic peaple from various engineering field got interested and, moreover, some national projects seem to start in the European countries. The author has reviewed the state of the art of the FDD and BO as well based on the references and the experience at the IEA study.

Nakahara, Nobuo

155

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

NASA Technical Reports Server (NTRS)

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.

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

1991-01-01

156

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

E-print Network

Airbus Toulouse ­ Flight test data centre. Diagnosis and treatment of noisy vibration flight test data. The trainee will work within flight test vibration analysis team.The main missions and activities on flight test vibration data; - Implement and test in LMS Test.Lab (vibration data processing software

Dobigeon, Nicolas

157

Research on Fault Diagnosis of Mixed-Signal Circuits Based on Genetic Algorithms  

Microsoft Academic Search

As the fault modes of mixed-signal circuits growing, aiming at the features for its signal are both analog and digital amount, the paper analyzed that the fault diagnosis program of mixed-signal circuits with genetic algorithms by using SABER simulation method to inject faults and data collection based on a brief discussion of basic principles and operation of genetic algorithms, focused

Shangcong Feng; Xiaofeng Wang

2012-01-01

158

Fault Diagnosis with Bayesian Networks: Application to the Tennessee Eastman Process  

E-print Network

Fault Diagnosis with Bayesian Networks: Application to the Tennessee Eastman Process Sylvain VERRON problem: the Tennessee Eastman Process. Three kinds of faults are taken into account on this complex process. The objective is to obtain the minimal recognition error rate for these 3 faults. Results

Paris-Sud XI, Université de

159

Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method  

Microsoft Academic Search

A method of compressor valve fault diagnosis using information entropy and SVM is proposed in this paper. The main obstacle in the fault diagnosis focuses on the low non-linear pattern recognition performance and small sample number. Therefore, the information entropy, which is flexible and tolerant to the non-linearity problem, is applied to analyze the characteristic of the signals. SVM is

Houxi Cui; Laibin Zhang; Rongyu Kang; Xinyang Lan

2009-01-01

160

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

E-print Network

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

Starzyk, Janusz A.

161

Application of Neuro-fuzzy Network for Fault Diagnosis in an Industrial Process  

Microsoft Academic Search

The purpose of this paper is to present results that were obtained in fault diagnosis of an industrial process. The diagnosis algorithm combines an artificial neural network (ANN) based supplement of a fuzzy system in a block-oriented configuration. A methodology for designing the system is described. As a motivating example, a chemical plant with a recycle stream is considered. Faults

Tianqi Yang

2007-01-01

162

Application of AI tools in fault diagnosis of electrical machines and drives-an overview  

Microsoft Academic Search

Condition monitoring leading to fault diagnosis and prediction of electrical machines and drives has recently become of importance. The topic has attracted researchers to work in during the past few years because of its great influence on the operational continuation of many industrial processes. Correct diagnosis and early detection of incipient faults result in fast unscheduled maintenance and short down

Mohamed A. Awadallah; Medhat M. Morcos

2003-01-01

163

Fault Diagnosis of Metro Shield Machine Based on Rough Set and Neural Network  

Microsoft Academic Search

Due to massive date to be monitored for Metro shield machine, in order to solve the problems of knowledge acquisition bottlenecks and complexity structure of network structure and long traing time which based on expert system and neural network fault diagnosis methods. This article will introduces rough set theory to the subway shield machine fault diagnosis, Propose a method which

Yang Yu; Chao Han

2010-01-01

164

Fuzzy-logic based trend classification for fault diagnosis of chemical Sourabh Dash a  

E-print Network

. Introduction One of the important reasons for developing a trend analysis technique is the subsequent use: Process monitoring; Qualitative trend analysis; Fuzzy logic; Classification; Fault diagnosis 1 of the classified trends in fault diagnosis. Hence, typical reasoning systems that depend on the use of `event

Koppelman, David M.

165

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

Microsoft Academic Search

This paper proposes a new connectionist (or neural network) expert system for the online fault diagnosis of a power substation. The connectionist expert diagnosis system has similar profile to 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

Hong-Tzer Yang; Wen-Yeau Chang; Ching-Lien Huang

1995-01-01

166

Application of Adaptive Estimation Techniques on Battery Fault Diagnosis Amardeep Singh1  

E-print Network

electronic gadgetry, critical medical devices, hybrid & electric vehicles to name a few. Our study aims fault diagnosis. Advisor(s): Afshin Izadian, Electrical & Computer Engineering Technology, Purdue SchoolApplication of Adaptive Estimation Techniques on Battery Fault Diagnosis Amardeep Singh1 , Afshin

Zhou, Yaoqi

167

Real-time antenna fault diagnosis experiments at DSS 13  

NASA Technical Reports Server (NTRS)

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.

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

1992-01-01

168

Human problem solving performance in a fault diagnosis task  

NASA Technical Reports Server (NTRS)

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.

Rouse, W. B.

1978-01-01

169

Fault Diagnosis Based on Bond Graph for Feedwater  

Microsoft Academic Search

The fault tree analysis method based on bond graph for feed water pump is introduced in this paper. Using a knowledge representation of bond graph modeling, which includes system structural, functional and behavioral information and there relation, fault tree based cause-effect reasoning is created by assigning qualitative value of parameters. Multiple fault hypotheses is employed to simplify branches of fault

Xiyun Yang; Xiaojuan Han; Haining Zhou; Daping Xu

2007-01-01

170

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

E-print Network

SPECTRUM CLASSIFICATION FOR EARLY FAULT DIAGNOSIS OF THE LP GAS PRESSURE REGULATOR BASED The present paper describes a frequency spectrum classi- fication method for fault diagnosis of the LP gas, such as polyno- mial or Gaussian kernels, or the conventional fault diagnosis method and Gaussian Mixture Model

Higuchi, Tomoyuki

171

A new engine fault diagnosis method based on spectrometric oil analysis  

NASA Astrophysics Data System (ADS)

According to statistics, wear fault is about sixty percent to eighty percent of all the machinery faults. Spectrometric oil analysis is an important condition monitoring and fault diagnosis technique for machinery maintenance. In practice, there are two existing fault diagnosis model of the engine based on spectrometric oil analysis, namely concentration model and gradient model. However, the two above models have their respective disadvantages in condition monitoring and fault diagnosis of the engine. In this paper, a new condition monitoring and fault diagnosis method, proportional model is described. Proportional model use the correlation among the elements in the lubricating oil to detect wear condition and occurring faults in the engine. Then the limit value of proportional model is established by analyzing a lot of spectrum data. In order to validate the availability and effect of proportional model, this paper apply proportional model to an engine and sampling the lubricating oil every 5 hours. Through analyzing the lubricating oil by spectrometer, we find that proportional model could find the abnormal wear information in spectrum data, give more accurate result of wear condition and give the fault form in the engine. The results from this paper prove that this method based on proportional model is applicable and available in condition monitoring and fault diagnosis of the engine.

Gao, Jingwei; Zhang, Peilin; Wang, Zhengjun; Zeng, Degui

2006-11-01

172

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

SciTech Connect

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.

Togami, Masato [Nagoya MFG (Japan)] [Nagoya MFG (Japan); Abe, Norihiro [Kyushu Inst. of Tech., Iizuka (Japan)] [Kyushu Inst. of Tech., Iizuka (Japan); Kitahashi, T. [Osaka Univ., Ibaraki (Japan)] [Osaka Univ., Ibaraki (Japan); Ogawa, Harunao [Togami Electric, Saga (Japan)] [Togami Electric, Saga (Japan)

1995-10-01

173

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

NASA Astrophysics Data System (ADS)

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.

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

2006-03-01

174

Fault diagnosis of air-conditioning fan based on RBF neural networks algorithm  

Microsoft Academic Search

In order to overcome the problems of slow rate of convergence, falling easily into local minimum and instability of learning performance caused by initial value in BP algorithm, the diagnosis method based on RBF neural networks was proposed. And the diagnosis method is applied to air-conditioning fan fault diagnosis. The result shows that RBF network has very high learning convergence

Wang Yi

2010-01-01

175

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

NASA Astrophysics Data System (ADS)

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

Hong, Liu; Dhupia, Jaspreet Singh

2014-03-01

176

Neural network based fault diagnosis and fault tolerant control for BLDC motor  

Microsoft Academic Search

A fault diagnostics and fault tolerant control system for controller of brushless direct current motor is designed. The neural network state observer is trained by real nonlinear control system. From the residual difference between outputs of actual system and neural network observer, the fault of control system is detected and determined. The simulation results and study on fault diagnostics are

Zheng Li

2009-01-01

177

Early Oscillation Detection for DC/DC Converter Fault Diagnosis  

NASA Technical Reports Server (NTRS)

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.

Wang, Bright L.

2011-01-01

178

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

NASA Technical Reports Server (NTRS)

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.

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

1986-01-01

179

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

PubMed

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

Zhao, Jinsong; Huang, Jianchao; Sun, Wei

2008-11-01

180

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

E-print Network

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

Wu, Lei

2009-05-15

181

Fault diagnosis for air contamination events in three-dimensional environments  

Microsoft Academic Search

We propose an Extended Implicit Kalman Filter for fault diagnosis of air contaminants in three-dimensional environments. In order to accurately detect and diagnose air contamination faults in environments such as a space station, we combine the use of an Extended Implicit Kalman Filter with the use of sensitivity matrices that provide the filter with an initial guess of the capacity

Anand P. Narayan; W. Fred Ramirez

2000-01-01

182

Analog system-level fault diagnosis based on symbolic method in the frequency domain  

E-print Network

requirements. This concept also holds in the fault diagnosis area. The more sensitive the circuit, the more easier to diagnose the fault. For practical purpose, low sensitivity circuits are the goal of circuit designing, but they are the diBicult case...

You, Zhihong

1993-01-01

183

Condition monitoring and fault diagnosis of electrical machines-a review  

Microsoft Academic Search

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

S. Nandi; H. A. Toliyat

1999-01-01

184

Study on CLIPS-based real-time fault diagnosis system used in power plant  

Microsoft Academic Search

Information of faults in power plant is too much and very complex, mostly, it is difficult to deal with effectively when a breakdown takes place. In response to this situation, a method of designing a real-time fault diagnosis system based on CLIPS is presented in this paper. It takes full advantage of CLIPS, VC++ and SQL SERVER database to construct

Dongyang Dou; Yingkai Zhao

2008-01-01

185

Fault diagnosis for a hydraulic drive system using a parameter-estimation method  

Microsoft Academic Search

Fault diagnosis using a parameter-estimation method is investigated in this paper. A digital state variable filter is employed to obtain derivatives of the variables, and an interpolation technique is used to approximate the values of the variables between samples. The method is applied to a hydraulic test rig based on real data, and the simulated faults — changes in the

D. Yu

1997-01-01

186

Analog circuit fault diagnosis approach using optimized SVMs based on MST algorithm  

Microsoft Academic Search

The classification accuracy and efficiency of multiclass SVMs are largely dependent on the SVM combination strategy in analog circuits fault diagnosis. An optimized SVM extension strategy is presented in this paper, which uses minimum spanning tree (MST) algorithm to simplify the SVM structure and decrease the classification errors. By taking the separability measure of fault classes as edge weight of

Guoming Song; Shuyan Jiang; Houjun Wang; Liu Hong

2011-01-01

187

A Hierarchical Fault Diagnosis Method Using a Decision Support System Applied to a Chemical Plant  

E-print Network

A Hierarchical Fault Diagnosis Method Using a Decision Support System Applied to a Chemical Plant D. A hierarchical scheme of fault detection and isolation based on Decision Support System (DSS) is presentedHeuristic Symptoms DECISION Figure 1: Architecture of a Decision Support System This paper is organised as follows

Paris-Sud XI, Université de

188

Fault Detection and Diagnosis Techniques for Liquid-Propellant Rocket Propellant Engines  

NASA Astrophysics Data System (ADS)

Fault detection and diagnosis plays a pivotal role in the health-monitoring techniques for liquid- propellant rocket engines. This paper firstly gives a brief summary on the techniques of fault detection and diagnosis utilized in liquid-propellant rocket engines. Then, the applications of fault detection and diagnosis algorithms studied and developed to the Long March Main Engine System(LMME) are introduced. For fault detection, an analytical model-based detection algorithm, a time-series-analysis algorithm and a startup- transient detection algorithm based on nonlinear identification developed and evaluated through ground-test data of the LMME are given. For fault diagnosis, neural-network approaches, nonlinear-static-models based methods, and knowledge-based intelligent approaches are presented. Keywords: Fault detection; Fault diagnosis; Health monitoring; Neural networks; Fuzzy logic; Expert system; Long March main engines Contact author and full address: Dr. Jianjun Wu Department of Astronautical Engineering School of Aerospace and Material Engineering National University of Defense Technology Changsha, Hunan 410073 P.R.China Tel:86-731-4556611(O), 4573175(O), 2219923(H) Fax:86-731-4512301 E-mail:jjwu@nudt.edu.cn

Wua, Jianjun; Tanb, Songlin

2002-01-01

189

A fault detection and diagnosis system based on input and output residual generation scheme for a CSTR benchmark process  

Microsoft Academic Search

Aim of this study is to propose fault detection and diagnosis (FDD) algorithm based on input and output residuals that consider both sensor and actuator faults separately. The existing methods which have capability of fault diagnosis and its magnitude estimation suffer from great computational complexity, so they would not be suitable for the real-time applications. The proposed method in this

Mehdi Rezagholizadeh; Karim Salahshoor; Ebrahim Moradi Shahrivar

2010-01-01

190

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

E-print Network

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

Koutsoukos, Xenofon D.

191

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

E-print Network

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

Rucci, Michele

192

An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network  

Microsoft Academic Search

In the present study, a fault diagnosis system is proposed for internal combustion engines using wavelet packet transform (WPT) and artificial neural network (ANN) techniques. In fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. The signal processing algorithm of the present system is gained from previous work used for speech recognition.

Jian-da Wu; Chiu-hong Liu

2009-01-01

193

Performance of diagnosis methods for IGBT open circuit faults in three phase voltage source inverters for AC variable speed drives  

Microsoft Academic Search

Variable speed drives have become industrial standard in many applications. Therefore fault diagnosis of voltage source inverters is becoming more and more important. One possible fault within the inverter is an open circuit transistor fault. An overview of the different strategies to detect this fault is given, including the algorithms used to localize the open transistor. Previous work showed significant

K. Rothenhagen; F. W. Fuchs

2005-01-01

194

Fuzzy Set Theory and Fault Tree Analysis based Method Suitable for Fault Diagnosis of Power Transformer  

Microsoft Academic Search

The fault detection and analysis for power transformer are the key measures to improve the security of power systems and the reliability of power supply. Due to the complicity of the power transformer structure and the variations in operating conditions, the occurrence of a fault inside power transformer is uncertain and random. Until now, the fault statistics of power transformer

Tong Wu; Guangyu Tu; Z. Q. Bo; A. Klimek

2007-01-01

195

Algorithm of Data Mining and its Application in Fault Diagnosis for Wind Turbine  

Microsoft Academic Search

Because more and more characteristic parameters are used to describe machine vibration, few and more parameters selected will usually affect the right ration of diagnosis. In this paper an algorithm of data mining based on fuzzy clustering is approached, which is used to mine the optimal characteristic parameters from the parameters describing the vibration wave of gear cases of wind

Zhigang Chen; Xiangjiao Lian; Huiyuan Yu; Zhongli Bao

2009-01-01

196

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

NASA Technical Reports Server (NTRS)

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.

Litt, Jonathan; Kurtkaya, Mehmet; Duyar, Ahmet

1994-01-01

197

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

Microsoft Academic Search

The machinery fault diagnosis is important for improving reliability and performance of systems. Many methods such as Time Synchronous Average (TSA), Fast Fourier Transform (FFT)-based spectrum analysis and short-time Fourier transform (STFT) have been applied in fault diagnosis and condition monitoring of mechanical system. The above methods analyze the signal in frequency domain with low resolution, which is not suitable

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

2008-01-01

198

Current-based feed axis condition monitoring and fault diagnosis  

Microsoft Academic Search

A kind of numerical control machine tool feed axis condition monitoring system based on AC servo motor current is proposed. Firstly, the mathematical model between motor current and the fault is established. Then the test principle is profoundly investigated. And the frequency response between the disturbance torque from the fault and motor current is analyzed based on Matlab\\/SIMULINK. To obtain

Yuqing Zhou; Xuesong Mei; Yun Zhang; Gedong Jiang; Nuogang Sun

2009-01-01

199

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

PubMed Central

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

Yu, Bing; Liu, Dongdong; Zhang, Tianhong

2011-01-01

200

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

PubMed

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

Yu, Bing; Liu, Dongdong; Zhang, Tianhong

2011-01-01

201

Engine Fault Diagnosis using DTW, MFCC and FFT  

NASA Astrophysics Data System (ADS)

. 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.

Singh, Vrijendra; Meena, Narendra

202

New informative features for fault diagnosis of industrial systems by supervised classification  

Microsoft Academic Search

The purpose of this article is to present a method for industrial process diagnosis. We are interested in fault diagnosis considered as a supervised classification task. The interest of the proposed method is to take into account new features (and so new informations) in the classifier. These new features are probabilities extracted from a Bayesian network comparing the faulty observations

Sylvain VERRON; Teodor TIPLICA; Abdessamad KOBI

2010-01-01

203

Fault Diagnosis of VLSI Circuits with Cellular Automata based Pattern Classifier  

E-print Network

#ciency of the model in respect of memory overhead, execution speed and percentage of diagnosis. Keywords: cellularFault Diagnosis of VLSI Circuits with Cellular Automata based Pattern Classifier Biplab K Sikdar 1. A special class of Cellular Automata (CA) referred to as Multiple Attractor CA (MACA) is employed

Ganguly, Niloy

204

CONTRIBUTION OF BELIEF FUNCTIONS TO HIDDEN MARKOV MODELS WITH AN APPLICATION TO FAULT DIAGNOSIS  

E-print Network

CONTRIBUTION OF BELIEF FUNCTIONS TO HIDDEN MARKOV MODELS WITH AN APPLICATION TO FAULT DIAGNOSIS- periments concern a diagnosis problem. Keywords: Evidential Hidden Markov Models, State Se- quence or evidence theory, the latter being more general. Hidden Markov Model (HMM) [1] is a famous proba- bilistic

Paris-Sud XI, Université de

205

1116 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 51, NO. 5, OCTOBER 2002 SBT Soft Fault Diagnosis in Analog Electronic  

E-print Network

Fault Diagnosis in Analog Electronic Circuits: A Sensitivity-Based Approach by Randomized Algorithms algorithms (RAs), sen- sitivity analysis. I. INTRODUCTION TESTING and diagnosis of electronic devices--This paper addresses the fault diagnosis issue based on a simulation before test philosophy in analog

Alippi, Cesare

206

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

NASA Astrophysics Data System (ADS)

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.

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

2014-12-01

207

The Application of Lifting Wavelet Transform in the Fault Diagnosis of Reciprocating Air Compressor  

Microsoft Academic Search

Lifting algorithm gives a simple and effective method for the construction of orthogonal wavelet, which is no longer the Fourier transformation completely, but the obtained wavelet has all the advantages of the first generation. This article makes use of lifting wavelet transformation to de-noise the vibration signals of the air compressor sensor, and to diagnose effectively the fault of reciprocating

Wang Lijun; Ma Lili; Huang Yongliang

2010-01-01

208

Low-cost motor drive embedded fault diagnosis systems  

E-print Network

offline, using data acquisition system, and online, employing the TMS320F2812 DSP to prove the effectiveness of the proposed tool. In addition to reference frame theory, another digital signal processor (DSP)-based phasesensitive motor fault signature...

Akin, Bilal

2009-05-15

209

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

Microsoft Academic Search

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

Masatake Kawada; Koji Yamada; Katsuya Yamashita

2003-01-01

210

Using Vibration Monitoring for Local Fault Detection on Gears Operating Under Fluctuating Load Conditions  

NASA Astrophysics Data System (ADS)

Gearboxes often operate under fluctuating load conditions during service. Conventional techniques for monitoring vibration are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the gearbox. However, this assumption is not valid for fluctuating load conditions. To find a methodology that could deal with such conditions, experiments were conducted on a gearbox test rig with different levels of tooth damage severity and the capability of applying fluctuating loads to the gear system. Different levels of fluctuation in constant loads as well as in sinusoidal, step and chirp loads were considered. The test data were order tracked and time synchronously averaged with the rotation of the shaft in order to compensate for the variation in rotational speed induced by the fluctuating loads. A pseudo-Wigner-Ville distribution was then applied to the test data, in order to identify the influence of the fluctuating load conditions. In this work, a vibration waveform normalisation approach is presented, which enables the use of the pseudo-Wigner-Ville distribution to indicate deteriorating fault conditions under fluctuating load conditions. Statistical parameters and various other features were extracted from the distribution in order to indicate the linear separation of the values for various fault conditions, after applying the vibration waveform normalisation approach. Feature vectors were compiled for the various fault and load conditions. Mahalanobis distances were calculated between the various feature vectors and an average feature vector was compiled from data measured on the undamaged gearbox. It was proved that the Mahalanobis distance could be used as a single parameter, which can readily be monotonically trended to indicate the progression of a fault condition under fluctuating load conditions. It was shown that a single layer perceptron network could be trained with the perceptron learning rule within a finite number of iterations.

Stander, C. J.; Heyns, P. S.; Schoombie, W.

2002-11-01

211

ATE applied into fault modeling and fault diagnosis of AC servo motor PWM driver system  

Microsoft Academic Search

AC servo motor PWM driver system (including power module, power PWM driver board, cable, motor and photoelectric encoder\\/decoder) is a key sub-system of semiconductor assembly and packaging equipment. Aimed at its high fault rate, in this document we build the fault models of system based on PWM (Pulse Width Modulation) voltage, controller command and position feedback, find the test method

Li Baoan; Fan Ju; P. Liu Chou Kee

2005-01-01

212

Neural Networks and Fault Probability Evaluation for Diagnosis Issues  

PubMed Central

This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method. PMID:25132845

Lefebvre, Dimitri; Guersi, Noureddine

2014-01-01

213

Fault diagnosis based on signed directed graph and support vector machine  

NASA Astrophysics Data System (ADS)

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.

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

2011-12-01

214

Fault diagnosis based on signed directed graph and support vector machine  

NASA Astrophysics Data System (ADS)

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.

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

2012-01-01

215

Fault detection and diagnosis using neural network approaches  

NASA Technical Reports Server (NTRS)

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.

Kramer, Mark A.

1992-01-01

216

Model-Based Fault Diagnosis for Turboshaft Engines  

NASA Technical Reports Server (NTRS)

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.

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

1998-01-01

217

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

NASA Technical Reports Server (NTRS)

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.

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

1987-01-01

218

Implementation of a research prototype onboard fault monitoring and diagnosis system  

NASA Technical Reports Server (NTRS)

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.

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

1987-01-01

219

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

NASA Technical Reports Server (NTRS)

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.

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

1988-01-01

220

Board-level fault diagnosis using Bayesian inference  

Microsoft Academic Search

Increasing integration densities and high operating speeds are leading to subtle manifestations of defects at the board level. Board-level functional test is therefore necessary for product qualification. The diagnosis of functional failures is especially challenging, and the cost associated with board-level diagnosis is escalating rapidly. An effective and cost-efficient board-level diagnosis strategy is needed to reduce manufacturing cost and time-to-market,

Zhaobo Zhang; Zhanglei Wang; Xinli Gu; Krishnendu Chakrabarty

2010-01-01

221

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

PubMed Central

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

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

2013-01-01

222

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

PubMed

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

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

2013-01-01

223

System design of ARM-based handset vibration analyzer  

Microsoft Academic Search

With application and popularity of vibration monitoring and fault diagnosis technology, it is also raising that performance requirement of measurement and analysis instruments for vibration. In order to enhance the current performance and function of portable vibration analyzer, using the technique of embedded system, new portable measurement and analysis instruments for vibration based on ARM technology has developed in this

He Qing; Du Dongmei; Tang Bin

2009-01-01

224

Investigation of the synthetic experiment system of machine equipment fault diagnosis  

NASA Astrophysics Data System (ADS)

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.

Liu, Hongyu; Xu, Zening; Yu, Xiaoguang

2008-12-01

225

A novel fuzzy logic approach to transformer fault diagnosis  

Microsoft Academic Search

Dissolved gas in oil analysis is an well established in-service technique for incipient fault detection in oil-insulated power transformers. A great deal of experience and data in dissolved gas in oil analysis (DGA) is now available within the utilities. Actually, diagnostic interpretations were solely done by human experts using past knowledge and standard techniques such as the ratio method. In

Syed Mofizul Islam; Tony Wu; Gerard Ledwich

2000-01-01

226

Non-cooperative Diagnosis of Submarine Cable Faults  

E-print Network

-Pacific Unity submarine cable system can transmit data between Japan and the west coast of the United States up to 4.8 Terabits per second (Tbits/s). Dramatic capacity upgrades to the existing Asia- Europe cable reported [11]. Data loss and substantial service interruption as a result of submarine cable faults

Chang, Rocky Kow-Chuen

227

Case study of Chilled Water Loop Low Delta-T Fault Diagnosis  

E-print Network

Case study of Chilled Water Loop Low ?T Fault Diagnosis Lei Wang1, Ph.D., P.E. Ken Meline2, P.E. James Watt1, P.E. Bahman Yazdani1 P.E., David E. Claridge1, Ph.D., P.E 1Energy Systems Laboratory, Texas A&M Engineering Experiment Station... Case Study of Chilled Water Loop Low DT Fault Diagnosis Presented by Lei Wang Ph.D. P.E. Energy Systems Laboratory, Texas A&M University System Sep. 15, 2014 Energy Systems Laboratory p. 1 ESL-IC-14-09-12a Proceedings of the 14th International...

Wang, L.; Meline, K.; Watt, J.

2014-01-01

228

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

NASA Technical Reports Server (NTRS)

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.

Bosworth, Edward L., Jr.

1987-01-01

229

Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing  

NASA Astrophysics Data System (ADS)

Roller bearing is one of the most widely used elements in rotary machines. Condition monitoring of such elements is conceived as pattern recognition problem. Pattern recognition has two main phases: feature extraction and feature classification. Statistical features like minimum value, standard error and kurtosis, etc. are widely used as features in fault diagnostics. These features are extracted from vibration signals. A rule set is formed from the extracted features and input to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper presents the use of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezo-electric transducer is captured for the following conditions—good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The statistical features are extracted and good features that discriminate the different fault conditions of the bearing are selected using decision tree. The rule set for fuzzy classifier is obtained once again by using the decision tree. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging.

Sugumaran, V.; Ramachandran, K. I.

2007-07-01

230

Artificial neural network application for space station power system fault diagnosis  

NASA Technical Reports Server (NTRS)

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.

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

1995-01-01

231

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

NASA Astrophysics Data System (ADS)

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

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

2013-01-01

232

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)

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.

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

2014-06-01

233

Fault Diagnosis of AC Squirrel-cage Asynchronous Motors based on Wavelet Packet-Neural Network  

Microsoft Academic Search

Squirrel-cage asynchronous motors are used widely in industry production process. It is significant to improve squirrel-cage asynchronous motors diagnosis technique in application. It helps to decrease the occurrence of accident and reduce the cost of maintenance. Based on the wavelet packet-neural network the scheme on the real-time diagnosis of the stator, bearing, and eccentricity fault of squirrel-cage asynchronous motors is

Zaiping Chen; Jing Meng; Bin Liang; Dan Guo

2007-01-01

234

A method of neuro-fuzzy computing for effective fault diagnosis  

Microsoft Academic Search

The purpose of this paper is to present results that were obtained in fault diagnosis of an industrial process. The diagnosis algorithm is based on a three-layer neuro-fuzzy network theory. We present a new technique for the treatment of overlaps among adjoining fuzzy sets. Inputs of the network are the process I\\/O data, such as pressure and temperature, parameters estimated

Tianqi Yang

2005-01-01

235

Online fault diagnosis of hybrid electric vehicles based on embedded system Changqing Song  

Microsoft Academic Search

In this paper, embedded system was used to work for the online fault diagnosis of hybrid electric vehicles (HEV). This system took 32-bit embedded one as a hardware platform, customized a WinCE6.0 operation system and used Embedded Visual C++ (EVC) as the tool to design the embedded application. Through this online diagnosis device, the failure phenomenon, failure causes and failure

Changqing Song; Jun Li; Dawei Qu; Dongqing Zhou; Luyan Fan

2010-01-01

236

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

NASA Astrophysics Data System (ADS)

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.

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

2013-08-01

237

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

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

238

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

NASA Astrophysics Data System (ADS)

In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the fault-test dependency matrix that couples the failed tests and faults via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple fault diagnosis problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a facility, respectively. The set-covering matrix encapsulates the relationship among the rows (tests or demand points) and columns (faults or locations) of the system at each time. By relaxing the coupling constraints using Lagrange multipliers, the DSC problem can be decoupled into independent subproblems, one for each column. Each subproblem is solved using the Viterbi decoding algorithm, and a primal feasible solution is constructed by modifying the Viterbi solutions via a heuristic. The proposed Viterbi-Lagrangian relaxation algorithm (VLRA) provides a measure of suboptimality via an approximate duality gap. As a major practical extension of the above problem, we also consider the problem of diagnosing faults with delayed test outcomes, termed delay-dynamic set-covering (DDSC), and experiment with real-world problems that exhibit masking faults. Also, we present simulation results on OR-library datasets (set-covering formulations are predominantly validated on these matrices in the literature), posed as facility location problems. Finally, we implement these algorithms to solve problems in aerospace and automotive applications. Firstly, we address the diagnostic ambiguity problem in aerospace and automotive applications by developing a dynamic fusion framework that includes dynamic multiple fault diagnosis algorithms. This improves the correct fault isolation rate, while minimizing the false alarm rates, by considering multiple faults instead of the traditional data-driven techniques based on single fault (class)-single epoch (static) assumption. The dynamic fusion problem is formulated as a maximum a posteriori decision problem of inferring the fault sequence based on uncertain outcomes of multiple binary classifiers over time. The fusion process involves three steps: the first step transforms the multi-class problem into dichotomies using error correcting output codes (ECOC), thereby solving the concomitant binary classification problems; the second step fuses the outcomes of multiple binary classifiers over time using a sliding window or block dynamic fusi

Kodali, Anuradha

239

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

PubMed

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

Seera, Manjeevan; Lim, Chee Peng

2014-04-01

240

Fault diagnosis in an expert system for health services management in the tropics.  

PubMed

An integrated large-scale expert system called Health-2000, for the management of health services in regions where tropical diseases are endemic, has been designed. This system combines knowledge and databases, the contents of which are operated upon by an inference engine, to produce usable information. The system allows a host of applications, ranging from medical diagnosis to fault detection and preventive maintenance of biomedical equipment. The theoretical background and approach used in the development of the fault diagnosis and equipment maintenance sub-system of Health-2000 is presented. Model-based knowledge acquisition, and an extension of the Failure Modes, Effects and Criticality Analysis are two methodologies applied to build its knowledge bases. The inference engine which supports backward and forward chaining, operates on numerical and non-numerical facts, and uses fuzzy logic to handle vague and uncertain knowledge. Fault isolation proceeds in a top-down fashion, from equipment sub-system, to modules and components. PMID:9242011

Kwankam, S Y; Asoh, D A; Boyom, S F

1997-02-01

241

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

NASA Astrophysics Data System (ADS)

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 + +.

Chen, Changzheng; Li, Yun

2011-10-01

242

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

NASA Technical Reports Server (NTRS)

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.

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

2010-01-01

243

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

SciTech Connect

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.

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

2008-09-01

244

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

E-print Network

of a conventional PWM voltage source inverter (VSI) system for an induction motor are investigated in [2]. Then the inverter systems and the induction motors. The protection devices will disconnect the power sources fromFault Diagnosis System for a Multilevel Inverter Using a Neural Network Surin Khomfoi Leon M

Tolbert, Leon M.

245

Fault diagnosis of power transformer based on support vector machine with genetic algorithm  

Microsoft Academic Search

Diagnosis of potential faults concealed inside power transformers is the key of ensuring stable electrical power supply to consumers. Support vector machine (SVM) is a new machine learning method based on the statistical learning theory, which is a powerful tool for solving the problem with small sampling, nonlinearity and high dimension. The selection of SVM parameters has an important influence

Sheng-wei Fei; Xiao-bin Zhang

2009-01-01

246

Abstract: Within the model based diagnosis community, Fault Detection and Isolation (FDI) techniques for hybrid systems  

E-print Network

Abstract: Within the model based diagnosis community, Fault Detection and Isolation (FDI Networks models are obtained they are used to generate residuals and to achieve FDI without any need and Isolation (FDI) techniques [11, 26] must be implemented to safeguard the 1 with Laboratoire CRe

Boyer, Edmond

247

Decentralised fault detection and diagnosis in navigation systems for unmanned aerial vehicles  

Microsoft Academic Search

Autonomous unmanned aerial vehicles (UAVs) are a technological phenomenon sweeping the world stage. Full autonomy implies that the guidance and navigation system employed must exhibit the highest level of integrity. This paper looks at the parity space fault detection and diagnosis (FDD) methods, and its applicability in fully autonomous guidance and navigation systems in a decentralised system architecture. Using the

S. M. Magrabi; P. W. Gibbens

2000-01-01

248

Hierarchical Modelling of Automotive Sensor Front-Ends For Structural Diagnosis of Aging Faults  

E-print Network

Hierarchical Modelling of Automotive Sensor Front-Ends For Structural Diagnosis of Aging Faults h.g.kerkhoff@utwente.nl Abstract: The semiconductor industry for automotive applications is growing, dependability, reliability, aging models, hierarchical interfacing, analogue automotive front-ends. I

Wieringa, Roel

249

On Electronic Equipment Fault Diagnosis Using Least Squares Wavelet Support Vector Machines  

Microsoft Academic Search

A systematic approach for fault diagnosis of analog circuits based on least squares wavelet support vector machines and wavelet lifting transform is presented, and is used in the scout radar electronic equipment. Firstly, output voltage signals under faulty conditions are obtained from analog circuits test points and noise is removed from signals with wavelet lifting transform. Then wavelet coefficients of

Zhiyong Luo; Zhongke Shi

2006-01-01

250

Natural Language Interface for Fault Diagnosis System of Nuclear Power Plant Control Systems  

Microsoft Academic Search

A fault diagnosis system was developed to improve the availability and maintainability of control systems in nuclear power plants. To facilitate man-machine communications in the system a natural language interface was introduced. Features of the interface include two-step analysis of the meaning of input sentences, identification of the kind of input content using sentence patterns, and retrieval of the information

Yukiharu OHGA; Yukio NAGAOKA; Satoshi SUZUKI; Tetsuo ITO

1990-01-01

251

Advanced Signal Processing Techniques for Fault Detection and Diagnosis in a Wind Turbine  

E-print Network

Advanced Signal Processing Techniques for Fault Detection and Diagnosis in a Wind Turbine Induction rotor bars and bearing damages. Index Terms--Wind turbines, motor current signature analy- sis, time factors, such as wind speed and acoustic noise, wind parks are being mainly constructed offshore. Studies

Paris-Sud XI, Université de

252

Wind Turbines Condition Monitoring and Fault Diagnosis Using Generator Current Amplitude  

E-print Network

detection in a Doubly-Fed Induction Generator (DFIG) based wind turbine for stationary and nonstationary cases. Index Terms--Wind turbine, DFIG, fault detection, diagnosis, amplitude modulation, Hilbert and maintaining older system, becomes more costly and challenging with obsolescence of key components. DFIG

Paris-Sud XI, Université de

253

Testing and Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures  

E-print Network

Testing and Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures Ian G. Harris-mail: harris@ecs.umass.edu, tessier@ecs.umass.edu Abstract As IC densities are increasing, cluster-based FPGA architectures are becoming the architecture of choice for major FPGA manufacturers. A cluster-based architecture

Harris, Ian G.

254

A review of recent advances in wind turbine condition monitoring and fault diagnosis  

Microsoft Academic Search

The state-of-the-art advancement in wind turbine condition monitoring and fault diagnosis for the recent several years is reviewed. Since the existing surveys on wind turbine condition monitoring cover the literatures up to 2006, this review aims to report the most recent advances in the past three years, with primary focus on gearbox and bearing, rotor and blades, generator and power

Bin Lu; Yaoyu Li; Xin Wu; Zhongzhou Yang

2009-01-01

255

Condition monitoring and fault diagnosis of electrical motors-a review  

Microsoft Academic Search

Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as

Subhasis Nandi; Hamid A. Toliyat; Xiaodong Li

2005-01-01

256

Distance rejection in a bayesian network for fault diagnosis of industrial systems  

Microsoft Academic Search

The purpose of this article is to present a method for industrial process diagnosis with Bayesian network. The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a bayesian network in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example: the

Sylvain VERRON; Teodor TIPLICA; Abdessamad KOBI

2008-01-01

257

Fault diagnosis of industrial systems by conditional Gaussian network including a distance rejection criterion  

Microsoft Academic Search

The purpose of this article is to present a method for industrial process diagnosis with Bayesian network, and more particularly with conditional Gaussian network (CGN). The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a CGN in order to detect new types of fault. The performances of this method are evaluated on

Sylvain Verron; Teodor Tiplica; Abdessamad Kobi

2010-01-01

258

A NEW PROCEDURE BASED ON MUTUAL INFORMATION FOR FAULT DIAGNOSIS OF INDUSTRIAL SYSTEMS  

Microsoft Academic Search

The purpose of this article is to present a new procedure for industrial process diagnosis. This method is based on bayesian classiers. A feature selection is done before the classication between the dieren t faults of a process. The feature selection is based on a new result about mutual information that we demonstrate. The performances of this method are evaluated

Sylvain Verron; Teodor Tiplica; Abdessamad Kobi

259

Fault diagnosis of inter-turn short-circuit in rotor windings based on artificial intelligence  

Microsoft Academic Search

The inter turn short-circuit in rotor windings take the induced electromotive force, which is detected by detecting coil, as a study object. And a method of fault diagnosis based on Wavelet analysis and neural network is presented. The induced electromotive force is analyzed by wavelet packet, which can decompose and construct the energy eigenvectors. Then set up the neural network

Zhao Juan

2010-01-01

260

Impact of propagation of fault signals on industrial diagnosis using current signature analysis  

Microsoft Academic Search

Diagnosis of the significant events in electrical equipments is a challenging research area. Motor current signature analysis provides good results in laboratory environment. In real life situation electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals.

Alireza Gheitasi; Adnan Al-Anbuky; Tek Tjing Lie

2011-01-01

261

An expert system for fault diagnosis in internal combustion engines using probability neural network  

Microsoft Academic Search

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a

Jian-da Wu; Peng-hsin Chiang; Yo-wei Chang; Yao-jung Shiao

2008-01-01

262

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

Microsoft Academic Search

Several aspects of the aircraft domain make inflight diagnosis difficult. Many stem from the fact that the aircraft is in operation during and after the occurrence of a fault. These aspects include failure propagation, operator compensation, and lack of complete information. Still other aspects include responding rapidly to a failure, recognizing multiple failures, and predicting the effect of the failure

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

1987-01-01

263

Fault diagnosis using dynamic trend analysis: A review and recent developments  

Microsoft Academic Search

Dynamic trend analysis is an important technique for fault detection and diagnosis. Trend analysis involves hierarchical representation of signal trends, extraction of the trends, and their comparison (estimation of similarity) to infer the state of the process. In this paper, an overview of some of the existing methods for trend extraction and similarity estimation is presented. A novel interval-halving method

Mano Ram Maurya; Raghunathan Rengaswamy; Venkat Venkatasubramanian

2007-01-01

264

Neural Network Based Algorithm of Soft Fault Diagnosis in Analog Electronic Circuits  

Microsoft Academic Search

Summary This paper addresses the fault diagnosis in analog electronic circuits based on neural network. Iterative algorithm of solving the system of diagnostic equations, using integral sensitivity matrix is also proposed in this paper. To obtain the system of diagnostic equations, the integral sensitivity matrix is used. The integral sensitivity matrix is constructed based on a result of the comparisons

Doried Mismar; Ayman AbuBaker

2010-01-01

265

Modelling methods for improving robustness in fault diagnosis of jet engine system  

Microsoft Academic Search

Modeling uncertainty is an inevitable consequence of the complexity of jet engine systems, and accurate dynamic models can never be fully obtained. The authors concentrate on the derivation of suitable mathematical models of a jet engine, to enable robust fault diagnosis designs to be achieved. The modeling uncertainty can be described as an additional term in the dynamic structure. Based

R. J. Patton; J. Chen; H. Y. Zhang

1992-01-01

266

Development of knowledge base of fault diagnosis system in solar power tower plants  

Microsoft Academic Search

Solar Power Tower (SPT) plant is a hugeous and complicated system, thus there have not been relative research productions on the record in the aspect of developing the knowledge base of its Fault Diagnosis System (FDS) in the whole world yet. In this paper, a modular and hierarchical knowledge base of FDS is designed and developed to use in SPT

S. Guo; D. Y. Liu; T. Z. Guo; C. Xu; D. S. Wan; W. Huang

2009-01-01

267

Artificial Neural Network Based Detection and Diagnosis of Plasma-Etch Faults  

Microsoft Academic Search

Abstract The plasma-etch process is one of many steps in the fabrication of semiconductorwafers. Currently, faultdetection\\/diagnosis for this process is done primarily by visual inspection of graphically displayed process data. By observing these data, experienced technicians can detect and classify many types of faults. The tediousness and intrinsic human unreliability of this method, as well as the high cost of

Shumeet Baluja; Roy A. Maxion

268

An algorithm of soft fault diagnosis for analog circuit based on the optimized SVM by GA  

Microsoft Academic Search

A soft fault diagnosis method for analog circuits based on support vector machine (SVM)is developed in this paper. SVM is a novel machine learning method based on the statistical learning theory, which is a powerful tool for solving the problem with small sampling, nonlinearity and high dimension. The multi-classification SVM methods including one versus rest, one versus one, and decision

Hua Li; Yong Xin Zhang

2009-01-01

269

The application of chaos support vector machines in transformer fault diagnosis  

Microsoft Academic Search

Due to the lack of typical damage samples in the transformer fault diagnosis, a new method based on chaos support vector machines (CSVMs) was proposed. According to the method, the five characteristic gases dissolved in transformer oil were extracted by the K-means clustering (KMC) method as feature vectors, which were input to chaotic optimal multi-classified SVMs for training. Then the

Jisheng Li; Xuefeng Zhao; Zhenquan Sun; Yanming Li

2009-01-01

270

Diagnosis of rotor faults in direct and indirect FOC induction motor drives  

Microsoft Academic Search

This paper is devoted to the diagnosis of rotor faults in direct and indirect rotor flux oriented controlled drives. Different diagnostic techniques are proposed and analyzed with this aim, putting in evidence the advantages and limitations of each one. It is demonstrated that depending on the structure of the control system, some diagnostic techniques may be applicable or not for

S. M. A. Cruz; A. J. M. Cardoso

2007-01-01

271

Rao-Blackwellised Particle Filtering for Fault Diagnosis Nando de Freitas  

E-print Network

Rao-Blackwellised Particle Filtering for Fault Diagnosis Nando de Freitas Department of Computer conditionally Gaussian state space models and an efficient Monte Carlo method known as Rao-Blackwellised parti MODEL AND INFERENCE OBJECTIVES 3 PARTICLE FILTERING 4 RAO-BLACKWELLISED PARTICLE FILTERING 5 EXPERIMENTS

de Freitas, Nando

272

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

NASA Astrophysics Data System (ADS)

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.

Weber, Wolfgang; Jungjohann, Jonas; Schulte, Horst

2014-12-01

273

Hidden Markov Model based fault diagnosis for stamping processes  

Microsoft Academic Search

Metal stamping process plays a very important role in the modern manufacturing industry. Owing to an ever-increasing demand for better quality at reduced cost, a practical on-line monitoring and diagnosis system is of much appeal. However, the stamping process is a complicated transient process involving a large number of variables. It is rather difficult to monitor and diagnose by classical

M. Ge; R. Du; Y. Xu

2004-01-01

274

Effectiveness of MED for Fault Diagnosis in Roller Bearings  

NASA Astrophysics Data System (ADS)

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

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

275

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

NASA Astrophysics Data System (ADS)

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.

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

2005-05-01

276

Fault diagnosis of rotating machinery using an intelligent order tracking system  

NASA Astrophysics Data System (ADS)

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

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

2005-02-01

277

The Marshall Space Flight Center Fault Detection Diagnosis and Recovery Laboratory  

NASA Technical Reports Server (NTRS)

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.

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

2008-01-01

278

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

NASA Technical Reports Server (NTRS)

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.

Abbott, Kathy Hamilton

1991-01-01

279

Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants  

NASA Technical Reports Server (NTRS)

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).

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

1998-01-01

280

Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants  

NASA Technical Reports Server (NTRS)

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.

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

1996-01-01

281

Automatic Channel Fault Detection and Diagnosis System for a Small Animal APD-Based Digital PET Scanner  

E-print Network

Fault detection and diagnosis is critical to many applications in order to ensure proper operation and performance over time. Positron emission tomography (PET) systems that require regular calibrations by qualified scanner operators are good candidates for such continuous improvements. Furthermore, for scanners employing one-to-one coupling of crystals to photodetectors to achieve enhanced spatial resolution and contrast, the calibration task is even more daunting because of the large number of independent channels involved. To cope with the additional complexity of the calibration and quality control procedures of these scanners, an intelligent system (IS) was designed to perform fault detection and diagnosis (FDD) of malfunctioning channels. The IS can be broken down into four hierarchical modules: parameter extraction, channel fault detection, fault prioritization and diagnosis. Of these modules, the first two have previously been reported and this paper focuses on fault prioritization and diagnosis. The purpose of the fault prioritization module is to help the operator to zero in on the faults that need immediate attention. The fault diagnosis module will then identify the causes of the malfunction and propose an explanation of the reasons that lead to the diagnosis. The FDD system was implemented on a LabPET avalanche photodiode (APD)-based digital PET scanner. Experiments demonstrated a FDD Sensitivity of 99.3 % (with a 95% confidence interval (CI) of: [98.7, 99.9]) for major faults. Globally, the Balanced Accuracy of the diagnosis for varying fault severities is 92 %. This suggests the IS can greatly benefit the operators in their maintenance task.

Jonathan Charest; Jean-François Beaudoin; Jules Cadorette; Roger Lecomte; Charles-Antoine Brunet; Réjean Fontaine

2014-06-16

282

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

PubMed

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

Ma, Jian; Lu, Chen; Liu, Hongmei

2015-01-01

283

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

PubMed Central

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

Ma, Jian; Lu, Chen; Liu, Hongmei

2015-01-01

284

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)

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.

Lin, Jinshan; Chen, Qian

2014-10-01

285

Diagnosis of internal combustion engine through vibration and acoustic pressure non-intrusive measurements  

Microsoft Academic Search

The present study proposes a diagnosis methodology for internal combustion engines (I.C.E.) working conditions, by means of non-invasive measurements on the cylinder head, such as acoustic and vibration, related to the internal indicated mean effective pressure. The experimental campaign was carried out on the internal combustion engine of the cogeneration plant at the Faculty of Engineering – University of Perugia

L. Barelli; G. Bidini; C. Buratti; R. Mariani

2009-01-01

286

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

PubMed

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

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

2014-09-01

287

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

NASA Technical Reports Server (NTRS)

A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.

Kobayashi, Takahisa; Simon, Donald L.

2006-01-01

288

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

PubMed

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

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

2012-11-01

289

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

SciTech Connect

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.

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

1999-07-01

290

Research on remote intelligent fault-diagnosis of CNC lathe based on bayesian networks  

Microsoft Academic Search

Considering the development of smart machine tools and Internet-based manufacturing and in order to manage the manufacturing process more efficiently, a unit of remote intelligent fault-diagnosis based on Bayesian Networks (BN) was designed and software based on internet was realized as well as the case study concerning CNC lathe. It is a compensation of machine tool's self-detection whose major job

Yuchun Li; Jianzhong Fu; Xinhua Yao; Jiao Huifeng

2010-01-01

291

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

E-print Network

ICEBO2006, Shenzhen, China Control Systems for Energy Efficiency and Comfort, Vol. V-5-6 Optimization of Fault Detection/Diagnosis Model for Thermal Storage System Using AIC Song Pan Mingjie Zheng Nobuo Nakahara Research Laboratory... Research Laboratory Nakahara Laboratory Sanko Air Conditioning Co.,LTD. Sanko Air Conditioning Co.,LTD. Environmental Sys-Tech Nagoya, Japan Nagoya, Japan Nagoya, Japan han@sanko-air.co.jp zheng@sanko-air.co.jp pl...

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

2006-01-01

292

An automated industrial fish cutting machine: Control, fault diagnosis and remote monitoring  

Microsoft Academic Search

In this paper, an automated industrial fish cutting machine, which was developed and tested in the Industrial Automation Laboratory (IAL) of the University of British Columbia, is presented including its hardware structure, control sub-system, fault diagnosis sub-system and the remote monitoring sub-system. First, the hardware of the machine including the mechanical conveyer system, pneumatic system and the hydraulic system, and

Haoxiang Lang; Ying Wang; Clarence W. de Silva

2008-01-01

293

A computer-based intelligent system for fault diagnosis of an aircraft engine  

Microsoft Academic Search

In this paper, an intelligent knowledge-based system (KBS) capable of assisting aircraft mechanics and engineers to deal with fault diagnosis of the turbo-prop aircraft engine is presented. The KBS intelligent jet engine trouble-shooting system (IJETSS) employs expert knowledge to act in a way similar to that of a human expert in an aircraft maintenance field by using if-then rule-based system.

F. Mustapha; S. M. Sapuan; N. Ismail; A. S. Mokhtar

2004-01-01

294

An expert system for fault diagnosis integrated in existing SCADA systems  

Microsoft Academic Search

The operators of Hydro-Quebec's 9 Regional Control Centres (RCC) are sometimes overloaded by the number of alarm messages produced when automatic controls operate to clear faults. To help operators, Hydro-Quebec has developed an expert system to perform a continuous analysis of alarm messages, automatically detect the application of the protection or restoration control and then produce a concise, real-time diagnosis

J.-P. Bernard; D. Durocher

1993-01-01

295

An expert system for fault diagnosis integrated in existing SCADA system  

Microsoft Academic Search

The operators of Hydro-Quebec's 9 Regional Control Centres (RCC) are sometimes overloaded by the number of alarm messages produced when automatic controls operate to clear faults. To help operators, Hydro-Quebec has developed an expert system to perform a continuous analysis of alarm messages, automatically detect the application of the protection or restoration control and then produce a concise, real-time diagnosis

J.-P. Bernard; D. Durocher

1994-01-01

296

Observer-Based Optimal Fault Detection and Diagnosis Using Conditional Probability Distributions  

Microsoft Academic Search

A new optimal fault detection and diagnosis (FDD) scheme is studied in this paper for the continuous-time stochastic dynamic systems with time delays, where the available information for the FDD is the input and the measured output probability density functions (pdf's) of the system. The square-root B-spline functional approximation technique is used to formulate the output pdf's with the dynamic

Lei Guo; Yu-Min Zhang; Hong Wang; Jian-Cheng Fang

2006-01-01

297

On-line fault diagnosis of distribution substations using hybrid cause-effect network and fuzzy rule-based method  

Microsoft Academic Search

A correct and rapid inference is required for practical use of an online fault diagnosis in power substations. This paper proposes a novel approach for on-line fault section estimations and fault types identification using the hybrid cause-effect network\\/fuzzy rule-based method in distribution substations. A cause-effect network, which is well suited to parallel processing, represents the functions of protective relays and

Wen-Hui Chen; Chih-Wen Liu; Men-Shen Tsai

2000-01-01

298

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

NASA Astrophysics Data System (ADS)

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.

Ren, Bin; Xu, Xiaoli

2010-12-01

299

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

NASA Astrophysics Data System (ADS)

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.

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

2014-05-01

300

The application study of the infrared imaging technology on the transformer fault diagnosis  

NASA Astrophysics Data System (ADS)

With the rapid development of the electric power projects and electrization of industrial corporations, more and more transformers have been used. When in operation, the real time supervision of Transformer Fault is very important to the secure operation of transformer. The Infrared imaging techniques played an important role in Fault diagnosing for transformer. The infrared imaging technique based on infrared radiation knowledge, by using this technique, the Fault modes of Power transform as well as the application of infrared imaging on Power transformer were studied. Then a series of practical engineering problems, such as the image shaping principle of infrared laser device, the utilizing of infrared laser device in power transformer fault diagnosis, the working procedure in real field operation, the acquiring of heat spectrum from fault transformer in field test, the analyzing of heat image spectrum etc, were considered and overcame. Finally, an example was presented by using the studied infrared heat imaging technique to diagnose the primary transformer at Baitaling substation. The results are closely according with the real situation.

Hou, Peiguo; Wang, Yanju; Wang, Yutian; Meng, Zong

2005-01-01

301

The research of sensor fault diagnosis based on genetic algorithm and one-against-one support vector machine  

Microsoft Academic Search

Fault diagnosis based on the wavelet packet decomposition, one-against-one support vector machine (SVM) and genetic algorithm (GA) is proposed in order to realize the real-time sensor fault diagnosis accurately. The input feature vectors of one-against-one SVM are produced by wavelet packet decomposition of the sensor output signal. GA is used to obtain optimal parameters of one-against-one SVM network model automatically,

Xu Lishuang; Cai Tao; Deng Fang; Liu Xin

2011-01-01

302

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

NASA Technical Reports Server (NTRS)

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.

Geser, Alfons; Miner, Paul S.

2004-01-01

303

Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov test  

NASA Astrophysics Data System (ADS)

Vibration behavior induced by gear shaft crack is different from that induced by gear tooth crack. Hence, a fault indicator used to detect tooth damage may not be effective for monitoring shaft condition. This paper proposes an autoregressive model-based technique to detect the occurrence and advancement of gear shaft cracks. An autoregressive model is fitted to the time synchronously averaged signal of the gear shaft in its healthy state. The order of the autoregressive model is selected using Akaike information criterion and the coefficient estimates are obtained by solving the Yule-Walker equations with the Levinson-Durbin recursion algorithm. The established autoregressive model is then used as a linear prediction filter to process the future signal. The Kolmogorov-Smirnov test is applied on line for the prediction of error signals. The calculated distance is used as a fault indicator and its capability to diagnose shaft crack effectively is demonstrated using a full lifetime gear shaft vibration data history. The other frequently used statistical measures such as kurtosis and variance are also calculated and the results are compared with the Kolmogorov-Smirnov test.

Wang, Xiyang; Makis, Viliam

2009-11-01

304

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

SciTech Connect

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.

Shih, R.F.; Lee, L.S. [National Central Univ., Chung-li (Taiwan, Province of China). Dept. of Chemical Engineering

1995-05-01

305

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

SciTech Connect

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.

Zhang Yumin; Lum, Kai-Yew [Temasek Laboratories, National University of Singapore, Singapore 117508 (Singapore); Wang Qingguo [Depa. Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)

2009-03-05

306

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

PubMed Central

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

Flores, Agustín; Morant, Francisco

2014-01-01

307

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

PubMed

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

Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

2014-01-01

308

Fault diagnosis of Tennessee Eastman process using signal geometry matching technique  

NASA Astrophysics Data System (ADS)

This article employs adaptive rank-order morphological filter to develop a pattern classification algorithm for fault diagnosis in benchmark chemical process: Tennessee Eastman process. Rank-order filtering possesses desirable properties of dealing with nonlinearities and preserving details in complex processes. Based on these benefits, the proposed algorithm achieves pattern matching through adopting one-dimensional adaptive rank-order morphological filter to process unrecognized signals under supervision of different standard signal patterns. The matching degree is characterized by the evaluation of error between standard signal and filter output signal. Initial parameter settings of the algorithm are subject to random choices and further tuned adaptively to make output approach standard signal as closely as possible. Data fusion technique is also utilized to combine diagnostic results from multiple sources. Different fault types in Tennessee Eastman process are studied to manifest the effectiveness and advantages of the proposed method. The results show that compared with many typical multivariate statistics based methods, the proposed algorithm performs better on the deterministic faults diagnosis.

Li, Han; Xiao, De-yun

2011-12-01

309

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

NASA Technical Reports Server (NTRS)

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.

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

1986-01-01

310

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

NASA Technical Reports Server (NTRS)

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.

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

311

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

SciTech Connect

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.

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

2013-01-01

312

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

NASA Astrophysics Data System (ADS)

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.

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

2013-09-01

313

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

NASA Astrophysics Data System (ADS)

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

Tadina, Matej; Boltežar, Miha

2011-07-01

314

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

PubMed Central

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

Lv, Yong; Zhu, Qinglin; Yuan, Rui

2015-01-01

315

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

PubMed

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

Lv, Yong; Zhu, Qinglin; Yuan, Rui

2015-01-01

316

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

SciTech Connect

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.

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

2012-10-01

317

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

NASA Technical Reports Server (NTRS)

When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to diagnosis. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a fault tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a fault tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be developed easily.

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

1994-01-01

318

Is intrasound vibration useful in the diagnosis of occult scaphoid fractures?  

PubMed

This study was designed to confirm the results of Finkenberg et al. (J Hand Surg 1993;18A: 4-7), who found a high sensitivity (100%) and specificity (95%) of the intrasound vibration method in diagnosing occult scaphoid fractures. These occult scaphoid fractures are not visible on x-ray films, but clinically the patients are suspected of having a scaphoid fracture. A vibratory apparatus is placed over the anatomical snuff-box and a vibration of 100 mW is emitted; a painful sensation is produced if the scaphoid is fractured. Thirty-seven consecutive patients with a clinically suspected scaphoid fracture were evaluated. In 6 patients, a scaphoid fracture was radiographically identified; in the remaining 31 patients, a 3-phase bone scan was obtained. Eleven wrists showed increased uptake over the scaphoid and were considered to have an occult scaphoid fracture. In this group, bone scintigraphy was used as the reference standard. The vibration test was painful in 1 of 6 patients with a proven scaphoid fracture and in 3 of the 11 patients with a positive bone scan. In contrast to the results of Finkenberg et al, the intrasound vibration method shows a sensitivity of 24%, a specificity of 85%, a positive predictive value of 40%, and a negative predictive value of 65%. We conclude that the accuracy of intrasound vibration is low and that it is not useful in the diagnosis of scaphoid fractures. PMID:9556260

Roolker, L; Tiel-van Buul, M M; Broekhuizen, T H

1998-03-01

319

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

NASA Astrophysics Data System (ADS)

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

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

2012-05-01

320

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

PubMed Central

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

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

2014-01-01

321

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

PubMed

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

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

2009-04-01

322

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

NASA Technical Reports Server (NTRS)

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.

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

1987-01-01

323

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

NASA Astrophysics Data System (ADS)

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.

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

2013-05-01

324

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

NASA Astrophysics Data System (ADS)

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.

Xu, Jiwei; Wu, Huijuan; Xiao, Shunkun

2014-12-01

325

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

PubMed

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

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

2014-01-01

326

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

PubMed Central

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

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

2014-01-01

327

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

NASA Technical Reports Server (NTRS)

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.

Lee, S. C.

1989-01-01

328

Combined expert system/neural networks method for process fault diagnosis  

DOEpatents

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.

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

1995-01-01

329

Combined expert system/neural networks method for process fault diagnosis  

DOEpatents

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.

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

1995-08-15

330

Intelligent multi-agent approach to fault location and diagnosis on railway 10kv automatic blocking and continuous power lines  

Microsoft Academic Search

This paper discusses the intelligent multi-agent technology, and proposes an intelligent multi-agent based accurate fault location detection and fault diagnosis system applied in 10kv automatic blocking and continuous power transmission lines along the railway. Agents are software processes capable of searching for information in the networks, interacting with pieces of equipment and performing tasks on behalf of their owner(device). Moreover,

He Zhengyou; Wang Qian; Qian Qingquan

2002-01-01

331

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

PubMed Central

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

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

2013-01-01

332

Serological Tests for Diagnosis and Staging of Hand–Arm Vibration Syndrome (HAVS)  

PubMed Central

The current gold standard for the diagnosis and staging of hand–arm vibration syndrome (HAVS) is the Stockholm workshop scale, which is subjective and relies on the patient’s recalling ability and honesty. Therefore, great potentials exist for diagnostic and staging errors. The purpose of this study is to determine if objective serum tests, such as levels of soluble thrombomodulin (sTM) and soluble intercellular adhesion molecule-1 (sICAM-1), may be used in the diagnosis and staging of HAVS. Twenty two nonsmokers were divided into a control group (n?=?11) and a vibration group (n?=?11). The control group included subjects without history of frequent vibrating tool use. The vibration group included construction workers with average vibrating tool use of 12.2 years. All were classified according to the Stockholm workshop scale (SN, sensorineural symptoms; V, vascular symptoms. SN0, no numbness; SN1, intermittent numbness; SN2, reduced sensory perception; SN3, reduced tactile discrimination; V0, no vasospasmic attacks; V1, intermittent vasospasm involving distal phalanges; V2, intermittent vasospasm extending to middle phalanges; V3, intermittent vasospasm extending to proximal phalanges; V4, skin atrophy/necrosis). All control subjects were SN0 V0. Seven out of 11 vibration subjects were SN1 V1, and 4 out of 11 were SN1 V2. A 10-cm3 sample of venous blood was collected from each subject. The sTM and sICAM-1 levels were determined by enzyme-linked immunosorbent assay. The mean plasma sTM levels were as follows: control group?=?2.93?±?0.47 ng/ml, and vibration group?=?3.61?±?0.24 ng/ml. The mean plasma sICAM-1 levels were as follows: control group?=?218.8?±?54.1 ng/ml, and vibration group?=?300.3?±?53.2 ng/ml. The sTM and sICAM-1 differences between control and vibration groups were statistically significant (p?vibration individuals (27%) who had sICAM-1 levels greater than the reference range. This was not statistically significant (p?=?0.08). When subjects were compared based on the Stockholm workshop scale, mean plasma sTM levels were SN0 V0 group?=?2.93?±?0.47 ng/ml, SN1 V1 group?=?3.59?±?0.25 ng/ml, and SN1 V2 group?=?3.65?±?0.27 ng/ml, and mean plasma sICAM-1 levels were SN0 V0?=?219?±?54.1 ng/ml, SN1 V1?=?275?±?33.5 ng/ml, and SN1 V2?=?345?±?54.6 ng/ml. The difference in sTM level among the three groups was statistically significant (p?

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

2007-01-01

333

A PARAMETER ESTIMATION METHOD FOR THE DIAGNOSIS OF SENSOR OR ACTUATOR ABRUPT FAULTS  

E-print Network

the fault isolation capability [Weber 98]. The isolation is only possible when the incidence matrix.Gentil@inpg.fr Keywords: Fault detection; fault isolation; sensor and actuator abrupt faults; parameter estimation. Abstract This paper describes a method for additive abrupt fault detection and isolation. Parameter

Paris-Sud XI, Université de

334

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

PubMed

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

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

2014-01-01

335

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

PubMed Central

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

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

2014-01-01

336

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

PubMed

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

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

2008-03-01

337

Intelligent fault diagnosis and failure management of flight control actuation systems  

NASA Technical Reports Server (NTRS)

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.

Bonnice, William F.; Baker, Walter

1988-01-01

338

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

PubMed

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

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

2014-01-01

339

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

PubMed Central

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

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

2014-01-01

340

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

NASA Astrophysics Data System (ADS)

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.

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

341

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

PubMed

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

Aydin, Ilhan; Karakose, Mehmet; Akin, Erhan

2014-03-01

342

DSP-Based Sensorless Electric Motor Fault Diagnosis Tools for Electric and Hybrid Electric Vehicle Powertrain Applications  

Microsoft Academic Search

The integrity of electric motors in work and passenger vehicles can best be maintained by frequently monitoring its condition. In this paper, a signal processing-based motor fault diagnosis scheme is presented in detail. The practicability and reliability of the proposed algorithm are tested on rotor asymmetry detection at zero speed, i.e., at startup and idle modes in the case of

Bilal Akin; Salih Baris Ozturk; Hamid A. Toliyat; Mark Rayner

2009-01-01

343

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

NASA Astrophysics Data System (ADS)

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

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

2010-11-01

344

Vibration measurement system based on position sensitive detector  

Microsoft Academic Search

Purpose – Vibration measurement is needed in many industrial production processes, such as equipment monitoring, fault diagnosis, and noise analysis and eliminating and so on. The purpose of this paper is to propose a simple vibration testing system which includes the laser, the string, position sensitive detector (PSD) and the corresponding signal processing circuit. Design\\/methodology\\/approach – PSD is an optical

Jinxue Sui; Xia Zhang; Li Yang; Zhilin Zhu; Zhang Xin

2011-01-01

345

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

PubMed

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control. PMID:11954726

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

2001-10-01

346

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

NASA Astrophysics Data System (ADS)

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

Tadina, Matej; Boltežar, Miha

2011-08-01

347

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

NASA Astrophysics Data System (ADS)

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.

Feng, Zhipeng; Chen, Xiaowang; Liang, Ming

2015-02-01

348

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

NASA Astrophysics Data System (ADS)

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

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

2015-01-01

349

Actuators Fault Diagnosis and Tolerant Control for an Unmanned Aerial Vehicle  

Microsoft Academic Search

In this paper a fault detection and isolation (FDI) method coupled with a fault tolerant control system are developed in order to deal with control surface failures for an Unmanned Aerial Vehicle (UAV). The failures considered are stuck control surfaces which occur during the aircraft manoeuvres: turn, velocity and slope variations. These faults are difficult to detect and to isolate.

Franois Bateman; Hassan Noura; Mustapha Ouladsine

2007-01-01

350

Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme  

NASA Technical Reports Server (NTRS)

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

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

2011-01-01

351

DSP-Based Sensorless Electric Motor Fault-Diagnosis Tools for Electric and Hybrid Electric Vehicle Powertrain Applications  

Microsoft Academic Search

The integrity of electric motors in work and passenger vehicles can best be maintained by frequently monitoring their condition. In this paper, a signal processing-based motor fault-diagnosis scheme in detail is presented. The practicability and reliability of the proposed algorithm are tested on rotor asymmetry detection at zero speed, i.e., at startup and idle modes in the case of a

Bilal Akin; Salih Baris Ozturk; Hamid A. Toliyat; Mark Rayner

2009-01-01

352

Using roving STARs for on-line testing and diagnosis of FPGAs in fault-tolerant applications  

Microsoft Academic Search

In this paper we present a novel integrated approach to on-line FPGA testing, diagnosis, and fault-toler- ance, to be used in high-reliability and high-availability hardware. The test process takes place in self-testing areas (STARs) of the FPGA, without disturbing the normal system operation. The entire chip is eventually tested by having STARs gradually rove across the FPGA. Our approach guar-

Miron Abramovici; Charles E. Stroud; Carter Hamilton; Sajitha Wijesuriya; Vinay Verma

1999-01-01

353

Diagnosis of helicopter gearboxes using structure-based networks  

NASA Technical Reports Server (NTRS)

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.

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

1995-01-01

354

EFFECTIVENESS AND SENSITIVITY OF VIBRATION PROCESSING TECHNIQUES FOR LOCAL FAULT DETECTION IN GEARS  

Microsoft Academic Search

This paper deals with gear condition monitoring based on vibration analysis techniques. The detection and diagnostic capability of some of the most effective techniques are discussed and compared on the basis of experimental results, concerning a gear pair affected by a fatigue crack. In particular, the results of new approaches based on time-frequency and cyclostationarity analysis are compared against those

G. Dalpiaz; A. Rivola; R. Rubini

2000-01-01

355

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

NASA Technical Reports Server (NTRS)

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.

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

2001-01-01

356

Generation of Fault Trees from Simulated Incipient Fault Case Data  

Microsoft Academic Search

Fault tree analysis is widely used in industry in fault diagnosis. The diagnosis of incipient or 'soft' faults is considerably more dif ficult than of 'hard' faults, which is the situation considered normally. A detailed fault tree model reflecting signal variations over wide range is required for diagnosing such soft faults. This paper describes the investigation of a machine learning

Michael G. Madden; Paul J. Nolan

1994-01-01

357

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

E-print Network

or motor may quickly fail. The various fault modes of a conventional PWM voltage source inverter (VSI to identify the type and location of occurring faults from inverter output voltage measurement. The neural enables the use of renewable energy sources. Two topologies of multilevel inverters for electric drive

Tolbert, Leon M.

358

Model-based fault diagnosis in electric drives using machine learning  

Microsoft Academic Search

Electric motor and power electronics-based inverter are the major components in industrial and automotive electric drives. In this paper, we present a model-based fault diagnostics system developed using a machine learning technology for detecting and locating multiple classes of faults in an electric drive. Power electronics inverter can be considered to be the weakest link in such a system from

Yi Lu Murphey; M. Abul Masrur; ZhiHang Chen; Baifang Zhang

2006-01-01

359

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

NASA Technical Reports Server (NTRS)

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.

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

1988-01-01

360

Condition monitoring for electrical failures in induction machine using neural network modelling of vibration signal  

Microsoft Academic Search

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Many of these faulty situations in three phase induction motors have an electrical reason. Vibration signal analysis is found to be sensitive to electrical faults. However, the conventional methods require detailed information on motor design characteristics, and cannot be applied effectively for vibration diagnosis because

Hua Su; Kil To Chong

2005-01-01

361

A subspace fitting method based on finite elements for fast identification of damages in vibrating mechanical  

E-print Network

for diagnosis of structural faults in a mechanical systems. 1 Introduction Detecting the damages in vibrating is one example of damages occurring in a mechanical system during its service life. Such phenomena canA subspace fitting method based on finite elements for fast identification of damages in vibrating

Paris-Sud XI, Université de

362

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

NASA Technical Reports Server (NTRS)

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).

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

2012-01-01

363

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

E-print Network

strategy is precisely presented for digital signal processor (DSP) system application. 4) Most of the diagnosis algorithms in the literature are developed assuming specific detection conditions which makes application difficult for universal diagnosis...

Choi, Seungdeog

2012-02-14

364

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

NASA Technical Reports Server (NTRS)

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.

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

2003-01-01

365

Fault diagnosis of marine diesel engine based on blind source separation  

Microsoft Academic Search

This article makes use of marine diesel engine local vibration signals. These signals have properties of non-stationary, non-Gaussian and low signal-to-noise. These unknown sources about diesel vibration signals can be estimated and recurrent according to blind source separation (bss) algorithm and combining other time domain, frequency domain analysis method. The independent signal source can also be identified through the linear

Yong Chang; Yihuai Hu

2010-01-01

366

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

SciTech Connect

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.

Shih, R.F.; Lee, L.S. [National Central Univ., Chung-li (Taiwan, Province of China). Dept. of Chemical Engineering

1995-05-01

367

Airdata sensor based position estimation and fault diagnosis in aerial refueling  

NASA Astrophysics Data System (ADS)

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

Sevil, Hakki Erhan

368

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

E-print Network

2. Ftu!It Detection Techniques C. Literature Review D. Rcscarch Objcctivc and Proposed Approach 1. Objectives 2. Proposed Approach E. Contribution . F. Organization of the Thesis DESCRIPTION OF THE MODEL-BASED FAULT DIAG- NOSIS SYSTEM A...-LINE IMPLEMENTATION OF THE FAULT DIAG!UO- SIS SYSTENI A. Ini!oduci, ior! . B. Description of the IVIotor Current, Predictor . C Description of the Speed I'ilter . D. Dcsc! ipiion ol ihc Neural igclwo! k F!anm!vork E. Description of the Prc-Pron ssing u!d Post...

Alladi, Vijaya Mallikarjun

2002-01-01

369

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

PubMed Central

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

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

2013-01-01

370

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

Microsoft Academic Search

This paper presents an automatic strategy that can be used in a building energy management and control system to detect, diagnose, and evaluate soft sensor faults in building air-conditioning systems. The strategy is based on fundamental conservation (mass and energy conservation) relations and accommodates changes of plant performance and working conditions. The existence and magnitude of non-abrupt biases in chilled

S. Wang; J. B. Wang

2000-01-01

371

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

Microsoft Academic Search

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

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

2011-01-01

372

Research on Fault Detection and Diagnosis of Scrolling Chiller with ANN  

E-print Network

, charge non-condense gas, shift temperature of cooling water and alter outside cold load. A set of characteristic parameters are defined in order to differentiate these faults and clarify the reasons. Finally, an FDD tool is programmed based on ANN...

Zhou, Y.; Zheng, J.; Liu, Z.; Yang, C.; Peng, P.

2006-01-01

373

Neural Network-based Actuator Fault Diagnosis for Attitude Control Subsystem of an Unmanned Space Vehicle  

Microsoft Academic Search

The main objective of this paper is to develop a neural network-based fault detection and isolation scheme (FDI) for the attitude control subsystem (ACS) of a satellite. Towards this end, two neural network architectures are considered. First, a dynamic neural network residual generator is constructed based on the dynamic multilayer perceptron (DMLP) network to perform the detection task. A generalized

Iz Al-dein Al-zyoud; Khashayar Khorasani

2006-01-01

374

RELATIONS OF TIMED EVENT GRAPHS AND TIMED AUTOMATA IN FAULT DIAGNOSIS  

Microsoft Academic Search

This paper investigates the relations between timed event graphs and timed automata that describe discrete-event systems subject to faults. An algorithm is presented for transforming a timed event graph into an equivalent timed automaton. The relations among the models of the faulty systems provides the basis for comparing diagnostic methods that have been developed in the past for these two

G. Schullerus; P. Supavatanakul; V. Krebs; J. Lunze

375

Fault Diagnosis of Nonlinear Analog Circuits Using Neural Networks with Wavelet and Fourier Transforms as Preprocessors  

Microsoft Academic Search

A neural-network based analog fault diagnostic system is developed for nonlinear circuits. This system uses wavelet and Fourier transforms, normalization and principal component analysis as preprocessors to extract an optimal number of features from the circuit node voltages. These features are then used to train a neural network to diagnose soft and hard faulty components in nonlinear circuits. Our neural

Farzan Aminian; Mehran Aminian

2001-01-01

376

Fault diagnosis of industrial boiler based on competitive agglomeration and fuzzy association rules  

Microsoft Academic Search

Applying datamining algorithm to the association analyzes between the measurable parameters and faults in the industrial boiler control system. The works we have done generally as follows. According to the distribution of the parameters that can be measured, using competitive agglomeration clustering algorithm to partition the fuzzy interval of each attribute; based on the principle of association rules, an algorithm

Zhao Hui; Jiang Bi-bo; Zhao Zhuo-qun

2010-01-01

377

Multiple sensor fault diagnosis for non-linear and dynamic system by evolving approach  

Microsoft Academic Search

Reliability of sensor measurement is vital to assure the performance of complex and nonlinear industrial operation. In this paper, the problem of designing and development of a data-driven multiple sensor fault detection and isolation (MSFDI) algorithm for nonlinear processes is investigated. The proposed scheme is based on an evolving multi-Takagi Sugeno framework in which each sensor output is estimated using

Mohamed El-koujok; Mohieddine Benammar; Nader Meskin; Mohamed Al-Naemi; Reza Langari

2012-01-01

378

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

NASA Astrophysics Data System (ADS)

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

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

2011-07-01

379

Development of automatic discriminating approach of process condition for FMC condition monitoring and fault diagnosis  

Microsoft Academic Search

Based on the features of flexible machining devices and processes, and the requirements of monitoring and diagnosis, a multi-parameter describing and discriminating model of machining operating mode and process condition has been presented to satisfy the real-time monitoring and diagnosis under the actual flexible machining environments. It mainly contains three parts: the analysis of feature parameters and the normalized processing,

Jin Qiu; Xisen Wen; Bingyang Tang

1995-01-01

380

Optimal Sensor Allocation for Fault Detection and Isolation  

NASA Technical Reports Server (NTRS)

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.

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

2004-01-01

381

Fault diagnosis in gas turbines using a model-based technique  

NASA Astrophysics Data System (ADS)

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

Merrington, G. L.

1994-04-01

382

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

NASA Technical Reports Server (NTRS)

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.

Smyth, P.; Mellstrom, J.

1990-01-01

383

A Quantum-inspired Evolutionary Algorithm with a competitive variation operator for Multiple-Fault Diagnosis  

Microsoft Academic Search

A heuristic search algorithm, the Quantum-inspired Competitive Evolutionary Algorithm (QuCEA), based on both quantum and evolutionary computing, is proposed. The individuals of a population, coded as qubit strings, evolve by means of an original variation operator inspired by competitive learning. The proposed operator is application independent and intuitively controllable by a single real parameter. QuCEA has been applied to Multiple-Fault

P. Arpaia; D. Maisto; C. Manna

2011-01-01

384

Analytic redundancy for on-line fault diagnosis in a nuclear reactor  

Microsoft Academic Search

A computer-aided diagnostic technique has been applied to on-line signal validation in an operating nuclear reactor. To avoid installation of additional redundant sensors for the sole purpose of fault isolation, a real-time model of nuclear instrumentation and the thermal-hydraulic process in the primary coolant loop was developed and experimentally validated. The model provides analytically redundant information sufficient for isolation of

Asok Ray; Mukund Desai; John Deyst; Robert Geiger

1983-01-01

385

Fault table generation using Graphics Processing Units  

Microsoft Academic Search

In this paper, we explore the implementation of fault table generation on a Graphics Processing Unit (GPU). A fault table is essential for fault diagnosis and fault detection in VLSI testing and debug. Generating a fault table requires extensive fault simulation, with no fault dropping, and is extremely expensive from a computational standpoint. Fault simulation is inherently parallelizable, and the

Kanupriya Gulati; Sunil P Khatri

2009-01-01

386

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

NASA Astrophysics Data System (ADS)

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.

Jegadeeshwaran, R.; Sugumaran, V.

2015-02-01

387

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)

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.

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

2013-07-01

388

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace 1 1 Supported by the National Natural Science Foundation of China (No.60504033)  

Microsoft Academic Search

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension

Xu ZHAO; Weiwu YAN; Huihe SHAO

2006-01-01

389

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

NASA Astrophysics Data System (ADS)

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

Tomaszewski, Franciszek; Szyma?ski, Grzegorz

2012-03-01

390

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

NASA Technical Reports Server (NTRS)

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.

Ali, Moonis

1990-01-01

391

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

NASA Astrophysics Data System (ADS)

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.

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

2013-12-01

392

Diagnosis of power plant faults using qualitative models and heuristic rules  

Microsoft Academic Search

This paper presents results obtained in an AI research effort in the industrial field of Nuclear Power Plants (NPP): malfunction diagnosis of the Emergency Feedwater System (EFWS) of a NPP. An expert system was developed which utilizes qualitative techniques for modeling the system and heuristic rules for generating causal explanations of an observed malfunction. The operation of the system, the

Irina Obreja

1990-01-01

393

Simultaneous Sensor and Actuator Fault Reconstruction and Diagnosis using Generalized Sliding Mode Observers  

E-print Network

and isolation (FDI) is of significant technical importance. Model-based FDI schemes employ measured variables, diagnosis and isolation is performed, and decisions and counteractions are then taken. Model-based FDI). In particular, observer- based techniques for FDI have drawn significant attention (see for example [2], [3], [4

Marquez, Horacio J.

394

Recent developments of induction motor drives fault diagnosis using AI techniques  

Microsoft Academic Search

This paper presents a review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI). It covers the application of expert systems, artificial neural networks (ANNs), and fuzzy logic systems that can be integrated into each other and also with more traditional techniques. The application of genetic algorithms is considered as well.

Fiorenzo Filippetti; Giovanni Franceschini; Carla Tassoni; Peter Vas

2000-01-01

395

Automated Fault Diagnosis in Embedded Systems # Peter Zoeteweij Jurryt Pietersma Rui Abreu Alexander Feldman  

E-print Network

results, and report on the application on industrial test cases. In addition, we propose to combine for advertising or promotional purposes or for creating new collective works for resale or redistribution on the diagnosis problem, which allows us to compare and relate MBD and SFL. . We report recent successes

Zoeteweij, Peter

396

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

Microsoft Academic Search

Human space travel is inherently dangerous. Hazardous conditions will exist. Real time health monitoring of critical subsystems is essential for providing a safe abort timeline in the event of a catastrophic subsystem failure. In this paper, we discuss a practical and cost effective process for developing critical subsystem failure detection, diagnosis and response (FDDR). We also present the results of

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

2007-01-01

397

BAYESIAN NETWORKS AND MUTUAL INFORMATION FOR FAULT DIAGNOSIS OF INDUSTRIAL SYSTEMS  

Microsoft Academic Search

The purpose of this article is to present and evaluate the performance of a new procedure for industrial process diagnosis. This method is based on the use of a bayesian network as a classier. But, as the classication performances are not very ecien t in the space described by all variables of the process, an identication of important variables is

Sylvain Verron; Teodor Tiplica; Abdessamad Kobi

398

Fault Diagnosis with Bayesian Networks: Application to the Tennessee Eastman Process  

Microsoft Academic Search

The purpose of this article is to present and evaluate the performance of a new procedure for industrial process diagnosis. This method is based on the use of a Bayesian network as a classifier. But, as the classification performances are not very efficient in the space described by all variables of the process, an identification of important variables is made.

Sylvain VERRON; Teodor TIPLICA; Abdessamad KOBI

2006-01-01

399

A fault detection and diagnosis module for oil production plants in offshore platforms  

Microsoft Academic Search

This paper describes an expert system for process supervision and diagnosis of an offshore oil product plant. The expert system contains main modules with detection, diagnostic and advisory functions. The implementation issues of the expert system using the Gensym G2 software environment are discussed. The system was developed on board the Petrobras XXIV platform in the Campos Basin, Rio de

E. Kaszkurewicz; A. Bhaya; N. F. F. Ebecken

1997-01-01

400

Variance component analysis based fault diagnosis of multi-layer overlay lithography processes  

Microsoft Academic Search

The overlay lithography process is one of the most important steps in semiconductor manufacturing. This work attempts to solve a challenging problem in this technique, namely error source identification and diagnosis for multistage overlay processes. In this paper, a multistage state space model for the misalignment errors of the lithography process is developed and a general mixed linear input–output model

Jie Yu; S. Joe Qin

2009-01-01

401

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

NASA Astrophysics Data System (ADS)

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.

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

2013-10-01

402

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

PubMed

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

Narayan, A P; Ramirez, W F

1998-01-01

403

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

NASA Technical Reports Server (NTRS)

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.

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

404

Bolt loosening analysis and diagnosis by non-contact laser excitation vibration tests  

NASA Astrophysics Data System (ADS)

In this paper, a vibration testing and health monitoring system based on an impulse response excited by laser ablation is proposed to detect bolted joint loosening. A high power Nd: YAG pulse laser is used to generate an ideal impulse on a structural surface which offers the potential to measure high frequency vibration responses on the structure. A health monitoring apparatus is developed with this vibration testing system and a damage detecting algorithm. The joint loosening can be estimated by detecting fluctuations of the high frequency response with the health monitoring system. Additionally, a finite element model of bolted joints is proposed by using three-dimensional elements with a pretension force applied and with contact between components taken into account to support the bolt loosening detection method. Frequency responses obtained from the finite element analysis and the experiments using the laser excitation are in good agreement. The bolt loosening can be detected and identified by introducing a damage index by statistical evaluations of the frequency response data using the Recognition-Taguchi method. The effectiveness of the present approach is verified by simulations and experimental results, which are able to detect and identify loose bolt positions in a six-bolt joint cantilever.

Huda, Feblil; Kajiwara, Itsuro; Hosoya, Naoki; Kawamura, Shozo

2013-11-01

405

The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis  

NASA Astrophysics Data System (ADS)

Spectral kurtosis (SK) represents a valuable tool for extracting transients buried in noise, which makes it very powerful for the diagnostics of rolling element bearings. However, a high value of SK requires that the individual transients are separated, which in turn means that if their repetition rate is high their damping must be sufficiently high that each dies away before the appearance of the next. This paper presents an algorithm for enhancing the surveillance capability of SK by using the minimum entropy deconvolution (MED) technique. The MED technique effectively deconvolves the effect of the transmission path and clarifies the impulses, even where they are not separated in the original signal. The paper illustrates these issues by analysing signals taken from a high-speed test rig, which contained a bearing with a spalled inner race. The results show that the use of the MED technique dramatically sharpens the pulses originating from the impacts of the balls with the spall and increases the kurtosis values to a level that reflects the severity of the fault. Moreover, when the algorithm was tested on signals taken from a gearbox for a bearing with a spalled outer race, it shows that each of the impulses originating from the impacts is made up of two parts (corresponding to entry into and exit from the spall). This agrees well with the literature but is often difficult to observe without the use of the MED technique. The use of the MED along with SK analysis also greatly improves the results of envelope analysis for making a complete diagnosis of the fault and trending its progression.

Sawalhi, N.; Randall, R. B.; Endo, H.

2007-08-01

406

A Quantum Annealing Approach for Fault Detection and Diagnosis of Graph-Based Systems  

E-print Network

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 $\\rightarrow$ QUBO $\\rightarrow$ 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.

Alejandro Perdomo-Ortiz; Joseph Fluegemann; Sriram Narasimhan; Rupak Biswas; Vadim N. Smelyanskiy

2014-10-02

407

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

E-print Network

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

Luo, Dong, 1966-

2001-01-01

408

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

NASA Astrophysics Data System (ADS)

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.

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

2015-02-01

409

Use of laser fluorescence in dental caries diagnosis: a fluorescence x biomolecular vibrational spectroscopic comparative study.  

PubMed

The aim of this work was to verify the existence of correlation between Raman spectroscopy readings of phosphate apatite (~960 cm-1), fluoridated apatite (~575 cm-1) and organic matrix (~1450 cm-1) levels and Diagnodent® readings at different stages of dental caries in extracted human teeth. The mean peak value of fluorescence in the carious area was recorded and teeth were divided in enamel caries, dentin caries and sound dental structure. After fluorescence readings, Raman spectroscopy was carried out on the same sites. The results showed significant difference (ANOVA, p<0.05) between the fluorescence readings for enamel (16.4 ± 2.3) and dentin (57.6 ± 23.7) on carious teeth. Raman peaks of enamel and dentin revealed that ~575 and ~960 cm-1 peaks were more intense in enamel caries. There was significant negative correlation (p<0.05) between the ~575 and ~960 cm-1 peaks and dentin caries. It may be concluded that the higher the fluorescence detected by Diagnodent the lower the peaks of phosphate apatite and fluoridated apatite. As the early diagnosis of caries is directly related to the identification of changes in the inorganic tooth components, Raman spectroscopy was more sensitive to variations of these components than Diagnodent. PMID:23657415

Carvalho, Fabíola Bastos de; Barbosa, Artur Felipe Santos; Zanin, Fátima Antonia Aparecida; Brugnera Júnior, Aldo; Silveira Júnior, Landulfo; Pinheiro, Antonio Luiz Barbosa

2013-01-01

410

Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park's vector approach  

Microsoft Academic Search

This paper describes the use of the extended Park's vector approach (EPVA) for diagnosing the occurrence of stator winding faults in operating three-phase synchronous and asynchronous motors. The major theoretical principles related with the EPVA are presented and it is shown how stator winding faults can be effectively diagnosed by the use of this noninvasive approach. Experimental results, obtained in

Sérgio M. A. Cruz; A. J. Marques Cardoso

2001-01-01

411

Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park's vector approach  

Microsoft Academic Search

This paper describes the use of the extended Park's vector approach (EPVA) for diagnosing the occurrence of stator winding faults in operating three-phase synchronous and asynchronous motors. The major theoretical principles related with the EPVA are presented and it is shown how stator winding faults can be effectively diagnosed by the use of this noninvasive approach. Experimental results, obtained in

S. M. A. Cruz; A. J. Marques Cardoso

2000-01-01

412

Diagnosis of rotor faults in brushless DC (BLDC) motors operating under non-stationary conditions using windowed Fourier ridges  

Microsoft Academic Search

There are several applications where the motor is operating in continuous non-stationary operating conditions. Actuators in the aerospace and transportation industries are examples of this kind of operation. Diagnostics of faults in such applications is, however, challenging. A novel method using windowed Fourier ridges is proposed in this paper for the detection of rotor faults in BLDC motors operating under

Satish Rajagopalan; Thomas G. Habetler; Ronald G. Harley; José M. Aller; José A. Restrepo

2005-01-01

413

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

PubMed

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

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

2014-09-01

414

Nonlinear and parametric coupled vibrations of the rotor-shaft system as fault identification symptom using stochastic methods  

Microsoft Academic Search

In the paper several stochastic methods for detection and identification of cracks in the shafts of rotating machines are\\u000a proposed. All these methods are based on the Monte Carlo simulations of the rotor-shaft lateral-torsional-longitudinal vibrations\\u000a mutually coupled by transverse cracks of randomly selected depths and locations on the shaft. For this purpose there is applied\\u000a a structural hybrid model of

T. Szolc; P. Tauzowski; J. Knabel; R. Stocki

2009-01-01

415

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

NASA Astrophysics Data System (ADS)

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.

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

2011-07-01

416

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

E-print Network

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

Weatherwax, Scott Eric

2009-05-15

417

Diagnosable Systems for Intermittent Faults  

Microsoft Academic Search

Diagnosable systems composed of interconnected units which are capable of testing each other have been studied primarily from the point of view of permanent faults. Along such lines, designs have been proposed, and necessary and sufficient conditions for the diagnosis of such faults have been established. In this paper, we study the intermittent fault diagnosis capabilities of such systems. Necessary

Sivanarayana Mallela; Gerald M. Masson

1978-01-01

418

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

NASA Astrophysics Data System (ADS)

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.

Cai, Gaigai; Chen, Xuefeng; He, Zhengjia

2013-12-01

419

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

PubMed

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

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

2012-01-01

420

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

PubMed

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

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

2013-01-01

421

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

PubMed Central

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

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

2013-01-01

422

Flight elements: Fault detection and fault management  

NASA Technical Reports Server (NTRS)

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.

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

1990-01-01

423

Voltage source inverter fault diagnosis in variable speed AC drives, by the average current Park's vector approach  

Microsoft Academic Search

This paper introduces a new approach, based on the average motor supply current Park's vector monitoring, for diagnosing voltage source inverter faults in variable speed AC drives. Both simulation and laboratory tests results demonstrate the effectiveness of the proposed on-line diagnostic technique

A. M. S. Mendes; A. J. Marques Cardoso

1999-01-01

424

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 24, NO. 2, JUNE 2009 431 Fault Detection and Diagnosis in a Set  

E-print Network

W inverter-fed asynchronous motor, in order to detect supply and motor faults. In this application applications, the asynchronous motor supply is done by converters. Thus, the currents are affected]­[3]. These motors present numerous advantages due to their ro- bustness and their power­weight ratio. Thus

Boyer, Edmond

425

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

NASA Astrophysics Data System (ADS)

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.

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

2012-05-01

426

NONLINEAR MODELS FOR RESIDUAL GENERATION IN FAULT DETECTION AND DIAGNOSIS SYSTEMS APPLIED TO THE PENDUBOT DYNAMIC SYSTEM  

Microsoft Academic Search

This paper presents a comparison of three nonlinear models used for residual generation. The residual generation is part of a simple fault detection and diagnostics scheme applied to the Pendubot dynamic system operating in closed loop. The compared models are: Hammerstein, neural NARMAX, and Takagi- Sugeno Fuzzy models.

Aldo Levrini; Aldo Cipriano

427

Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach  

Microsoft Academic Search

This paper presents a unified methodology for detecting, isolating and accommodating faults in a class of nonlinear dynamic systems. A fault diagnosis component is used for fault detection and isolation. On the basis of the fault information obtained by the fault-diagnosis procedure, a fault-tolerant control component is designed to compensate for the effects of faults. In the presence of a

Xiaodong Zhang; Thomas Parisini; Marios M. Polycarpou

2004-01-01

428

Dynamic Fault Test and Diagnosis in Digital Systems Using Multiple Clock Schemes and Multi-VDD Test  

Microsoft Academic Search

Performance test is a powerful technique to identify difficult to detect defects. Recently, the authors have shown that multi-VDD test schemes may be used in a BIST environment to simulate multi-clock test. Using circuit and logic-level fault simulation it has been demonstrated that the effect of lowering VDD on the propagation delay time, while keeping invariant the observation pace at

M. Rodríguez-irago; Juan J. Rodríguez-andina; Fabian Vargas; Marcelino B. Santos; Isabel C. Teixeira; João Paulo Teixeira

2005-01-01

429

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

NASA Technical Reports Server (NTRS)

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.

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

1992-01-01

430

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

PubMed

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

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

2015-01-01

431

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

NASA Astrophysics Data System (ADS)

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.

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

2015-01-01

432

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

NASA Astrophysics Data System (ADS)

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.

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

2015-03-01

433

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

NASA Astrophysics Data System (ADS)

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

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

2015-01-01

434

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

NASA Astrophysics Data System (ADS)

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/um 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.

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

2015-02-01

435

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

PubMed Central

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

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

2013-01-01

436

Intelligent diagnosis of mechanical-pneumatic systems using miniaturized sensors  

NASA Astrophysics Data System (ADS)

Fault detection and diagnosis (FDD) is applied to mechanical-pneumatic systems to perform intelligent diagnosis of various faults in the system by utilizing the sensory information commonly found in typical systems, such as pressures and flow rates. In this paper, we present research results on intelligent FDD and characterization of MEMS flow sensor. Vectorized maps are created and calibrated for the purpose of intelligent FDD. In addition, maps of N-manifold can be used for redundancy in diagnosis to improve the accuracy and reliability of the methodology. Such redundant vectorized maps provide for explanation of physical significance of the behavior of the system and the formation or detection of faults. As a result, both physical-based and signal-based intelligent fault detection and diagnosis techniques and methodology can be applied for various types of applications. Experimental results suggest that intuitive choices of parameters and features, based on the understanding of physics of the mechanical-pneumatic system, can be applied with success to intelligent detection and diagnosis of faults. Furthermore, with miniaturization, sensors can be readily made and integrated for intelligent diagnosis. Characterization and modeling of such innovative sensor designs are presented. Using new smart multi-function, telemetric, and integrated sensors as "intelligent nodes" in systems will provide necessary sensory information (e.g., pressure, flow, and temperature) for the next-generation diagnosis. The characterization and study of MEMS sensor include: correlation of flow and deflection of sensory element, analysis and modeling, vibration characteristics, fatigue tests, backflow characterization,... etc. Specifically, the results of fatigue tests provide information and feedback for the design and fabrication of the MEMS sensors; more importantly, long fatigue life is essential for the flow sensors to sustain as a transducer. Results of the findings are presented.

Kao, Imin; Li, Xiaolin; Kumar, Abhinav; Boehm, Christian; Binder, Josef

2006-03-01

437

Normalized complex Teager energy operator demodulation method and its application to fault diagnosis in a rubbing rotor system  

NASA Astrophysics Data System (ADS)

As a newer signal demodulation method composed of an empirical AM-FM decomposition and a Hilbert transform, the Normalized Hilbert transform (NHT) method has been proved effective to overcome several drawbacks of the direct Hilbert transform (HT) demodulation method to a certain extent, including limitation of Bedrosian theorem, negative frequency values and inevitable boundary fluctuations of the demodulation results. However, studies in this paper will show that the FM signal resulting from the empirical AM-FM decomposition may contain riding waves and its local extrema values may also deviate much from unity value in some cases. These two problems involved in the empirical AM-FM decomposition are not beneficial to extracting a desirable instantaneous frequency. Moreover, since the Hilbert transform is still used in the NHT method to extract instantaneous frequency from the FM signal, the boundary fluctuations will inevitably occur. Aiming at the drawbacks of NHT method, a new signal demodulation method named the normalized complex Teager energy operator (NCTEO) is proposed in this paper, which consists of an improved empirical AM-FM decomposition and a new instantaneous frequency estimate based on complex Teager energy operator (CTEO). In this demodulation method, the improved empirical AM-FM decomposition is firstly applied to a monocomponent signal for instantaneous amplitude extraction, achieving the separation of the envelope signal (AM part) and the carrier (FM part), then the proposed CTEO method is employed to extract the instantaneous frequency from the resulting FM signal. The results of comparative analysis on simulated signals and experimental rotor data demonstrate that NCTEO method can effectively extract the time-frequency information, and provide a reliable diagnostic basis for the rotor rubbing fault; moreover, comparisons with some other existing demodulation methods, such as HT, NHT and TEO methods, show the promising applications of NCTEO method.

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

2015-01-01

438

Fault Motion  

NSDL National Science Digital Library

This collection of animations provides elementary examples of fault motion intended for simple demonstrations. Examples include dip-slip faults (normal and reverse), strike-slip faults, and oblique-slip faults.

439

Sensor Fault Detection and Isolation System  

E-print Network

The purpose of this research is to develop a Fault Detection and Isolation (FDI) system which is capable to diagnosis multiple sensor faults in nonlinear cases. In order to lead this study closer to real world applications in oil industries...

Yang, Cheng-Ken

2014-08-01

440

Fault management for data systems  

NASA Technical Reports Server (NTRS)

Issues related to automating the process of fault management (fault diagnosis and response) for data management systems are considered. Substantial benefits are to be gained by successful automation of this process, particularly for large, complex systems. The use of graph-based models to develop a computer assisted fault management system is advocated. The general problem is described and the motivation behind choosing graph-based models over other approaches for developing fault diagnosis computer programs is outlined. Some existing work in the area of graph-based fault diagnosis is reviewed, and a new fault management method which was developed from existing methods is offered. Our method is applied to an automatic telescope system intended as a prototype for future lunar telescope programs. Finally, an application of our method to general data management systems is described.

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

1993-01-01

441

Page 1 of 8 Generation of Fault Trees from Simulated  

E-print Network

tree analysis is widely used in industry in fault diagnosis. The diagnosis of incipient or `soft of the approach. 1 Introduction Fault tree analysis (FTA) and fault tree synthesis (FTS) evolved primarily within for over 30 years. Fault tree analysis can be valuable as a design tool; using it to identify and eliminate

Madden, Michael

442

On time diagnosis of discrete event systems  

Microsoft Academic Search

A formulation and solution methodology for on-time fault diagnosis in discrete event systems is presented. This formulation and solution methodology captures the timeliness aspect of fault diagnosis and is therefore different from all other approaches to fault diagnosis in discrete event systems which are asymptotic in nature. A monitor observes a projection of the events that occur in the system.

Aditya Mahajan; Demosthenis Teneketzis

2008-01-01

443

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

NASA Technical Reports Server (NTRS)

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

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

2009-01-01

444

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

E-print Network

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

Firenze, Università degli Studi di

445

Results of independent medical interview and examination in the diagnosis and assessment of hand-arm vibration syndrome.  

PubMed

In the UK the use of the Stockholm Classification has been recommended by the Health and Safety Executive and by the Working Party of the Faculty of Occupational Medicine. The Stockholm Workshop 1994 did not recommend any changes to the existing classification but considered the variety of screening and diagnostic tests suitable for the staging of HAVS. Thirty one males claiming to be suffering from HAVS were interviewed and examined by each of the authors independently. The examination of each patient included detailed occupational and medical histories, standard physical examination with the additional tests of the rewarm time and aesthesiometry. Thermal neutral zone test (TNZ), vibrotactile thresholds and grip strength were also performed by McGeoch. All patients were classified by the Taylor/Pelmear and Stockholm Classifications. Both authors agreed that all the patients were suffering from HAVS. Agreement to within one stage was high for both the vascular and neurological elements. The additional neurological tests used by McGeoch appeared to result the raising of the neurological staging. The results indicate that independent interview plus objective tests performed by experienced physicians allow for reliable diagnosis and staging of claimants. Standardisation of tests is urgently required. PMID:9150985

McGeoch, K L; Welsh, C L

1995-01-01

446

Vibration signatures, wavelets and principal components analysis in diesel engine  

E-print Network

on vibration data acquired during normal operation (N) and after inducing four commonly occurring faults components. It could be used to detect incipient faults in the engine. Several commonly occurring faults were artificial neural nets based fault dia­ gnostic systems. It was found that the best results were obtained

Sharkey, Amanda

447

Improving Multiple Fault Diagnosability using Possible Conflicts  

NASA Technical Reports Server (NTRS)

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

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

2012-01-01

448

Faulted Barn  

USGS Multimedia Gallery

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

449

Fault Separation  

NSDL National Science Digital Library

Students use gestures to explore the relationship between fault slip direction and fault separation by varying the geometry of faulted layers, slip direction, and the perspective from which these are viewed.

Carol Ormand

450

Fault diagnosis using substation computer  

Microsoft Academic Search

A number of substation integrated control and protection systems (ICPS) are being developed around the world, where the protective relaying, control, and monitoring functions of a substation are implemented using microprocessors. In this design, conventional relays and control devices are replaced by clusters of microprocessors, interconnected by multiplexed digital communication channels using fibre optic, twisted wire pairs or coaxial cables.

B. Jeyasurya; S. S. Venkata; S. V. Vadari; J. Postforoosh

1990-01-01

451

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

PubMed

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

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

2015-01-01

452

Fault diagnosability of arrangement graphs Shuming Zhou a,b,  

E-print Network

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

Xu, Jun-Ming

453

Neural network based fault detection in robotic manipulators  

Microsoft Academic Search

Fault detection, diagnosis, and accommodation play a key role in the operation of autonomous and intelligent robotic systems. System faults, which typically result in changes in critical system parameters or even system dynamics, may lead to degradation in performance and unsafe operating: conditions. This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators. A learning architecture, with neural

Arun T. Vemuri; Marios M. Polycarpou; Sotiris A. Diakourtis

1998-01-01

454

Isolation and identification of dry bearing faults in induction machine using wavelet transform  

Microsoft Academic Search

Any vibration signal obtained from electromechanical systems contains a level of random changes. These random changes in the measured signal may be due to the random vibrations that can be related to the health of the machine for some faults such as dry bearing fault or bearing ageing. The presence of dry bearing fault, which is caused by the lack

G. K. Singh; Sa’ad Ahmed Saleh Al Kazzaz

2009-01-01

455

Identification of Multiple faults in an Inertial Measurement Unit  

E-print Network

with Matlab/Simulink. Keywords: Fault diagnosis, fault detection, fault isolation, fault identification accelerometers, and detects changes in rotational attributes such as pitch, roll and yaw angles using one or more, including Unmanned Aerial Vehicles (UAVs), among many others, and space- crafts, including shuttles

Paris-Sud XI, Université de

456

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

E-print Network

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

Li, H.; Braun, J.

2006-01-01

457

Transformer oil diagnosis using fuzzy logic and neural networks  

Microsoft Academic Search

Dissolved-gas analysis (DGA) is widely used for detection and diagnosis of incipient faults in large oil-filled transformers. Many factors contribute to extreme “noisiness” in the data and make early fault detection and diagnosis difficult. This paper shows how fuzzy logic and neural networks are being used to automate standard DGA methods and improve their usefulness for power transformer fault diagnosis.

J. J. Dukarm

1993-01-01

458

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

NASA Technical Reports Server (NTRS)

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

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

1996-01-01

459