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1

A practical approach to electromotor fault diagnosis of Imam Khomaynei silo by vibration condition monitoring  

Microsoft Academic Search

Practical experience has shown that vibration technique in a machine condition monitoring program provides useful reliable information, bringing significant cost benefits to industry. The objective of this research was to investigate the correlation between vibration analysis and electromotor fault diagnosis. This was achieved by vibration analysis of an electromotor in Imam Khomaynei silo. The vibration analysis was initially run under

Hojat Ahmadi; Kaveh Mollazade

2

Vibration Fault Detection and Diagnosis Method of Power System Generator Based on Wavelet Fractal Network  

Microsoft Academic Search

A novel fault diagnosis method for turbo-generator set based on fractal exponent theory and wavelet network is presented. When faults occur, they usually produce nonstationary vibration signals. The wavelet transform is used to localizes the characteristics of vibration signal in the time frequency domains and in a view of the inter relationship of wavelet transform between fractal theory, the whole

Kang Shanlin; Liang Baoshe; Fan Feng; Shen Songhua

2007-01-01

3

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

E-print Network

A Study of Rolling-Element Bearing Fault Diagnosis Using Motor's Vibration and Current Signatures Grundfos A/S, Poul Due Jensens Vej 7, 8850 Bjerringbro, Denmark Abstract: This paper investigates the fault. The average of a set of Short- Time FFT (STFFT) is used for the current spectrum analysis. A set of fault

Yang, Zhenyu

4

Torsional-Vibration Assessment and Gear-Fault Diagnosis in Railway Traction System  

Microsoft Academic Search

The diagnosis of mechanical faults in railway trac- tion systems (RTSs) has a significant importance on both safety and reliability, which can avoid train crashes. This paper deals with torsional-vibration assessment and gear-fault diagnosis in the mechanical transmission of a high-speed RTS by a fully non- invasive technique. Previous studies on a simple gearbox-based electromechanical system have shown that the

Humberto Henao; Shahin Hedayati Kia; Gérard-André Capolino

2011-01-01

5

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

6

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

PubMed Central

Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods. PMID:24379045

He, Qingbo; Wang, Xiangxiang; Zhou, Qiang

2014-01-01

7

Turbine Rotor Vibration Faults Diagnosis based on Wavelet Packet Analysis and the Largest Lyapunov Exponent  

Microsoft Academic Search

According to the four typical fault signals of turbine rotor vibration, including rubbing, loosening, misalignment and mass unbalance which are collected from the Bently experiment set, the method which combines wavelet packet and the largest Lyapunov exponent is adopted to diagnose the faults. First, use wavelet packet analysis for filtering the fault signals, extracting useful signal frequency segments from original

Bo Yan; Ping Liang; Lei Bai

2010-01-01

8

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

PubMed

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures. PMID:24975564

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

2014-09-01

9

Diagnosis of instrument fault  

Microsoft Academic Search

The diagnosis of faults in instrumentation equipment can often be confused with faults in the system. The correct diagnosis of instrument faults is of importance. Here it is described how to detect instrument faults in non-linearity. Time-varying processes that include uncertainties such as modelling error, parameter ambiguity, and input and output noise. The design of state estimation filters with zero

K. Watanabe; A. Komori; T. Kiyama

1994-01-01

10

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, University of Sheffield, U.K. amanda@dcs.shef.ac.uk Abstract: When an engine component participating of a diesel engine and 4 fault conditions, Artificial Neural Nets based on data from any one of these three

Sharkey, Amanda

11

Bearing fault diagnosis based on rough set  

Microsoft Academic Search

Bearing defects were categorized as localized and distributed. For on-line bearing fault diagnosis, in this paper, the time-domain kurtosis calculation and the frequency domain wavelet analysis were used to extract the transitory features of non-stationary vibration signal produced by the bearing distributed defects. To distributed defects, bearing fault diagnosis was built on the reducing decision based on rough set. According

Chen Xin; Yuhua Chen; Guofeng Wang; Hu Dong

2010-01-01

12

Fault diagnosis for bearing based on Mahalanobis-Taguchi system  

Microsoft Academic Search

This paper presents a method for fault diagnosis based on Mahalanobis-Taguchi system (MTS), which is applied to practical fault diagnosis for rolling element bearing. Firstly, this method utilizes time\\/frequency domain analysis for feature extraction from the vibration data. Then, a computational scheme based on Mahalanobis distance (MD) is used for fault clustering. In addition, Taguchi methods are employed to reduce

Zhipeng Wang; Zili Wang; Laifa Tao; Jian Ma

2012-01-01

13

Pattern Recognition for Automatic Machinery Fault Diagnosis  

Microsoft Academic Search

We present a generic methodology for machinery fault diagnosis through pattern recog- nition techniques. The proposed method has the advantage of dealing with complicated signatures, such as those present in the vibration signals of rolling element bearings with and without defects. The signature varies with the location and severity of bearing defects, load and speed of the shaft, and different

Qiao Sun; Ping Chen; Dajun Zhang; Fengfeng Xi

2004-01-01

14

Bearing Fault Diagnosis Based on PCA and SVM  

Microsoft Academic Search

A new method of fault diagnosis based on principal components analysis (PCA) and support vector machine is presented on the basis of statistical learning theory and the feature analysis of vibrating signal of rolling bearing. The key to the fault bearings diagnosis is feature extracting and feature classifying. Multidimensional correlated variable is converted into low dimensional independent eigenvector by means

Lu Shuang; Li Meng

2007-01-01

15

Fault diagnosis of analog circuits  

NASA Astrophysics Data System (ADS)

Theory and algorithms associated with four main categories of modern techniques used to locate faults in analog circuits are presented. These four general approaches are: the fault dictionary (FDA), the parameter identification (PIA), the fault verification (FVA), and the approximation (AA) approaches. The preliminaries and problems associated with the FDA, such as fault dictionary construction, the methods of optimum measurement selection, fault isolation criteria, and efficient methods of fault simulation, are discussed. The PIA techniques that utilize either linear or nonlinear systems of equations for identification of network elements are examined. Description of the FVA includes node-fault diagnosis, branch-fault diagnosis, subnetwork testability conditions, as well as combinatorial techniques, the failure-bound technique, and the network decomposition technique. In the AA, probabilistic methods and optimization-based methods are considered. In addition, the artificial intelligence technique and the different measures of testability are presented. A series of block diagrams is included.

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

1985-08-01

16

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

17

Diesel Engine Fault Diagnosis and Classification  

Microsoft Academic Search

Vibration signal of diesel engine fault is nonstationary and nonlinear. It is very difficult to analyze. Distinguishing diesel faults and classifying them is more difficult. In this paper, we use a new method 'Wigner Trispectrum (WT)' to describe the characteristics of vibration signals got from diesel engine. WT of signals can characterize each fault. Then WT of signals as fault

Shi Xiaochun; Hu Hongying

2006-01-01

18

Intelligent fault diagnosis of power transmission line; -.  

E-print Network

??This dissertation presents the application of recent intelligent newlinetechniques for fault diagnosis in electrical power transmission line Fault newlinesection identification classification and location are the… (more)

Malathi, V

2014-01-01

19

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

NASA Astrophysics Data System (ADS)

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

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

2012-05-01

20

Wavelet Decomposition for the Detection and Diagnosis of Faults in Rolling Element Bearings  

Microsoft Academic Search

Condition monitoring and fault diagnosis of equipment and processes are of great concern in industries. Early fault detection in machineries can save millions of dollars in emergency maintenance costs. This paper presents a wavelet-based analysis technique for the diagnosis of faults in rotating machinery from its mechanical vibrations. The choice between the discrete wavelet transform and the discrete wavelet packet

J. Chebil; G. Noel; M. Mesbah; M. Deriche

2009-01-01

21

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

22

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

23

Immune Memory Network-Based Fault Diagnosis  

Microsoft Academic Search

In this paper, based on artificial immune network, a novel approach to immune memory network-based fault diagnosis methodology for on-line fault diagnosis system is presented. The diagnosis scheme consists of the memory cell network and the antibody network. They are employed to work together for network establishment, immune identification and antibody learning. Meanwhile, the key parameters of the approach are

Lin Liang; Guanghua Xu; Tao Sun

2006-01-01

24

A ball bearing fault diagnosis method based on wavelet and EMD energy entropy mean  

Microsoft Academic Search

According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method based on wavelet and empirical mode decomposition (EMD), energy entropy mean is put forward in this paper. Firstly, original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs) and wavelet components, then the concept of energy entropy

Farshid Tavakkoli; Mohammad Teshnehlab

2007-01-01

25

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

NASA Astrophysics Data System (ADS)

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

Tse, Peter W.; Wang, Dong

2013-11-01

26

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

NASA Astrophysics Data System (ADS)

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

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

2014-01-01

27

Instrument for bearing fault diagnosis based on demodulated resonance technology  

NASA Astrophysics Data System (ADS)

Rolling bearing is a very important comment for mechanical system. The traditional measurement method is using time domain analysis and frequency domain analysis with mechanical vibration theory. However, these methods are very difficult to precisely diagnose the rolling bearing fault. Demodulated resonance technology is an effective method to diagnose bearing fault. In this paper, the realization of demodulated resonance technology is described in detail. It includes three important aspects: design of filter, envelope demodulation and spectrum zooming. Hilbert transform is an effective solution to envelope demodulation. Based on this theory, the PC controlled experimental device for fault diagnosis was built up. The ball bearing of type 6201 was measured on the experimental device, and the applications software was programmed by VC++, which had the functions of real-time monitoring, spectral analysis (FFT), digital filtering and zoom-FFT etc. So this system can get fault characteristic from the vibration speed signal with big noise and automatically identify the type of fault. The types included the fault of inner ring, outer ring and rolling element. Results showed that it has a remarkable effect on bearing fault diagnosis, and it had many advantages such as high precision, high degree of automatic measurement.

Lu, Yi; Hu, Xiao-feng; Zheng, Yong-jun

2010-08-01

28

Fault detection and diagnosis in rotating machinery  

Microsoft Academic Search

The detection and diagnosis of mechanical faults in rotating machinery using a model-based approach is studied. For certain types of faults, for example raceway faults in rolling element bearings, increase in mass unbalance and changes in stiffness and damping, algorithms suitable for real-time implementation are developed and tested

Kenneth A. Loparo; Nader Afshari; Mohammed Abdel-Magied

1998-01-01

29

Fault detection and diagnosis of rotating machinery  

Microsoft Academic Search

A model-based approach to the detection and diagnosis of mechanical faults in rotating machinery is studied in this paper. For certain types of faults, for example, raceway faults in rolling element bearings, an increase in mass unbalance, and changes in stiffness and damping, algorithms suitable for real-time implementation are developed and evaluated using computer simulation

Kenneth A. Loparo; M. L. Adams; Wei Lin; M. Farouk Abdel-Magied; Nadar Afshari

2000-01-01

30

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

31

Fault diagnosis and computer integrated manufacturing systems  

Microsoft Academic Search

This paper examines one aspect of human interaction with computer-integrated systems, that of fault diagnosis or troubleshooting. The complexity (and attendant unreliability) of the new manufacturing systems has meant that fault diagnosis has become an increasing proportion and an integral part of operators' jobs. Establishing and maintaining high levels of diagnostic accuracy and efficiency is important for a variety of

D. L. Morrison; David M. Upton

1994-01-01

32

On-Line Diagnosis of Unrestricted Faults  

Microsoft Academic Search

A formal model for the study of on-line diagnosis is introduced and used to investigate the diagnosis of unrestricted faults. Within this model a fault of a system S is considered to be a transformation of S into another system S' at some time r. The resulting faulty system is taken to be the system which looks like S up

John F. Meyer; Robert J. Sundstrom

1975-01-01

33

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

34

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

35

Planetary gearbox fault diagnosis using an adaptive stochastic resonance method  

NASA Astrophysics Data System (ADS)

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

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

2013-07-01

36

Probabilistic Performance Analysis of Fault Diagnosis Schemes  

NASA Astrophysics Data System (ADS)

The dissertation explores the problem of rigorously quantifying the performance of a fault diagnosis scheme in terms of probabilistic performance metrics. Typically, when the performance of a fault diagnosis scheme is of utmost importance, physical redundancy is used to create a highly reliable system that is easy to analyze. However, in this dissertation, we provide a general framework that applies to more complex analytically redundant or model-based fault diagnosis schemes. For each fault diagnosis problem in this framework, our performance metrics can be computed accurately in polynomial-time. First, we cast the fault diagnosis problem as a sequence of hypothesis tests. At each time, the performance of a fault diagnosis scheme is quantified by the probability that the scheme has chosen the correct hypothesis. The resulting performance metrics are joint probabilities. Using Bayes rule, we decompose these performance metrics into two parts: marginal probabilities that quantify the reliability of the system and conditional probabilities that quantify the performance of the fault diagnosis scheme. These conditional probabilities are used to draw connections between the fault diagnosis and the fields of medical diagnostic testing, signal detection, and general statistical decision theory. Second, we examine the problem of computing the performance metrics efficiently and accurately. To solve this problem, we examine each portion of the fault diagnosis problem and specify a set of sufficient assumptions that guarantee efficient computation. In particular, we provide a detailed characterization of the class of finite-state Markov chains that lead to tractable fault parameter models. To demonstrate that these assumptions enable efficient computation, we provide pseudocode algorithms and prove that their running time is indeed polynomial. Third, we consider fault diagnosis problems involving uncertain systems. The inclusion of uncertainty enlarges the class of systems that may be analyzed with our framework. It also addresses the issue of model mismatch between the actual system and the system used to design the fault diagnosis scheme. For various types of uncertainty, we present convex optimization problems that yield the worst-case performance over the uncertainty set. Finally, we discuss applications of the performance metrics and compute the performance for two fault diagnosis problems. The first problem is based on a simplified air-data sensor model, and the second problem is based on a linearized vertical take-off and landing aircraft model.

Wheeler, Timothy Josh

37

Adaptive feature extraction using sparse coding for machinery fault diagnosis  

NASA Astrophysics Data System (ADS)

In the signal processing domain, there has been growing interest in sparse coding with a learned dictionary instead of a predefined one, which is advocated as an effective mathematical description for the underlying principle of mammalian sensory systems in processing information. In this paper, sparse coding is introduced as a feature extraction technique for machinery fault diagnosis and an adaptive feature extraction scheme is proposed based on it. The two core problems of sparse coding, i.e., dictionary learning and coefficients solving, are discussed in detail. A natural extension of sparse coding, shift-invariant sparse coding, is also introduced. Then, the vibration signals of rolling element bearings are taken as the target signals to verify the proposed scheme, and shift-invariant sparse coding is used for vibration analysis. With the purpose of diagnosing the different fault conditions of bearings, features are extracted following the proposed scheme: basis functions are separately learned from each class of vibration signals trying to capture the defective impulses; a redundant dictionary is built by merging all the learned basis functions; based on the redundant dictionary, the diagnostic information is made explicit in the solved sparse representations of vibration signals; sparse features are formulated in terms of activations of atoms. The multiclass linear discriminant analysis (LDA) classifier is used to test the discriminability of the extracted sparse features and the adaptability of the learned atoms. The experiments show that sparse coding is an effective feature extraction technique for machinery fault diagnosis.

Liu, Haining; Liu, Chengliang; Huang, Yixiang

2011-02-01

38

Fault diagnosis of rotor systems using ICA based feature extraction  

Microsoft Academic Search

A method is proposed for fault diagnosis of rotor systems, with independent component analysis (ICA) based feature extraction and multi-layer perceptron (MLP) based pattern classification. By the use of ICA, feature vectors are integratedly extracted from multichannel vibration measurements collected under different operating patterns (in term of rotating speed and\\/or load). Thus, a robust multi-MLP classifier insensitive to the change

Weidong Jiao; Yongping Chang

2009-01-01

39

ICA-MLP classifier for fault diagnosis of rotor system  

Microsoft Academic Search

A novel classifier is proposed for fault diagnosis of rotor system, with independent component analysis (ICA) based feature extraction and multi-layer perceptron (MLP) based pattern classification. By the use of ICA, feature vectors are integratedly extracted from multi-channel vibration measurements collected under different operating patterns (in term of rotating speed and\\/or load). Thus, a robust multi-MLP classifier insensitive to the

Weidong Jiao; Yongping Chang

2009-01-01

40

Intelligent diagnosis method for a centrifugal pump using features of vibration signals  

Microsoft Academic Search

In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because\\u000a the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because\\u000a definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis\\u000a method for a centrifugal pump system

Huaqing Wang; Peng Chen

2009-01-01

41

Tractable particle filters for robot fault diagnosis  

Microsoft Academic Search

Experience has shown that even carefully designed and tested robots may encounter anomalous situations. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner. Robot fault diagnosis typically requires tracking a very large number of possible faults in complex non-linear dynamic systems with noisy sensors. Traditional methods either ignore

Vandi Verma; GEOFF GORDON; REID SIMMONS; SEBASTIAN THRUN

2005-01-01

42

Tractable particle filters for robot fault diagnosis  

NASA Astrophysics Data System (ADS)

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

Verma, Vandi

43

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

44

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

45

Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses  

Microsoft Academic Search

In this work, signal processing and pattern recognition techniques are combined to diagnose the severity of bearing faults. The signals were pre-processed by detrended-fluctuation analysis (DFA) and rescaled-range analysis (RSA) techniques and investigated by neural networks and principal components analysis in a total of four schemes. Three different levels of bearing fault severities together with a standard no-fault class were

E. P. de Moura; C. R. Souto; A. A. Silva; M. A. S. Irmão

2011-01-01

46

Application of improved EMD algorithm for the fault diagnosis of reciprocating pump valves with spring failure  

Microsoft Academic Search

This paper presents an improved EMD algorithm for the fault diagnosis of reciprocating pump valves with spring failure. The vibration signal of reciprocating pumps is of typical nonstationarity. Although the EMD algorithm adopting the cubic spline interpolation is an effective tool processing non-stationary signal, it couldnpsilat accurately extract fault characteristics from the highly nonstationary signals. So the paper presents a

Liu Shulin; Zhao Haifeng; Wang Hui; Ma Rui

2007-01-01

47

Fault diagnosis of rolling element bearing using time-domain features and neural networks  

Microsoft Academic Search

Rolling element bearings are critical mechanical components in rotating machinery. Fault detection and diagnosis in the early stages of damage is necessary to prevent their malfunctioning and failure during operation. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper presents an algorithm using feed forward neural

B. Sreejith; A. K. Verm; A. Srividya

2009-01-01

48

A method of multi-class faults classification based-on Mahalanobis-Taguchi system using vibration signals  

Microsoft Academic Search

In this paper, an improved Mahalanobis-Taguchi system based fault diagnosis scheme is presented, vibration signals are used as the signal resource. Mahalanobis-Taguchi System is utilized for fault clustering method in order to classify faults into different categories, Lipschitz Exponents are used to extract characteristic vectors. Firstly, the procedure of implementing Mahalanobis-Taguchi System is introduced, a multi-class faults classification method is

Jiangtao Ren; Yuanwen Cai; Xiaochen Xing; Jing Chen

2011-01-01

49

Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy mean and root mean square (RMS)  

Microsoft Academic Search

According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy mean and root mean square (RMS), energy entropy mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy

O. R. Seryasat; Mahdi Aliyari Shoorehdeli; F. Honarvar; Abolfazl Rahmani

2010-01-01

50

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

PubMed

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

51

Completing fault models for abductive diagnosis  

SciTech Connect

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

Knill, E. [Los Alamos National Lab., NM (United States); Cox, P.T.; Pietrzykowski, T. [Technical Univ., NS (Canada)

1992-11-05

52

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

53

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

54

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

55

Development of artificial neural network based fault diagnosis of induction motor dearing  

Microsoft Academic Search

The common component failure of induction motor is bearing. Thus, timely detection and diagnosis of induction motor bearing (IMB) is very crucial in order to prevent sudden damage. This paper proposes developing artificial neural network (ANN) model of IMB fault diagnosis by using Elman Network. The vibration signal obtained from Case Western Reserve University website are been used as input

Abd Kadir Mahamad; Takashi Hiyama

2008-01-01

56

Automatic bearing fault pattern recognition using vibration signal analysis  

Microsoft Academic Search

This paper presents vibration analysis techniques for fault detection in rotating machines. Rolling-element bearing defects inside a motor pump are the object of study. A dynamic model of the faults usually found in this context is presented. Initially a graphic simulation is used to produce the signals. Signal processing techniques, like frequency filters, Hilbert transform and spectral analysis are then

E. Mendel; L. Z. Mariano; I. Drago; S. Loureiro; T. W. Rauber; R. J. Batista

2008-01-01

57

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

58

Fault diagnosis of distributed networked control systems  

Microsoft Academic Search

In order to deal with the limited bandwidth of the network and to avoid the uncertainty caused by transmission delays and packet loss, two-level DNCS with deterministic communication logic is presented and central fault diagnosis unit is designed under this communication pattern. The central FDI unit and each subsystem both have identical estimators and thus identical state estimate. For each

Qun Zong; Wenjing Liu; Liankun Sun

2007-01-01

59

ModelBased Diagnosis for Open Systems Fault Management  

E-print Network

, and correction of faults remains intractable. Fault management is there- fore a prominent functional area of open1 Model­Based Diagnosis for Open Systems Fault Management F. Steimann, P. Fr¨ohlich and W. Nejdl for model­based diagnosis. Based thereon, we present an efficient algorithm that localizes faults

Steimann, Friedrich

60

Robust nonlinear fault diagnosis in input-output systems  

Microsoft Academic Search

The design and analysis of fault diagnosis architectures using the model-based analyticalredundancy approach has received considerable attention during the last two decades. One ofthe key issues in the design of such fault diagnosis schemes is the effect of modeling uncertaintieson their performance. This paper describes a fault diagnosis algorithm for a class of nonlineardynamic systems with modeling uncertainties when not

Arun T. Vemuri; Marios M. Polycarpou

1997-01-01

61

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

62

Distributed Fault Diagnosis Using Consensus of Unobservable Markov Chains  

E-print Network

Distributed Fault Diagnosis Using Consensus of Unobservable Markov Chains Dejan P. Jovanovi@maths.uq.edu.au Abstract A fault diagnosis procedure is proposed based on consensus in a group of local agents is extended to accommodate Hidden Markov models (HMMs). Index Terms Fault diagnosis, consensus algorithm

Pollett, Phil

63

Fault Diagnosis of Timed Systems In this Chapter, we review the main results pertaining to the problem of fault diagnosis  

E-print Network

Chapter 4 Fault Diagnosis of Timed Systems In this Chapter, we review the main results pertaining to the problem of fault diagnosis of timed automata. Timed automata are introduced in Chapter 1 and Chapter 2 from a particular state, ...). The notion of fault diagnosis for discrete event systems (DES

Paris-Sud XI, Université de

64

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

65

Monitoring and Diagnosis of Multiple Incipient Faults Using Fault Tree Induction  

E-print Network

synthesis (FTS) and fault tree analysis (FTA). FTS involves the construction of fault trees, and typicallyMonitoring and Diagnosis of Multiple Incipient Faults Using Fault Tree Induction Michael G. M algorithm for induction of fault trees. It learns from an examples database comprising sensor recordings

Madden, Michael

66

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

67

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

68

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

NASA Astrophysics Data System (ADS)

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

Chen, Man; Wang, Liyong; Ma, Biao

69

Gear faults diagnosis based on wavelet-AR model and PCA  

NASA Astrophysics Data System (ADS)

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

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

2010-08-01

70

Composite Bending Box section Modal Vibration fault Detection  

Microsoft Academic Search

Abstract: 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

Rudy Werlink

71

A fault diagnosis approach for roll bearing based on wavelet-SOFM network  

Microsoft Academic Search

A novel method of pattern recognition and fault diagnosis in roll bearing based on the wavelet-neural network is proposed according to the frequency spectrum characteristics of vibration signal. Based on the advantage of multi-dimensional multi-scaling decomposition of wavelet packets, the abrupt change information can be obtained and the features related to the fault of roll bearing is extracted through the

Fei Zhong; Xiang Zhou; Tielin Shi; Tao He

2007-01-01

72

Software Faults Diagnosis in Complex OTS Based Safety Critical Systems  

Microsoft Academic Search

This work addresses the problem of software fault di- agnosis in complex safety critical software systems. The transient manifestations of software faults represent a chal- lenging issue since they hamper a complete knowledge of the system fault model at design\\/development time. By tak- ing into account existing diagnosis techniques, the paper proposes a novel diagnosis approach, which combines the detection

Gabriella Carrozza; Domenico Cotroneo; Stefano Russo

2008-01-01

73

Fault diagnosis of ball bearings using machine learning methods  

Microsoft Academic Search

Ball bearings faults are one of the main causes of breakdown of rotating machines. Thus, detection and diagnosis of mechanical faults in ball bearings is very crucial for the reliable operation. This study is focused on fault diagnosis of ball bearings using artificial neural network (ANN) and support vector machine (SVM). A test rig of high speed rotor supported on

P. K. Kankar; Satish C. Sharma; S. P. Harsha

2011-01-01

74

Fault diagnosis of automobile engine based on support vector machine  

Microsoft Academic Search

n, 104101141@163.co m Abstract - Support vector machine (SVM) based on classification is applied for fault diagnosis of the automotive engine. The basic idea is to identify the information by using the trained SVM model to classify new fault samples. The data from the engine simulation model by AMESim software are fault features extracted, and these fault characteristic parameters have

Wang Dejun; Xing Tianliang; Lin Chengdong; Wang Lihua

2011-01-01

75

SSME fault monitoring and diagnosis expert system  

NASA Technical Reports Server (NTRS)

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

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

1989-01-01

76

Understanding Vibration Spectra of Planetary Gear Systems for Fault Detection  

NASA Technical Reports Server (NTRS)

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

Mosher, Marianne

2003-01-01

77

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

78

Vibration Signature Analysis of a Faulted Gear Transmission System  

NASA Technical Reports Server (NTRS)

A comprehensive procedure in predicting faults in gear transmission systems under normal operating conditions is presented. Experimental data was obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. Time synchronous averaged vibration data was recorded throughout the test as the fault progressed from a small single pit to severe pitting over several teeth, and finally tooth fracture. A numerical procedure based on the Winger-Ville distribution was used to examine the time averaged vibration data. Results from the Wigner-Ville procedure are compared to results from a variety of signal analysis techniques which include time domain analysis methods and frequency analysis methods. Using photographs of the gear tooth at various stages of damage, the limitations and accuracy of the various techniques are compared and discussed. Conclusions are drawn from the comparison of the different approaches as well as the applicability of the Wigner-Ville method in predicting gear faults.

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

1994-01-01

79

Fault Diagnosis and Logic Debugging Using Boolean Satisfiability  

E-print Network

1 Fault Diagnosis and Logic Debugging Using Boolean Satisfiability Alexander Smith, Student Member diagnosis and logic debugging have not been addressed within a satisfiability-based framework. This work proposes a novel Boolean satisfiability-based method for multiple fault diagnosis and multiple design error

Viglas, Anastasios

80

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

81

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

82

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

83

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

84

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

NASA Technical Reports Server (NTRS)

This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) fault diagnostic system introduced in the preceding article. For this vibration data from two different helicopter gearboxes: OH-58A and S-61, are used. A salient feature of SBCN is its reliance on the knowledge of the gearbox structure and the type of features obtained from processed vibration signals as a substitute to training. To formulate this knowledge, approximate vibration transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component faults on vibration features. The validity of the structural influences is evaluated by comparing them with those obtained from experimental RMS values. These influences are also evaluated ba comparing them with the weights of a connectionist network trained though supervised learning. The results indicate general agreement between the modeled and experimentally obtained influences. The vibration data from the two gearboxes are also used to evaluate the performance of SBCN in fault diagnosis. The diagnostic results indicate that the SBCN is effective in directing the presence of faults and isolating them within gearbox subsystems based on structural influences, but its performance is not as good in isolating faulty components, mainly due to lack of appropriate vibration features.

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

1998-01-01

85

UNSUPERVISED CLUSTERING FOR FAULT DIAGNOSIS IN NUCLEAR POWER PLANT COMPONENTS  

E-print Network

1 UNSUPERVISED CLUSTERING FOR FAULT DIAGNOSIS IN NUCLEAR POWER PLANT COMPONENTS Piero Baraldi1 on transients originated by different faults in the pressurizer of a nuclear power reactor. Key Words: Fault of Nuclear Power Plants (NPPs) [Cheon et al., 1993; Kim et al., 1996; Reifman, 1997; Zio et al., 2006a; Zio

Boyer, Edmond

86

A novel extension method for transformer fault diagnosis  

Microsoft Academic Search

Dissolved gas analysis (DGA) is one of the most useful techniques to detect incipient faults in power transformers. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational variables. In this paper, a novel extension method is presented for fault diagnosis of power transformers,

Mang-Hui Wang

2003-01-01

87

Diagnosis of Realistic Bridging Faults with Single Stuckat Information  

E-print Network

Diagnosis of Realistic Bridging Faults with Single Stuck­at Information Brian Chess David B. Lavo F Precise failure analysis requires accurate fault diag­ nosis. A previously proposed method for diagnosing bridging faults using single stuck­at dictionaries was applied only to small circuits, produced large

Larrabee, Tracy

88

Stator current demodulation for induction machine rotor faults diagnosis  

E-print Network

and Park transform for dynamic rotor faults (broken or cracked rotor bars and dynamic rotor eccentricityStator current demodulation for induction machine rotor faults diagnosis El Houssin El Bouchikhi--Several studies have demonstrated that induction machine faults introduce phase and/or amplitude modulation

Boyer, Edmond

89

Intelligent diagnosis of transparent faults in process control  

Microsoft Academic Search

Various alarm systems with intelligent diagnosis support are being developed for industrial processes. These systems are unable to detect faults in certain situations. There could be some faults which may be generated in a process plant that cannot be detected until they become catastrophic, as the normal function of the process is not disturbed. The authors treat these faults as

P. K. Chande; S. Kher; M. Shirvastava

1992-01-01

90

Exclusive Test and its Applications to Fault Diagnosis  

Microsoft Academic Search

Exclusive Test We introduce a new type of test, culled ezclusive test, und discuss its application to fault diagnosis in combi- national circuits, A test that detects exactly one fault from a given pair of faults is culled an exclusive test, In general, generation of an exclusive test by a con- ventionul automatic test generator requires a model of the

Vishwani D. Agrawal; Dong Hyun Baik; Yong Chang Kim; Kewal K. Saluja

2003-01-01

91

Fault Diagnosis of Bearing Based on Fractal Method  

Microsoft Academic Search

Fractal geometry is a new method to apply in analyzing fault signals. After researching the characteristic of rolling bearings, a new quantificational definition about fault signals of rolling bearings is proposed. Based on fractal theory and the conception of box dimension it can describe both non-stationary and non-linear signals of vibration signals generated by rolling bearings. Experiment results shows that

Lu Shuang; Liu Jing

2006-01-01

92

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

NASA Technical Reports Server (NTRS)

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

Mallela, S.; Masson, G. M.

1980-01-01

93

The intelligent fault diagnosis frameworks based on fuzzy integral  

Microsoft Academic Search

Fuzzy integral is an information aggregation and combination process in a multi-criteria environment using fuzzy measures. This paper presents a new data fusion method using fuzzy integral for fault diagnosis. The method consists of two frameworks. The first framework was employed to identify the relations between features and a specified fault. The second framework was implemented to integrate different diagnosis

M. Karakose; I. Aydin; E. Akin

2010-01-01

94

Fault Diagnosis of Rolling Bearing Based on Wavelet Packet Transformation  

Microsoft Academic Search

\\u000a The faults of rolling bearings frequently occur in rotary machinery, therefore the rolling bearings fault diagnosis is a very\\u000a important research project. In this paper, a method of pattern recognition for fault diagnosis of rolling bearing is proposed,\\u000a which is based on wavelet packet transformation combined with Statistics. Firstly, the wavelet packet analysis is utilized\\u000a to divide the dynamic signal

Zhitong Jiang; Chengfei Zhu; Guanqing Chang; Hongxing Chang

95

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

96

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

97

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

98

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

99

Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform  

Microsoft Academic Search

The vibration signal of the run-up or run-down process is more complex than that of the stationary process. A novel approach\\u000a to fault diagnosis of roller bearing under run-up condition based on order tracking and Teager-Huang transform (THT) is presented.\\u000a This method is based on order tracking, empirical mode decomposition (EMD) and Teager Kaiser energy operator (TKEO) technique.\\u000a The nonstationary

Hui Li; Yuping Zhang; Haiqi Zheng

2010-01-01

100

BAYESIAN NETWORKS AND MUTUAL INFORMATION FOR FAULT DIAGNOSIS OF  

E-print Network

BAYESIAN NETWORKS AND MUTUAL INFORMATION FOR FAULT DIAGNOSIS OF INDUSTRIAL SYSTEMS Sylvain Verron for industrial process diagnosis. This method is based on the use of a bayesian network as a classifier. But diagnosis, bayesian network classfiers 1. INTRODUCTION Nowadays, industrial processes are more and more

Paris-Sud XI, Université de

101

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

102

Classification of time-frequency representations using improved morphological pattern spectrum for engine fault diagnosis  

NASA Astrophysics Data System (ADS)

Time-frequency representations (TFR) have been intensively employed for analyzing vibration signals in the field of mechanical faults diagnosis. However, in many applications, TFR are simply utilized as a visual aid. It is very attractive to investigate the utility of TFR for automatic classification of vibration signals. A key step for this work is to extract discriminative parameters from TFR as input feature vector for classifiers. This paper contributes to this ongoing investigation by developing an improved morphological pattern spectrum (IMPS) for feature extraction from TFR. The S transform, which combines the separate strengths of the short time Fourier transform and wavelet transforms, is chosen to perform the time-frequency analysis of vibration signals. Then, we present an improved morphological pattern spectrum (IMPS) scheme, which utilizes the first moment replace of the volume measure used in traditional morphological pattern spectrum (MPS) method. The promise of IMPS is illustrated by performing our procedure on vibration signals measured from an engine with five operating states. Experimental results have demonstrated the presented IMPS to be an effective approach for characterizing the TFR of vibration signals in engine fault diagnosis.

Li, Bing; Mi, Shuang-shan; Liu, Peng-yuan; Wang, Zheng-jun

2013-06-01

103

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

PubMed

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

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

2013-03-01

104

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

105

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

106

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

107

Online sensor fault diagnosis for robust chiller sequencing control  

Microsoft Academic Search

Chiller chilled water flow rate, supply and return temperature are used in building cooling load direct measurement in central chilling systems. Healthy sensor measurements of them are essential for proper chiller sequencing control. Site experience indicates that these measurements are easily corrupted by systematic errors or measurement faults. Therefore, an online sensor fault detection and diagnosis (FDD) strategy based on

Yongjun Sun; Shengwei Wang; Gongsheng Huang

2010-01-01

108

Fault Detection and Diagnosis Method for VAV Terminal Units  

E-print Network

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

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

2004-01-01

109

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

110

Fault diagnosis of electrical machines-a review  

Microsoft Academic Search

Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down time and improve salability. Prodigious improvement in signal processing hardware and software has made

Subhasis Nandi; Hamid A. Toliyat

1999-01-01

111

Fault detection and diagnosis capabilities of test sequence selection  

E-print Network

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

Thulsiraman, Krishnaiyan

112

Diagnosis of Realistic Defects Based on the X-Fault Model Ilia Polian  

E-print Network

Diagnosis of Realistic Defects Based on the X-Fault Model Ilia Polian Yusuke Nakamura Piet Engelke by conventional fault models are a challenge for state-of-the-art fault diagnosis techniques. The X-fault model the performance of the X-fault diagnosis for a number of defect classes leading to highly complex circuit behavior

Polian, Ilia

113

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

114

Optimal sinusoidal modelling of gear mesh vibration signals for gear diagnosis and prognosis  

NASA Astrophysics Data System (ADS)

In this paper, the synchronous signal average of gear mesh vibration signals is modelled with the multiple modulated sinusoidal representations. The signal model parameters are optimised against the measured signal averages by using the batch learning of the least squares technique. With the optimal signal model, all components of a gear mesh vibration signal, including the amplitude modulations, the phase modulations and the impulse vibration component induced by gear tooth cracking, are identified and analysed with insight of the gear tooth crack development and propagation. In particular, the energy distribution of the impulse vibration signal, extracted from the optimal signal model, provides sufficient information for monitoring and diagnosing the evolution of the tooth cracking process, leading to the prognosis of gear tooth cracking. The new methodologies for gear mesh signal modelling and the diagnosis of the gear tooth fault development and propagation are validated with a set of rig test data, which has shown excellent performance.

Man, Zhihong; Wang, Wenyi; Khoo, Suiyang; Yin, Juliang

2012-11-01

115

Gearbox fault diagnosis based on frame decomposition  

Microsoft Academic Search

Frame decomposition is flexible in representing arbitrary signals, and thereby is effective in matching the characteristic structure of and extracting the time-frequency features directly. It is applied to analyzing the gearbox vibration signals under healthy and faulty statuses. Based on a wavelet frame, the periodic impulses characteristic of localized damaged gear vibration are extracted in joint time-frequency domain, and the

Zhipeng Feng; Rujiang Hao; Jin Zhang; Fulei Chu

2010-01-01

116

Automated misfire diagnosis in engines using torsional vibration and block rotation  

NASA Astrophysics Data System (ADS)

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

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

2012-05-01

117

A Comparative Study of SVM Classifiers and Artificial Neural Networks Application for Rolling Element Bearing Fault Diagnosis using Wavelet Transform Preprocessing  

Microsoft Academic Search

? Abstract—Effectiveness of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) classifiers for fault diagnosis of rolling element bearings are presented in this paper. The characteristic features of vibration signals of rotating driveline that was run in its normal condition and with faults introduced were used as input to ANN and SVM classifiers. Simple statistical features such as standard

Commander Sunil Tyagi

2008-01-01

118

Automated diagnosis of rolling bearing faults in electrical drives  

Microsoft Academic Search

This paper deals with a new automated diagnosis method for detecting bearing faults. The diagnosis is carried out by frequency response analysis of the mechanical system of the drive. The detection of bearing damages of a ball bearing on motor and load side of the mechanical drive system is addressed. The mechanics is assumed to be a two-inertia-system with one

Henning Zoubek; Sebastian Villwock; Mario Pacas

2007-01-01

119

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

120

APPLICATION OF WAVELET PACKET ANALYSIS FOR FAULT DETECTION IN ELECTROMECHANICAL SYSTEMS BASED ON TORSIONAL VIBRATION MEASUREMENT  

Microsoft Academic Search

This paper primarily focuses on detecting electrical faults in turbine generator sets by monitoring torsional vibrations with the help of the non-contact measurement technique and analysing the data acquired from torsional vibration meter. Torsional vibrations in shaft trains can be excited by periodic excitation due to a variety of electromagnetic disturbances or unsteady flow in large steam turbine generator sets

X. Li; L. Qu; G. Wen; C. Li

2003-01-01

121

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

122

Application of Hilbert-Huang transform and Mahalanobis-Taguchi System in mechanical fault diagnostics using vibration signals  

Microsoft Academic Search

As for non-destructive condition monitoring method, vibration signals are measured and analyzed to diagnose mechanical faults. A method of mechanical fault levels identification is proposed in this paper using vibration signals. Firstly, Hilbert-Huang transform is used to analyze the signal. Fault information is obtained. And energy ratio of intrinsic mode functions is taken as fault characteristics. Secondly, different levels of

Ren Jiangtao; Cai Yuanwen; Xing Xiaochen

2011-01-01

123

Fault Diagnosis of Roller Bearing Based on PCA and Multi-class Support Vector Machine  

Microsoft Academic Search

\\u000a This paper discusses the fault features selection using principal component analysis and using multi-class support vector\\u000a machine (MSVM) for bearing faults classification. The bearings vibration signal is obtained from experiment in accordance\\u000a with the following conditions: normal bearing, bearing with inner race fault, bearing with outer race fault and bearings with\\u000a balls fault. Statistical parameters of vibration signal such as

Guifeng Jia; Shengfa Yuan; Chengwen Tang

2010-01-01

124

Auxiliary signal design in fault detection and diagnosis  

NASA Astrophysics Data System (ADS)

Fault-detection and diagnosis schemes for systems represented by linear MIMO stochastic models are developed analytically, with a focus on on the design and application of auxiliary signals. The basic principles of optimal-input design are reviewed, and consideration is given to the sequential probability ratio test (SPRT), auxiliary signals for improving SPRT fault detection, and the extension of the SPRT to multiple-hypothesis testing. Two chapters are devoted to the application of the SPRT to a model chemical plant (producing anhydrous caustic soda), including model derivation, model identification, detection of type I and type II faults, and the fault-diagnosis decision-making mechanism. Numerical results are presented in graphs and briefly characterized.

Zhang, Xue Jun

125

A data structure and algorithm for fault diagnosis  

NASA Technical Reports Server (NTRS)

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

Bosworth, Edward L., Jr.

1987-01-01

126

Roller bearings fault diagnosis based on LS-SVM  

Microsoft Academic Search

A new method of roller bearings fault diagnosis based on least squares support vector machines (LS-SVM) was presented. Feature selection method based on simulated annealing (SA) algorithm was discussed in this paper. LS-SVM classifier was constructed for bearing faults. Compared with the Artificial Neural Network based method, the LS-SVM based method possessed desirable advantages. Experiment shows that the presented method

Wentao Sui; Dan Zhang; Wilson Wang

2009-01-01

127

On the application of envelope-wavelet analysis in the fault diagnosis of rolling bearing  

Microsoft Academic Search

In this paper, the fault mechanism and vibration characteristics of typical faults of rolling bearing are analyzed. In order to increase the signal noise ratio, an approach to extract fault features from their vibrations with envelope analysis and orthogonal decomposition of wavelet is presented, where envelope analysis is used as the preprocessing of the decomposition. In addition, a novel rolling

Tong-Xiao Zhang; Xi-Jin Guo; Zhen Wang

2005-01-01

128

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

129

Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors  

NASA Astrophysics Data System (ADS)

Gearbox fault diagnosis is very important for preventing catastrophic accidents. Vibration signals of gearboxes measured by sensors are useful and dependable as they carry key information related to the mechanical faults in gearboxes. Effective signal processing techniques are in necessary demands to extract the fault features contained in the collected gearbox vibration signals. Overcomplete rational dilation discrete wavelet transform (ORDWT) enjoys attractive properties such as better shift-invariance, adjustable time-frequency distributions and flexible wavelet atoms of tunable oscillation in comparison with classical dyadic wavelet transform (DWT). Due to these advantages, ORDWT is presented as a versatile tool that can be adapted to analysis of gearbox fault features of different types, especially in analyzing the non-stationary and transient characteristics of the signals. Aiming to extract the various types of fault features confronted in gearbox fault diagnosis, a fault feature extraction technique based on ORDWT is proposed in this paper. In the routine of the proposed technique, ORDWT is used as the pre-processing decomposition tool, and a corresponding post-processing method is combined with ORDWT to extract the fault feature of a specific type. For extracting periodical impulses in the signal, an impulse matching algorithm is presented. In this algorithm, ORDWT bases of varied time-frequency distributions and varied oscillatory natures are adopted, moreover an improved signal impulsiveness measure derived from kurtosis is developed for choosing optimal ORDWT bases that perfectly match the hidden periodical impulses. For demodulation purpose, an improved instantaneous time-frequency spectrum (ITFS), based on the combination of ORDWT and Hilbert transform, is presented. For signal denoising applications, ORDWT is enhanced by neighboring coefficient shrinkage strategy as well as subband selection step to reveal the buried transient vibration contents. The proposed fault feature extraction technique is applied in a range of engineering applications, and the processing results demonstrate that the ORDWT-based feature extraction technique successfully identifies the incipient fault features in the cases where DWT and empirical mode decomposition method are less effective.

Chen, Binqiang; Zhang, Zhousuo; Sun, Chuang; Li, Bing; Zi, Yanyang; He, Zhengjia

2012-11-01

130

Application of statistics filter method and clustering analysis in fault diagnosis of roller bearings  

NASA Astrophysics Data System (ADS)

Condition diagnosis of roller bearings depends largely on the feature analysis of vibration signals. Spectrum statistics filter (SSF) method could adaptively reduce the noise. This method is based on hypothesis testing in the frequency domain to eliminate the identical component between the reference signal and the primary signal. This paper presents a statistical parameter namely similarity factor to evaluate the filtering performance. The performance of the method is compared with the classical method, band pass filter (BPF). Results show that statistics filter is preferable to BPF in vibration signal processing. Moreover, the significance level awould be optimized by genetic algorithms. However, it is very difficult to identify fault states only from time domain waveform or frequency spectrum when the effect of the noise is so strong or fault feature is not obvious. Pattern recognition is then applied to fault diagnosis in this study through system clustering method. This paper processes experiment rig data that after statistics filter, and the accuracy of clustering analysis increases substantially.

Song, L. Y.; Wang, H. Q.; Gao, J. J.; Yang, J. F.; Liu, W. B.; Chen, P.

2012-05-01

131

Expert network development environment for automating machine fault diagnosis  

NASA Astrophysics Data System (ADS)

Automation of machine fault diagnosis is approached using an expert network which captures human expertise in symbolic form and is refined using historical performance data. A development environment for expert networks which draws from knowledge implicit in historical data to build and refine the expert network dynamically is presented. The testbed for the design of this development environment is fault diagnosis for gas chromatographs used in detecting contaminants in soil samples. The expert knowledge capture procedure for this testbed problem and its implementation in the G2 commercial expert system package were presented at AeroSense '95. The development environment for the fault diagnosis system includes several data-assisted methods which complement the expert knowledge embedded in the expert network. The first module presented, NetMaker, automatically constructs the network in G2 from an ASCH knowledge table file. NetMedic, the second module, is a data- assisted method which is used to confirm, refine, and augment expert knowledge in order to make the knowledge table more accurate. These tools form the foundation of the expert network development environment. The basis of the expert networks developed for machine fault diagnosis is the knowledge table, a matrix of signature symptoms and machine faults related by linguistic qualifiers. The knowledge table undergoes frequent revision due to refinements from the experts, data-enhanced knowledge from NetMedic, and improved symptom extraction algorithms. NetMaker satisfies the need to easily revise the knowledge tables and incorporate them seamlessly into the G2 expert network environment. NetMedic is used to improve machine fault diagnosis by suggesting alterations to the physical architecture of the knowledge table and the associated expert network, including several non-trainable parameters. This utility discovers relationships in the sample data using statistics from historical data. The experts may then incorporate new relationships in the expert knowledge as well as confirm existing knowledge. This approach preserves the ability to retrieve the expert knowledge from the modified network.

Adair, Kristin L.; Levis, Alan P.; Hruska, Susan I.

1996-03-01

132

Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network  

NASA Astrophysics Data System (ADS)

After analyzing the shortcomings of current feature extraction and fault diagnosis technologies, a new approach based on wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) are combined to extract fault feature frequency and neural network for rotating machinery early fault diagnosis is proposed. Acquisition signals with fault frequency feature are decomposed into a series of narrow bandwidth using WPD method for de-noising, then, the intrinsic mode functions (IMFs), which usually denoted the features of corresponding frequency bandwidth can be obtained by applying EMD method. Thus, the component of IMF with signal feature can be separated from all IMFs and the energy moment of IMFs is proposed as eigenvector to effectively express the failure feature. The classical three layers BP neural network model taking the fault feature frequency as target input of neural network, the 5 spectral bandwidth energy of vibration signal spectrum as characteristic parameter, and the 10 types of representative rotor fault as output can be established to identify the fault pattern of a machine. Lastly, the fault identification model of rotating machinery with rotor lateral early crack based on BP neural network is taken as an example. The results show that the proposed method can effectively get the signal feature to diagnose the occurrence of early fault of rotating machinery.

Bin, G. F.; Gao, J. J.; Li, X. J.; Dhillon, B. S.

2012-02-01

133

A study on automatic machine condition monitoring and fault diagnosis for bearing and unbalanced rotor faults  

Microsoft Academic Search

this paper demonstrates a simple and effective data-based scheme for the continuous automatic condition monitoring and diagnosis of bearing and unbalanced rotor faults. The key idea is to use a normalized cross-correlation sum operator as similarity measure for the automatic classification of machine faults using the k-nearest neighbor (k-NN) algorithm. This technique is both noise tolerance and shift-invariance. The experiments

W.-Y. Chen; J.-X. Xu; S. K Panda

2011-01-01

134

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

NASA Astrophysics Data System (ADS)

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

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

2008-12-01

135

Heuristics for fault diagnosis when testing from finite state machines  

E-print Network

State Machines (FSMs), a failure observed in the Implementation Under Test (IUT) is called a symptom as a Finite State Machine (FSM) [1, 2, 3, 4, 5]. 1 #12;1.1 Background In FSM-based testing, a standard testHeuristics for fault diagnosis when testing from finite state machines 1 Qiang Guo, 2 Robert M

Singer, Jeremy

136

Hierarchical fault detection and diagnosis for unmanned ground vehicles  

Microsoft Academic Search

This paper presents a fault detection and diagnosis (FDD) method for unmanned ground vehicles (UGVs) operating in multi agent systems. The hierarchical FDD method consisting of three layered software agents is proposed: Decentralized FDD (DFDD), centralized FDD (CFDD), and supervisory FDD (SFDD). Whereas the DFDD is based on modular characteristics of sensors, actuators, and controllers connected or embedded to a

Sunho Lee; Seunghan Yang; Bongsob Song

2009-01-01

137

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

138

Observer-Based Fault Diagnosis of Power Electronics Systems  

E-print Network

Observer-Based Fault Diagnosis of Power Electronics Systems Kieran T. Levin, Eric M. Hope- invariant systems to switched-linear systems commonly encountered in power electronics. The result, and industrial control equip- ment. In all these applications, power electronics systems are the essential

Liberzon, Daniel

139

Fault diagnosis of plant systems using immune networks  

Microsoft Academic Search

Recently, systems such as chemical and nuclear plant systems have been increasing in scale and complexity. In these systems, when a certain device (unit) in a plant system becomes faulty, its influence propagates through the whole system, and then causes a fatal situation. To construct the safety and reliability of plant systems, the necessity for an efficient fault diagnosis technique

A. Ishiguro; Y. Watanabe; Yoshiki UCHIKAWA

1994-01-01

140

Detection and Diagnosis of HVAC Faults via Electrical Load Monitoring  

Microsoft Academic Search

Detection and diagnosis of faults (FDD) in HVAC equipment have typically relied on measurements of variables available to a control system, including temperatures, flows, pressures, and actuator control signals. Electrical power at the level of a fan, pump, or chiller has been generally ignored because power meters are rarely installed at individual loads. This paper presents two techniques for using

S. R. Shaw; L. K. Norford; D. D. Luo; S. B. Leeb

2002-01-01

141

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

Koutsoukos, Xenofon D.

142

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

PubMed

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

143

Wavelet packets analysis of rolling bearing vibration signal and fault testing  

Microsoft Academic Search

Proposes a method for fault testing on a rolling bearing based on wavelet transformation and pattern recognition. Wavelet packets decomposition is used to extract features of dynamic vibration information; K-NN is introduced to test the fault on a rolling bearing. Experiments show that the proposed method attains a satisfactory effect.

Xia Limin

2002-01-01

144

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

E-print Network

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

Tolbert, Leon M.

145

A new model-free method performing closed-loop fault diagnosis  

E-print Network

A new model-free method performing closed-loop fault diagnosis for an aeronautical system Julien), CNRS-SUPELEC-Univ Paris-Sud, France, eric.walter@lss.supelec.fr Abstract: Fault diagnosis for a closed detection and isolation, fault diagnosis, guidance and control. 1. INTRODUCTION In-flight securement

Paris-Sud XI, Université de

146

Fault Diagnosis of Power Systems Using Intelligent Systems  

NASA Technical Reports Server (NTRS)

The power system operator's need for a reliable power delivery system calls for a real-time or near-real-time Al-based fault diagnosis tool. Such a tool will allow NASA ground controllers to re-establish a normal or near-normal degraded operating state of the EPS (a DC power system) for Space Station Alpha by isolating the faulted branches and loads of the system. And after isolation, re-energizing those branches and loads that have been found not to have any faults in them. A proposed solution involves using the Fault Diagnosis Intelligent System (FDIS) to perform near-real time fault diagnosis of Alpha's EPS by downloading power transient telemetry at fault-time from onboard data loggers. The FDIS uses an ANN clustering algorithm augmented with a wavelet transform feature extractor. This combination enables this system to perform pattern recognition of the power transient signatures to diagnose the fault type and its location down to the orbital replaceable unit. FDIS has been tested using a simulation of the LeRC Testbed Space Station Freedom configuration including the topology from the DDCU's to the electrical loads attached to the TPDU's. FDIS will work in conjunction with the Power Management Load Scheduler to determine what the state of the system was at the time of the fault condition. This information is used to activate the appropriate diagnostic section, and to refine if necessary the solution obtained. In the latter case, if the FDIS reports back that it is equally likely that the faulty device as 'start tracker #1' and 'time generation unit,' then based on a priori knowledge of the system's state, the refined solution would be 'star tracker #1' located in cabinet ITAS2. It is concluded from the present studies that artificial intelligence diagnostic abilities are improved with the addition of the wavelet transform, and that when such a system such as FDIS is coupled to the Power Management Load Scheduler, a faulty device can be located and isolated from the rest of the system. The benefit of these studies provides NASA with the ability to quickly restore the operating status of a space station from a critical state to a safe degraded mode, thereby saving costs in experimentation rescheduling, fault diagnostics, and prevention of loss-of-life.

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

1996-01-01

147

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

148

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

149

Detrended fluctuation analysis of vibration signals for bearing fault detection  

Microsoft Academic Search

Rolling element bearings are widely used in various rotary machines. Accordingly, a reliable bearing fault detection technique is critically needed in industries to prevent these machines' performance degradation, malfunction, or even catastrophic failures. Although a number of approaches have been reported in the literature, bearing fault detection, however, still remains a very challenging task because most of the bearing fault

Jie Liu

2011-01-01

150

Study on Fault Diagnosis of Rolling Bearing Based on K-L Transformation and Lagrange Support Vector Regression  

Microsoft Academic Search

On the basis of vibration signal of rolling bearing, a new method of fault diagnosis based on K-L transformation and Lagrange support vector regression is presented.Multidimensional correlated variable is transformed into low dimensional independent eigenvector by the means of K-L transformation. The pattern recognition and nonlinear regression are achieved by the method of Lagrange support vector regression. Lagrange support vector

Yangwen Xu

2009-01-01

151

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

E-print Network

into the pattern recognition, the parameter identification, the fault verification, and the approximation techniques. Two basic methods of analog fault diagnosis are the simulation ? before ? test ( SBT) and the simulation ? after ? test ( SAT ) [2]. Fig. I... of only signal hard fault. The Fault diagnosis techni ues SBT techni ues SAT techni ues Fault dictionary techniques Probabilistic techniques Limited measure techniques Sufficient measure techniques Pattern recognition techniques...

You, Zhihong

2012-06-07

152

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

153

Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems  

Microsoft Academic Search

In this multi-university collaborative research, we will develop a framework for the dynamic data-driven fault diagnosis of\\u000a wind turbines which aims at making the wind energy a competitive alternative in the energy market. This new methodology is\\u000a fundamentally different from the current practice whose performance is limited due to the non-dynamic and non-robust nature\\u000a in the modeling approaches and in

Yu Ding; Eunshin Byon; Jiong Tang; Yi Lu; Xin Wang

2007-01-01

154

Fault Diagnosis of VLSI Circuits with Cellular Automata based Pattern Classifier  

E-print Network

Fault Diagnosis of VLSI Circuits with Cellular Automata based Pattern Classifier Biplab K Sikdar 1, India 700094 palchau@vsnl.net Abstract---This paper reports a fault diagnosis scheme for V LSI circuits dictionary used for diagnosis of V LSI circuits. The proposed diagnosis scheme employs significantly lesser

Ganguly, Niloy

155

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

E-print Network

their efectivenes. 1.1 The Rolling Element Bearing Fault Detection Problem Bearing faults are detected using either vibration signals or, les commonly, high frequency acoustic emisions. Figure 1 shows a simple diagram of how a mechanical fault, appears... their efectivenes. 1.1 The Rolling Element Bearing Fault Detection Problem Bearing faults are detected using either vibration signals or, les commonly, high frequency acoustic emisions. Figure 1 shows a simple diagram of how a mechanical fault, appears...

Weatherwax, Scott Eric

2009-05-15

156

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

157

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

158

Sensor Fault Diagnosis of Maglev Train Based on Kalman Filter Group  

Microsoft Academic Search

Sensor fault diagnosis of maglev train is studied based on Kalman filtering theory. Usually, a single Kalman filter of a control system can only detect faults, but can not locate fault parts. Therefore, Kalman filter group is introduced in. Further more, a single sensor fault will cause the system matrix of a close loop feedback control system to change which

Song Xue; Zhiqiang Long; Guang He; Ning He

2012-01-01

159

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

160

Fault diagnosis in turbine engines using unsupervised neural networks technique  

NASA Astrophysics Data System (ADS)

A fault diagnosis system based on the neural networks clustering technique is developed for a mid-sized jet propulsion engine. The currently recorded data set for this engine has several limitations in its quality, which results in the lack of information required for the incipient fault detection and wide coverage of failure modes. Using the residuals of core speed, exhausted gas temperature and fuel flow, the developed system is designed to diagnose the failures related to combustor liner, bleed band, and exhausted gas temperature (EGT) sensor rake. The fault diagnosis system reports not only the machine condition but also the belief factor convincing the diagnostic decisions. In this work the actual flight data collected in the field is used to develop and validate the system, and the results are shown for the test on five engines which had experienced three different failures. The presented system is implemented in the form of web-based service and has demonstrated its robustness by isolating the failures successfully in the field.

Kim, Kyusung; Ball, Charles; Nwadiogbu, Emmanuel

2004-04-01

161

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

162

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

PubMed

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

Fassois, Spilios D; Sakellariou, John S

2007-02-15

163

Neural Network Expert System in the Application of Tower Fault Diagnosis  

NASA Astrophysics Data System (ADS)

For the corresponding fuzzy relationship between the fault symptoms and the fault causes in the process of tower crane operation, this paper puts forward a kind of rapid new method of fast detection and diagnosis for common fault based on neural network expert system. This paper makes full use of expert system and neural network advantages, and briefly introduces the structure, function, algorithm and realization of the adopted system. Results show that the new algorithm is feasible and can achieve rapid faults diagnosis.

Liu, Xiaoyang; Xia, Zhongwu; Tao, Zhiyong; Zhao, Zhenlian

164

Lossy Electric Transmission Line Soft Fault Diagnosis: an Inverse Scattering Approach  

E-print Network

1 Lossy Electric Transmission Line Soft Fault Diagnosis: an Inverse Scattering Approach Huaibin diagnosis is the reflectometry, which consists in analyzing the reflection and the transmission of electric Tang and Qinghua Zhang Abstract--In this paper, the diagnosis of soft faults in lossy electric

Paris-Sud XI, Université de

165

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 is to present and evaluate the performance of a new procedure for industrial process diagnosis. This method [9], [10]. Finally, for the fault diagnosis techniques we can cite the book of Chiang, Russell

Paris-Sud XI, Université de

166

Fault Diagnosis in Discrete-Event Systems: How to Analyse Algorithm Performance?  

E-print Network

Fault Diagnosis in Discrete-Event Systems: How to Analyse Algorithm Performance? Yannick Pencol´e 1 the fault diagnosis problem in discrete-event systems in an experimental way. To achieve this pur- pose, we an experimental platform based on a tool called DIADES (Diagnosis of Discrete-Event Sys- tems) to run experiments

Pencolé, Yannick

167

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

168

Vibration-based fault detection of accelerometers in helicopters  

E-print Network

-elements that convert and transmit vibrations to the recording system must not corrupt the signal. These elements and Safety of Technical Processes, Mexico : Mexico (2012)" DOI : 10.3182/20120829-3-MX-2028.00049 #12;Fig. 2

Paris-Sud XI, Université de

169

Vibration-based fault detection of accelerometers in helicopters  

E-print Network

-elements that convert and transmit vibrations to the recording system must not corrupt the signal. These elements of accelerometers hal-00747268,version1-6Nov2012 Author manuscript, published in "SAFEPROCESS, Mexico City : France

Boyer, Edmond

170

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

NASA Astrophysics Data System (ADS)

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

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

2013-10-01

171

Diagnosis of Compressor Product's Malfunctions Based on Vibration Analysis  

NASA Astrophysics Data System (ADS)

Diagnosis of compressor product's malfunctions based on the vibration wave pattern data of the compressor and the FFT analysis data is developed. The feature of the analysis technique is two points. One is "Evaluating the ratio of the FFT integral calculus value of low frequency area and the high frequency area of the vibration wave pattern for the feature of abnormalitys", and the other is "Separating and evaluating the wave pattern region at the time of the start and the steady part as correspondence to the phenomenon that an abnormal tendency appears in the non-steady early start region". The feature of each abnormality is extracted. A diagnosis algorithm that distinguished normality and abnormality is developed. Moreover, the cause is distinguished about abnormality. The verification examination of the scroll compressor is done in the production line. And the effectiveness of this diagnosis algorithm is validated for both the alternating-current motor type scroll compressor and the direct-current motor type scroll compressor.

Toyoshima, Masaki; Yamashita, Koji; Obase, Shinichirou; Nobata, Toshihumi

172

Fault Diagnosis of Bearings Based on Time-Delayed Correlation and Demodulation as Well as B-Spline Fuzzy Neural Networks  

Microsoft Academic Search

\\u000a As one of the most common parts of various rolling mechanical equipments, rolling element bearing is vulnerable. Therefore,\\u000a great attentions have been attributed to the theories, Afailure diagnosis methods and their applications for rolling bearings.\\u000a Vibration analysis is also a very important means for condition monitoring and fault diagnosis. This paper aims at the research\\u000a on the methods of signal

Pan Fu; Li Jiang; A. D. Hope; Weiling Li

173

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

PubMed

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

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

2013-02-01

174

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

175

Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram  

NASA Astrophysics Data System (ADS)

The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is very important in the field of predictive maintenance. Till date, the resonant demodulation technique (envelope analysis) has been widely exploited in practice. However, much practical diagnostic equipment for carrying out the analysis gives little flexibility to change the analysis parameters for different working conditions, such as variation in rotating speed and different fault types. Because the signals from a flawed bearing have features of non-stationarity, wide frequency range and weak strength, it can be very difficult to choose the best analysis parameters for diagnosis. However, the kurtosis of the vibration signals of a bearing is different from normal to bad condition, and is robust in varying conditions. The fast kurtogram gives rough analysis parameters very efficiently, but filter centre frequency and bandwidth cannot be chosen entirely independently. Genetic algorithms have a strong ability for optimization, but are slow unless initial parameters are close to optimal. Therefore, the authors present a model and algorithm to design the parameters for optimal resonance demodulation using the combination of fast kurtogram for initial estimates, and a genetic algorithm for final optimization. The feasibility and the effectiveness of the proposed method are demonstrated by experiment and give better results than the classical method of arbitrarily choosing a resonance to demodulate. The method gives more flexibility in choosing optimal parameters than the fast kurtogram alone.

Zhang, Yongxiang; Randall, R. B.

2009-07-01

176

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

PubMed

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

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

2014-01-01

177

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.

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

2014-01-01

178

Fault classification on vibration data with wavelet based feature selection scheme  

Microsoft Academic Search

Fault classification based upon vibration data is an essential building block of a sophisticated conditional based monitoring (CBM) system. Multiple sensor channels are called for to assure the redundancy and to achieve the desired reliability and accuracy. The shortcoming of using multiple sensor input channels is the need to deal with high dimensional features set, a computational expensive task in

Gary G. Yen; Wen Fung Leong

2005-01-01

179

A Simple Real-Time Fault Signature Monitoring Tool for Motor-Drive-Embedded Fault Diagnosis Systems  

Microsoft Academic Search

The reference frame theory constitutes an essential aspect of electric machine analysis and control. In this study, apart from the conventional applications, it is reported that the reference frame theory approach can successfully be applied to real-time fault diagnosis of electric machinery systems as a powerful toolbox to find the magnitude and phase quantities of fault signatures with good precision

Bilal Akin; Seungdeog Choi; Umut Orguner; Hamid A. Toliyat

2011-01-01

180

Low-Cost Motor Drive-Embedded Fault Diagnosis - A Simple Harmonic Analyzer  

Microsoft Academic Search

The reference frame theory and its applications to fault diagnosis of electric machinery as a powerful tool to find the magnitude and phase quantities of fault signatures are explored in this paper. The core idea is to convert the associated fault signature to a dc quantity, followed by calculating the signal average value in the new reference frame to filter

Bilal Akin; Hamid A. Toliyat; Umut Orguner; Mark Rayner

2007-01-01

181

Discussion on: "Sensor Gain Fault Diagnosis for a Class of Nonlinear Systems"  

E-print Network

transformation In this section, it is assumed that the conditionnally identifiable sensor gain faults, i is the Lipschitz constant. First, we propose an output transformation to isolate the free fault component y1Discussion on: "Sensor Gain Fault Diagnosis for a Class of Nonlinear Systems" H. Rafaralahy , M

Boyer, Edmond

182

The fault diagnosis method of rolling bearing based on wavelet packet transform and zooming envelope analysis  

Microsoft Academic Search

The fault of rolling bearing is one of familiar faults in rotaries. In accordance with the defects of traditional envelope analysis to specify the resonant frequency band manually, a new fault diagnosis method based on wavelet packet transform and zooming envelope analysis is proposed. Firstly, the modulated frequency of resonant frequency band is extracted for rolling bearing, and the original

Shu-Ting Wan; Lu-Yong Lv

2007-01-01

183

Application of Wigner-Ville distribution and probability neural network for scooter engine fault diagnosis  

Microsoft Academic Search

An expert system for internal combustion engine fault diagnosis using Wigner–Ville distribution for feature extraction and probability neural network for fault classification is described in this paper. Most of the conventional techniques for fault signal analysis in a mechanical system are based chiefly on the difference of signal amplitude in the time and frequency domains. Unfortunately, in some conditions the

Jian-da Wu; Peng-hsin Chiang

2009-01-01

184

Improving Fault-Tolerance in Intelligent Video Surveillance by Monitoring, Diagnosis and Dynamic Reconfiguration  

Microsoft Academic Search

ó In this paper, we present an approach for improving fault-tolerance and service availability in intelligent video surveillance (IVS) systems. A typical IVS system consists of various intelligent video sensors that combine image sensing with video analysis and network streaming. System monitoring and fault diagnosis fol- lowed by appropriate dynamic system recongur ation mitigate effects of faults and therefore enhance

Andreas Doblander; Arnold Maier; Bernhard Rinner; Helmut Schwabach

2005-01-01

185

A Fault Diagnosis Method of Rolling Bearings Using Empirical Mode Decomposition and Hidden Markov Model  

Microsoft Academic Search

This paper describes a new approach to detect localized rolling bearing defects based on empirical mode decomposition (EMD) and hidden Markov model (HMM). In view of the non-stationary characteristics of bearing fault vibration signals, using EMD method, the original non-stationary vibration signal can be decomposed into a finite number of stationary signals. The stationary signal adapts itself better to the

Bin Wu; Changjian Feng; Minjie Wang

2006-01-01

186

Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition  

Microsoft Academic Search

Due to the importance of rolling bearings as the most widely used machine elements, it is necessary to establish a suitable condition monitoring procedure to prevent malfunctions and breakages during operation. This paper presents a new method for detecting localized bearing defects based on wavelet transform. Bearing race faults have been detected by using discrete wavelet transform (DWT). Vibration signals

V. Purushotham; S. Narayanan; Suryanarayana A. N. Prasad

2005-01-01

187

Induction motor fault diagnosis based on neuropredictors and wavelet signal processing  

Microsoft Academic Search

Early detection and diagnosis of incipient faults is desirable for online condition assessment, product quality assurance and improved operational efficiency of induction motors running off power supply mains. In this paper, a model-based fault diagnosis system is developed for induction motors, using recurrent dynamic neural networks for transient response prediction and multi-resolution signal processing for nonstationary signal feature extraction. In

Kyusung Kim; Alexander G. Parlos

2002-01-01

188

The application of wavelet packet and SVM in rolling bearing fault diagnosis  

Microsoft Academic Search

The method of fault diagnosis of rolling bearings based on wavelet packet transform and support vector machine is presented. The key to fault bearings diagnosis is feature extracting and feature classifying. Wavelet packet transform, as a new technique of signal processing, possesses excellent characteristic of time-frequency localization and is suitable for analyzing the time-varying or transient signals. Support vector machine

Meng Li; Ping Zhao

2008-01-01

189

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

190

Fault diagnosis of the steam turbine condenser system based on SOM neural network  

Microsoft Academic Search

The condenser system is one of the most important and complicated steam turbine thermodynamic systems. The SOM (self-organizing map) neural network is applied to fault diagnosis of the system, which is implemented by the neural network toolbox in MATLAB. The method for fault diagnosis of the condenser system is effective and it has been verified by simulation results.

Nian-Su Hu; Na-Na He; Sheng Hu

2003-01-01

191

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

E-print Network

Fuzzy-logic based trend classification for fault diagnosis of chemical processes Sourabh Dash, fault diagnosis based on patterns exhibited in the sensors measuring the process variables is considered of the features leads to imprecise classification boundaries at the trend-identification stage and hence

Koppelman, David M.

192

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

193

Identification of critical molecules via fault diagnosis engineering.  

PubMed

Systems biology envisions that the application of complex system engineering approaches to cell signaling molecular networks can lead to novel understandings of complex human disorders. In this paper we show that by developing biologically-driven vulnerability assessment methods, the vulnerability of complex signaling networks to the dysfunction of each molecule can be determined. We have analyzed signaling networks that regulate mitosis and the activity of the transcription factor CREB. Our results indicate that biologically-relevant critical components of intracellular molecular networks can be identified using the proposed systems biology/fault diagnosis engineering technique. The application of this approach can improve our physiological understanding of the functionality of biological systems, can be used as a tool to identify novel genes associated with complex human disorders, and ultimately, has the potential to find the most prominent targets for drug discovery. PMID:19963868

Abdi, Ali; Tahoori, Mehdi B; Emamian, Effat S

2009-01-01

194

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

195

Research on Fault Detection and Diagnosis of Scrolling Chiller with ANN  

E-print Network

ICEBO2006, Shenzhen, China Co ntrol Systems for Energy Efficiency and Comfort, Vol. V-5-3 Research on Fault Detection and Diagnosis of Scrolling Chiller with ANN1 Yuli ZHOU Jie ZHENG Zhiju LIU Chaojie YANG Peng PENG... ZHENG, Yuli ZHOU.HVAC The Analysis Of Fault Characteristics And The Establishment Of Diagnosis System Journal of Guizhou Industry University 2003.Vol.32.add .175-178 [4] Srinivas Katipamula, PhD Michael R. Brambley, PhD Methods for Fault...

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

2006-01-01

196

Detection and diagnosis of changes in the time-scale eigenstructure for vibrating systems  

Microsoft Academic Search

Focuses on techniques used in monitoring machine condition and diagnosing mechanical faults in vibrating mechanical equipment which has varied operational modes and whose dynamic signals are nonstationary. Because of the nonstationary nature of the vibrating system, one has to apply the time-scale transform to capture the nonstationary modes. The autoregressive moving average modeling captures the modal signatures, in particular, the

Ahmed Hambaba; E. Huff; U. Kaul

2001-01-01

197

Application of data fusion method to fault diagnosis of nuclear power plant  

NASA Astrophysics Data System (ADS)

The work condition of nuclear power plant (NPP) is very bad, which makes it has faults easily. In order to diagnose the faults real time, the fusion diagnosis system is built. The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosis into three levels, which are data fusion level, feature level and decision level. The feature level uses three parallel neural networks whose structures are the same. The purpose of using neural networks is mainly to get basic probability assignment (BPA) of D-S evidence theory, and the neural networks in feature level are used for local diagnosis. D-S evidence theory is adopted to integrate the local diagnosis results in decision level. The reactor coolant system is the study object and we choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example. The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirement of complicated system, and verify that the fusion fault diagnosis system can realize the fault diagnosis of NPP on line timely.

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

2005-03-01

198

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

PubMed

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

Li, Chaoshun; Zhou, Jianzhong

2014-09-01

199

Self-Organizing Map-Based Fault Dictionary Application Research on Rolling Bearing Faults  

Microsoft Academic Search

Vibration signal resulting from rolling bearing defects presents a rich content of physical information, the appropriate analysis methods of which can lead to the clear identification of the nature of the fault. A novel procedure is presented for construction of fault diagnosis dictionary through self-organization map (SOM). The experiments show that the bearing faults diagnosis dictionary could be effectively applied

Jun Pi; Jiaquan Lin; Xiangjiang Li

2008-01-01

200

Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals  

Microsoft Academic Search

Generalized roughness is the most common damage occurring to rolling bearings. It produces a frequency spreading of the characteristic fault frequencies, thus making it difficult to detect with spectral or envelope analysis. A statistical analysis of typical bearing faults is proposed here in order to identify the spreading bandwidth related to specific conditions, relying on current or vibration measurements only.

Fabio Immovilli; Marco Cocconcelli; Alberto Bellini; Riccardo Rubini

2009-01-01

201

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

NASA Astrophysics Data System (ADS)

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

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

2007-12-01

202

SINGULARITY ANALYSIS USING CONTINUOUS WAVELET TRANSFORM FOR BEARING FAULT DIAGNOSIS  

Microsoft Academic Search

In this paper, wavelet transform is applied to detect abrupt changes in the vibration signals obtained from operating bearings being monitored. In particular, singularity analysis across all scales of the continuous wavelet transform is performed to identify the location (in time) of defect-induced bursts in the vibration signals. Through modifying the intensity of the wavelet transform modulus maxima, defect-related vibration

Q. Sun; Y. Tang

2002-01-01

203

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

204

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

205

Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network  

Microsoft Academic Search

In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems\\u000a with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown\\u000a but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on

Mou Chen; Chang-Sheng Jiang; Qing-Xian Wu

2008-01-01

206

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

Microsoft Academic Search

The vibration data, especially those collected during the system run-up and run-down periods, contain rich information for gearbox condition monitoring. Time-frequency (TF) signal analysis is an effective tool to detect gearbox faults under varying shaft speed. However, the feature of the amplitude modulated-frequency modulated (AM-FM) gearbox fault signal usually cannot be directly extracted from the blurred time-frequency representation (TFR) caused

Chuan Li; Ming Liang

2012-01-01

207

Computer diagnosis systems grounded on hand-crafted fault trees are wide-spread in industrial practice. Since the  

E-print Network

Abstract Computer diagnosis systems grounded on hand-crafted fault trees are wide diagnosis equip- ment can be drastically reduced and quality management of diagnosis equipment can 1100 STILL service workshop trucks utilize fault tree- based computer diagnosis systems for workshop

Hamburg,.Universität

208

Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks  

E-print Network

Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification University of Texas Medical Branch, Galveston Abstract The problem of active diagnosis arises in sev- eral applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly

Bhavnani, Suresh K.

209

Transients Analysis of a Nuclear Power Plant Component for Fault Diagnosis  

E-print Network

Transients Analysis of a Nuclear Power Plant Component for Fault Diagnosis Piero Baraldia.baraldi@polimi.it, We analyze signal data collected during 148 shut-down transients of a nuclear power plant (NPP

Paris-Sud XI, Université de

210

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

E-print Network

Condition monitoring of electric machinery has received increased attention due to the advantages it offers in terms of productivity. Automation of the fault detection and diagnosis processes would not only allow for extensive monitoring but also...

Alladi, Vijaya Mallikarjun

2012-06-07

211

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

SciTech Connect

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.

Zhao Jinsong [College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029 (China)], E-mail: jinsongzhao@mail.tsinghua.edu.cn; Huang Jianchao [College of Information Science and Technology, Beijing Institute of Technology, Beijing 10086 (China); Sun Wei [College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029 (China)

2008-11-15

212

Applied Study of Electromotor Fault Diagnosis Based on Wavelet Packets and Neural Network  

Microsoft Academic Search

This paper presents a novelty electromotor fault diagnosis method based on wavelet packets and BP neural network. Wavelet packets transform can decompose original signal into different spectrums, the corresponding energy eigenvector can be obtained, it expresses the energy characteristics of original signal; BP (back propagation) neural network is an effective tool to recognize fault types, neural network can be trained

Bei-Ping Hou; Wen Zhu; Xin-Jian Xiang; Xing-Yao Shang

2006-01-01

213

Computational Analysis of Sparse Datasets for Fault Diagnosis in Large Tribological Mechanisms  

Microsoft Academic Search

This paper presents the most up-to-date methods for the task of designing a system to accurately classify abnormal events, or faults, in a complex tribological mechanism, using el- emental analysis of lubrication oil as an indicator of engine con- dition. The discussion combines perspectives from numerous fault diagnosis applications, both online and offline, to focus upon the task of offline

Ian Morgan; Honghai Liu

2011-01-01

214

HVAC Fault Diagnosis System Using Rough Set Theory and Support Vector Machine  

Microsoft Academic Search

Preventive maintenance plays a very important role in the modern Heating, Ventilation and Air Conditioning (HVAC) systems for guaranteeing the thermal comfort, energy saving and reliability. The fault diagnosis on HVAC system is a difficult problem due to the complex structure of the HVAC and the presence of multi-excite sources. As the HVAC system fault information has inaccurate and uncertainty

Xuemei Li; Ming Shao; Lixing Ding

2009-01-01

215

A Smart Algorithm for the Diagnosis of Short-Circuit Faults in a Photovoltaic Generator  

E-print Network

A Smart Algorithm for the Diagnosis of Short-Circuit Faults in a Photovoltaic Generator Wail Rezgui observations distributed over classes is used for simulation purposes. Keywords--Photovoltaic generator, SVM, k-NN, short-circuit fault, smart classification, linear programming. NOMENCLATURE PV = Photovoltaic; SVM

Paris-Sud XI, Université de

216

Wavelet co-efficient of thermal image analysis for machine fault diagnosis  

Microsoft Academic Search

The ultimate goal of this study is to introduce a new method of machine fault diagnosis using different machine conditions data such as normal, misalignment, mass-unbalance and bearing-fault from infrared thermography (IRT). Using thermal image, it is easy to obtain information about the machine condition rather than other conventional methods of machine condition diagnostic technique. Thermal image technique can be

A. M. Younus; Bo-Suk Yang

2010-01-01

217

Neural networks-based scheme for fault diagnosis in fossil electric power plants  

Microsoft Academic Search

This paper presents the development and application of a neural networks-based scheme for fault diagnosis in fossil electric power plants. The scheme is constituted by two components: residuals generation and fault classification. The first component generates residuals via the difference between measurements coming from the plant and a neural network predictor. The neural network predictor is trained with healthy data

Jose A. Ruz-Hernandez; E. N. Sanchez; D. A. Suarez

2005-01-01

218

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 of maintenance in offshore environment, teledetection of wind turbine faults is becoming a crucial issue

Paris-Sud XI, Université de

219

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

220

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

221

Fault Diagnosis of an Air-Handling Unit System Using a Dynamic Fuzzy-Neural Approach  

Microsoft Academic Search

\\u000a This paper presents a diagnostic tool to be used to assist building automation systems for sensor heath monitoring and fault\\u000a diagnosis of an Air-Handling Unit (AHU). The tool employs fault detection and diagnosis (FDD) strategy based on an Efficient\\u000a Adaptive Fuzzy Neural Network (EAFNN) method. EAFNN is a Takagi-Sugeno-Kang (TSK) type fuzzy model which is functionally equivalent\\u000a to the Ellipsoidal

Juan Du; Meng Joo Er; Leszek Rutkowski

2010-01-01

222

Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger  

Microsoft Academic Search

This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical

Hacene Habbi; Madjid Kidouche; Michel Kinnaert; Mimoun Zelmat

2011-01-01

223

Diagnosis of mechanical faults by spectral kurtosis energy  

Microsoft Academic Search

Generalized roughness is the most common damage occurring to roller bearing. It produces a frequency spreading of the characteristics fault frequencies, thus being difficult to detect with spectral or envelope analysis. A statistical analysis of typical bearing faults is here proposed in order to identify the spreading bandwidth related to a specific conditions, relying on current measurements only. Then a

Alberto Bellini; Marco Cocconcelli; Fabio Immovilli; Riccardo Rubini

2008-01-01

224

Proceedings of the 2008 International Conference on Electrical Machines Paper ID 1434 DFIG-Based Wind Turbine Fault Diagnosis  

E-print Network

-Based Wind Turbine Fault Diagnosis Using a Specific Discrete Wavelet Transform E. Al-Ahmar1,2 , M for electrical and mechanical fault diagnosis in a DFIG-based wind turbine. The investigated technique unambiguously diagnose faults under transient conditions. Index Terms--Wind turbine, Doubly-Fed Induction

Boyer, Edmond

225

A Blind Deconvolution Technique for Machine Fault Diagnosis  

Microsoft Academic Search

Blind source separation (BSS) is a powerful signal processing technique, based on the advantage of data mining and handing. The observed signal containing too much redundancy information, the application of BSS in machine vibration signals is a new method for feature extracting. The traditional BSS model usually neglect that machine vibration signal always is wideband signal for it can be

Liu Tingting; Ren Xingmin

2009-01-01

226

Fault diagnosis of gearbox based on matching pursuit  

Microsoft Academic Search

Matching pursuit is effective in matching the characteristic structure of signals and extracting the time-frequency features directly. It is employed to analyze the vibration signals of a gearbox under healthy and faulty statuses. Based on a compound dictionary, the periodic impulses characterizing the vibration of localized damaged gears are extracted in joint time-frequency domain, and the localized gear damage is

Zhi-Peng Feng; Jin Zhang; Ru-Jiang Hao; MING J. ZUO; Fu-Lei Chu

2010-01-01

227

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

228

ROLLING ELEMENT BEARING FAULT DIAGNOSIS IN ROTATING MACHINES OF OIL EXTRACTION RIGS  

Microsoft Academic Search

This paper presents vibration analysis techniques for fault detection in rotating machines. Rolling element bearing defects inside a motor pump are the subject of study. Signal processing techniques, like frequency filters, Hilbert tra ns- form, and spectral analysis are used to extract features use d later as a base to classify the condition of machines. Also, pattern recognition techniques are

E. Mendel; T. W. Rauber; F. M. Varej; R. J. Batista

2009-01-01

229

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

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

230

The reliability of general vague fault-tree analysis on weapon systems fault diagnosis  

Microsoft Academic Search

An algorithm of vague fault-tree analysis is proposed in this paper to calculate fault interval of system components from\\u000a integrating expert's knowledge and experience in terms of providing the possibility of failure of bottom events. We also modify\\u000a Tanaka et al's definition and extend the new usage on vague fault-tree analysis in terms of finding most important basic system\\u000a component

J.-R. Chang; K.-H. Chang; S.-H. Liao; C.-H. Cheng

2006-01-01

231

Singularity analysis of the vibration signals by means of wavelet modulus maximal method  

Microsoft Academic Search

Machine fault diagnosis is vital for safe services and non-interrupted production. The key issue in fault diagnosis is the pattern recognition. A set of valid features will simplify the classifying operations and enhance the accuracy in diagnosis. In this paper, a novel singularity based fault features is presented. Vibration signals collected under different machine health conditions will show different patterns

Z. K. Peng; F. L. Chu; Peter W. Tse

2007-01-01

232

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

233

Developing a new transformer fault diagnosis system through evolutionary fuzzy logic  

Microsoft Academic Search

To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC\\/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and

Yann-Chang Huang; C. L. Huang; H. T. Yang

1997-01-01

234

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

Microsoft Academic Search

The accuracy of the diagnosis obtained from a nuclear power plant fault-diagnostic advisor using neural networks is addressed in this paper in order to ensure the credibility of the diagnosis. A new error estimation scheme called error estimation by series association provides a measure of the accuracy associated with the advisor's diagnoses. This error estimation is performed by a secondary

Keehoon Kim; Eric B. Bartlett

1996-01-01

235

An investigation of MML methods for fault diagnosis in mobile robots  

Microsoft Academic Search

The purpose of this study is to evaluate the utility of a diagnosis technique, which uses minimum message length (MML) for autonomous mobile robot fault diagnosis. A simulator was developed for a behavior-based robotic system and results were gathered for over 24,000 simulations varying the level of test noise and the components with simulated failures. The results showed that the

Jennifer Carlson; Robin R. Murphy

2004-01-01

236

A Class of Nonlinear Unknown Input Observer for Fault Diagnosis: Application to Fault Tolerant  

E-print Network

) mission. The goal of the mission is to return samples from Mars to the Earth for analysis (see [3 and accommodate thruster faults of an autonomous spacecraft involved in the rendezvous phase of the Mars Sample Return (MSR) mission. Considered fault scenarios represent fully closed thruster and thruster efficiency

Boyer, Edmond

237

Neural network approach to fault diagnosis in CMOS opamps with gate oxide short faults  

NASA Astrophysics Data System (ADS)

Faults owing to gate oxide shorts in a CMOS opamp have been diagnosed in simulations using artificial neural networks to identify corresponding variations in supply current. Ramp and sinusoidal signals gave fault diagnostic accuracy of 67 and 83 percent, respectively. Using both test signals 100 percent diagnostic accuracy was achieved.

Yu, S.; Jervis, B. W.; Eckersall, K. R.; Bell, I. M.; Hall, A. G.; Taylor, G. E.

1994-04-01

238

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. Although discrete- event diagnosis methods are used extensively, they do not easily apply to parametric

Koutsoukos, Xenofon D.

239

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

240

Study and application of acoustic emission testing in fault diagnosis of low-speed heavy-duty gears.  

PubMed

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

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

2011-01-01

241

Feature Group Optimization for Machinery Fault Diagnosis Based on Fuzzy Measures  

Microsoft Academic Search

With the development of modern multi-sensor based data acquisition technology often used with advanced signal processing techniques,\\u000a more and more features are being extracted for the purposes of fault diagnostics and prognostics of machinery integrity. Applying\\u000a multiple features can enhance the condition monitoring capability and improve the fault diagnosis accuracy. However, an excessive\\u000a number of features also increases the complexity

Xiaofeng Liu; Lin Ma; Sheng Zhang; Joseph Mathew

242

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

243

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

244

Fault Detection and Diagnosis of 3Phase Inverter System  

Microsoft Academic Search

This paper describes a method of detection and identification of transistor base drive open-circuit fault of 3-phase voltage source inverter (VSI), feeding a fuzzy logic controlled induction motor. The detection mechanism is based on a novel technique of wavelet transform. In this method, the stator currents will be used as an input to the system. No direct access to the

M. S. Khanniche; M. R. Mamat-Ibrahim

2001-01-01

245

Transformer Fault Diagnosis by Dissolved-Gas Analysis  

Microsoft Academic Search

The great majority of incipient faults occurring in power transformers gives evidence of their presence early in their developmental stages. Oil and oil-impregnated electrical insulating materials can decompose under the influence of thermal and electrical stresses generating gaseous decomposition products which dissolve in the mineral oil. The nature and the amount of the individual component gases extracted from the oil

Joseph J. Kelly

1980-01-01

246

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

247

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

248

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

249

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

250

Automated function generation of symptom parameters and application to fault diagnosis of machinery under variable operating conditions  

Microsoft Academic Search

Dimensional or nondimensional symptom parameters are usually used for condition monitoring of plant machinery. However, it is difficult to extract the most important symptom parameters and the functions of those parameters by which machinery faults can be sensitively detected and the fault types can be precisely distinguished. In order to overcome this difficulty and to ensure highly accurate fault diagnosis,

Peng Chen; Toshio Toyota; Zhengja He

2001-01-01

251

Fault Diagnosis and Control-reconfiguration in Large-scale Systems: a Plug-and-Play Approach  

E-print Network

of fault-tolerance (see the seminal paper [?]). Model-based schemes have emerged as prominent approachesFault Diagnosis and Control-reconfiguration in Large-scale Systems: a Plug-and-Play Approach with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use

Ferrari-Trecate, Giancarlo

252

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

253

Power Spectral Density Technique for Fault Diagnosis of an Electromotor  

Microsoft Academic Search

\\u000a Developing a special method for maintenance of electrical equipments of industrial company is necessary for improving maintenance\\u000a quality and reducing operating costs. The combination of corrective preventative and condition based maintenance will require\\u000a applying for critical equipments of industrial company. This type of maintenance policy and strategy will improve performance\\u000a of this equipment through availability of industrial equipment. Many vibration

Hojjat Ahmadi; Zeinab Khaksar

254

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

255

EEMD-1.5 Dimension Spectrum Applied to Locomotive Gear Fault Diagnosis  

Microsoft Academic Search

The criterion of adding white noise in Ensemble Empirical Mode Decomposition (EEMD) method is established. EEMD, used for avoiding mode mixing in signal decomposition, is combined with 1.5 dimension spectrum, which is the bispectrum diagonal slice to eliminate white noise and extract nonlinear coupling feature. A new method of EEMD-1.5 dimension spectrum for fault feature extraction is proposed. Firstly, vibration

Lue Chen; Yanyang Zi; Wei Cheng; Zhengjia He

2009-01-01

256

Fault detection based on fractional order models: Application to diagnosis of thermal systems  

NASA Astrophysics Data System (ADS)

The aim of this paper is to propose diagnosis methods based on fractional order models and to validate their efficiency to detect faults occurring in thermal systems. Indeed, it is first shown that fractional operator allows to derive in a straightforward way fractional models for thermal phenomena. In order to apply classical diagnosis methods, such models could be approximated by integer order models, but at the expense of much higher involved parameters and reduced precision. Thus, two diagnosis methods initially developed for integer order models are here extended to handle fractional order models. The first one is the generalized dynamic parity space method and the second one is the Luenberger diagnosis observer. Proposed methods are then applied to a single-input multi-output thermal testing bench and demonstrate the methods efficiency for detecting faults affecting thermal systems.

Aribi, Asma; Farges, Christophe; Aoun, Mohamed; Melchior, Pierre; Najar, Slaheddine; Abdelkrim, Mohamed Naceur

2014-10-01

257

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

SciTech Connect

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

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

2004-01-01

258

Signal-based versus model-based fault diagnosis-a trade-off in complexity and performance  

Microsoft Academic Search

Early detection and diagnosis of incipient faults is desirable not only for on-line condition assessment but also for product quality assurance and improved operational efficiency of induction motors. At the same time, reducing the probability of false alarms increases the confidence levels of equipment owners in this new technology. In this paper, a model-based fault detection and diagnosis system that

Parasuram P. Harihara; Kyusung Kim; Alexander G. Parlos

2003-01-01

259

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

260

Sequential diagnosis for rolling bearing using fuzzy neural network  

Microsoft Academic Search

In the case of fault diagnosis of the plant machinery, knowledge for distinguishing faults is ambiguous because definite relationships between symptoms and fault types cannot be easily identified. So this paper presents a sequential diagnosis method for rolling bearing by a fuzzy neural network with the features of a vibration signal in time domain. The fuzzy neural network is realized

Huaqing Wang; Peng Chen

2008-01-01

261

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

262

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

263

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

264

Detection and Diagnosis of Recurrent Faults in Software Systems by Invariant Analysis  

Microsoft Academic Search

A correctly functioning enterprise-software system exhibits long-term, stable correlations between many of its monitoring metrics. Some of these correlations no longer hold when there is an error in the system, potentially enabling error detection and fault diagnosis. However, existing approaches are inefficient, requiring a large number of metrics to be monitored and ignoring the relative discriminative properties of different metric

Miao Jiang; Mohammad Ahmad Munawar; Thomas Reidemeister; Paul A. S. Ward

2008-01-01

265

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

E-print Network

model is applied to rank the suspects so that the most suspicious one will be ranked at or near the top. Several fast filtering methods are used to prune unrelated suspects. Finally, to refine the diagnosis, fault simulation is performed on the top...

Wu, Lei

2009-05-15

266

Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization  

Microsoft Academic Search

This paper presents a fault diagnosis system for airplane engines using Bayesian networks (BN) and distributed particle swarm optimization (PSO). The PSO is inherently parallel, works for large domains and does not trap into local maxima. We implemented the algorithm on a computer cluster with 48 processors using message passing interface (MPI) in Linux. Our implementation has the advantages of

Ferat Sahin; M. Çetin Yavuz; Ziya Arnavut; Önder Uluyol

2007-01-01

267

Wind turbine condition monitoring and fault diagnosis using both mechanical and electrical signatures  

Microsoft Academic Search

Some large wind turbines use a synchronous generator, directly-coupled to the turbine, and a fully rated converter to transform power from the turbine to the mains. This paper considers condition monitoring and diagnosis of mechanical and electrical faults in such a variable speed machine. A new condition monitoring technique is proposed in this paper, which removes the negative influence of

Wenxian Yang; P. J. Tavner; Michael Wilkinson

2008-01-01

268

A Router for Improved Fault Isolation, Scalability and Diagnosis in CAN  

E-print Network

1 A Router for Improved Fault Isolation, Scalability and Diagnosis in CAN R. Obermaisser, R. We introduce a CAN router that detects and isolates node failures in the value and time domain. The CAN router ensures that minimum message interarrival times are satisfied and reserves CAN identifiers

269

An expert system for transformer fault diagnosis using dissolved gas analysis  

Microsoft Academic Search

A prototype of an expert system based on the dissolved gas analysis (DGA) technique for diagnosis of a suspected transformer faults and their maintenance actions is developed. The synthetic method is proposed to assist the popular gas ratio method. Moreover, the uncertainty of key gas analysis, norms threshold and gas ratio boundaries are managed by using a fuzzy set concept.

C. E. Lin; J.-M. Ling; C.-L. Huang

1993-01-01

270

Automated Diagnosis for Fault Detection, Identification and Recovery in Autosub 6000  

E-print Network

. Livingstone is a constraint-based diagnosis system developed at NASA Ames Research Cen- tre specifically it to be computationally efficient and makes modelling relatively easy, but restricts the systems to which it can be explained as the effects of ocean currents on the vehicle. For faults of this type the discrete approach

Yao, Xin

271

Bearings Fault Detection and Diagnosis Using Envelope Spectrum of Laplace Wavelet Transform  

Microsoft Academic Search

Envelope spectrum analysis is widely used to detection bearing localized fault. In order to overcome the shortcomings in the traditional envelope analysis in which manually specifying a resonant frequency band is required, a new approach based on the fusion of the Laplace wavelet transform and envelope spectrum is proposed for detection and diagnosis defects in rolling element bearings. This approach

Hui Li; Lihui Fu; Haiqi Zheng

2009-01-01

272

Modelling of gearbox dynamics under time-varying nonstationary load for distributed fault detection and diagnosis  

Microsoft Academic Search

Fault detection and diagnosis in mechanical systems during their time-varying nonstationary operation is one of the most challenging issues. In the last two decades or so researches have noticed that machines work in nonstationary load\\/speed conditions during their normal operation. Diagnostic features for gearboxes were found to be load dependent. This was experimentally confirmed by a smearing effect in the

Walter Bartelmus; Fakher Chaari; Radoslaw Zimroz; Mohamed Haddar

2010-01-01

273

Health monitoring, fault diagnosis and failure prognosis techniques for Brushless Permanent Magnet Machines  

Microsoft Academic Search

Over the past few years, many researchers have been attracted by the challenges of electrical machines' fault diagnosis and condition monitoring, which provide early warnings that could help schedule necessary maintenance to avoid catastrophic consequence. With advancements in the use of rare-earth magnets, Brushless Permanent Magnet Machines are widely used in industry recently, which has led to the development of

Yao Da; Xiaodong Shi; Mahesh Krishnamurthy

2011-01-01

274

An approach for bearing fault diagnosis based on PCA and multiple classifier fusion  

Microsoft Academic Search

The purpose of this paper is to propose a new system, with both high efficiency and accuracy for fault diagnosis of rolling bearing. After pretreatment and choosing sensitive features of different working conditions of bearing from both time and frequency domain, principal component analysis(PCA) is conducted to compress the data dimension and eliminate the correlation among different statistical features. The

Min Xia; Fanrang Kong; Fei Hu

2011-01-01

275

Dynamic structure identification of Bayesian network model for fault diagnosis of FMS  

E-print Network

, 38402 ST Martin D'Heres Cedex, France {Dang-Trinh.Nguyen, Quoc-Bao.Duong, Eric.Zamai, Muhammad to identify the model for fault diagnosis with corresponding variables. The artificial intelligence methods corrective maintenance. However, due to the complexity of present-day manufacturing systems, this graph- ical

276

Computer-based monitoring and fault diagnosis: a chemical process case study  

Microsoft Academic Search

Principal component analysis (PCA) for process modeling and multivariate statistical techniques for monitoring, fault detection, and diagnosis are becoming more common in published research, but are still underutilized in practice. This paper summarizes an in-depth case study on a chemical process with 20 monitored process variables, one of which reflects product quality. Data from intervals of “good” operation times are

Patricia Ralston; Gail DePuy; James H. Graham

2001-01-01

277

Improving Model-Based Gas Turbine Fault Diagnosis Using Multi-Operating Point Method  

Microsoft Academic Search

A comprehensive gas turbine fault diagnosis system has been designed using a full nonlinear simulator developed in Turbotec company for the V94.2 industrial gas turbine manufactured by Siemens AG. The methods used for detection and isolation of faulty components are gas path analysis (GPA) and extended Kalman filter (EKF). In this paper, the main health parameter degradations namely efficiency and

Amin Salar; SeyedMehrdad Hosseini; Ali Khaki Sedigh; Behnam Rezaei Zangmolk

2010-01-01

278

Model Based Building Chilled Water Loop Delta-T Fault Diagnosis  

E-print Network

distribution loop by the building's chilled water system under various loading conditions. The results show model-based building chilled water Loop delta-T fault diagnosis is an effective way to evaluate existing building chilled water loop delta-T performance...

Wang, L.; Watt, J.; Zhao, J.

2012-01-01

279

Wind Turbines Condition Monitoring and Fault Diagnosis Using Generator Current Amplitude  

E-print Network

Wind Turbines Condition Monitoring and Fault Diagnosis Using Generator Current Amplitude in the research of renewable energy sources. In order to make wind turbines as competitive as the classical detection in a Doubly-Fed Induction Generator (DFIG) based wind turbine for stationary and nonstationary

Paris-Sud XI, Université de

280

Synchronous Machine Faults Detection and Diagnosis for Electro-mechanical Actuators  

E-print Network

Synchronous Machine Faults Detection and Diagnosis for Electro-mechanical Actuators in Aeronautics not require additional material or sensors since they are based on the signals already monitored that becomes more and more popular in aeronautics, and on a 9-slots 8-poles PMSM used in critical application

Boyer, Edmond

281

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

282

Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems: A Review  

E-print Network

for the reduction of operational and maintenance costs of Wind Energy Conversion Systems (WECS). The most efficient. Index Terms--Wind turbine, induction generator, drive train, condition monitoring, fault diagnosis. I turbines have been developed in size from 20 kW to 2 MW, while even larger wind turbines already are being

Paris-Sud XI, Université de

283

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

E-print Network

-inverter drive (MLID) system using a mathematical model because MLID systems consist of many switching devices such as photovoltaic, wind, and fuel cells can be easily interfaced to the multilevel inverter system for a high powerFault Diagnosis System for a Multilevel Inverter Using a Neural Network Surin Khomfoi Leon M

Tolbert, Leon M.

284

Civil aircraft maintenance and support Fault diagnosis from a business perspective  

Microsoft Academic Search

Aircraft maintenance down times together with maintenance activity durations and associated man-hour expenditure are extremely important factors contributing to two major yardsticks of airline and civil aircraft performance: despatch reliability and direct maintenance costs, and have important cost implications. In maintaining an aircraft there is a need to predict fault diagnosis activities in quantitative terms of time. Traditionally estimating maintenance,

Robert M. H. Knotts

1999-01-01

285

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

NASA Technical Reports Server (NTRS)

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

Meyer, J. F.

1974-01-01

286

Decision Tree Support Vector Machine based on Genetic Algorithm for fault diagnosis  

Microsoft Academic Search

Decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dichotomy, is proposed to solve the multi-class fault diagnosis tasks. Since the classification performance of DTSVM is closely related to its structure, genetic algorithm is introduced into the formation of decision tree, to cluster the multi-classes with maximum distance between the clustering centers of

Qiang Wang; Huanhuan Chen; Yi Shen

2008-01-01

287

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.

288

Model-based Diagnosis Extensions (Fault Models, Algorithms, . . . )  

E-print Network

: ¢¡¤£¥£§¦©¨ ! ¡" #$ %'&)(0£1¡'23¦¤!4 657(8 #9 6 ¡@ #9$ %'&)(0£1¡'23¦¤!4A (CBD2@E"£© F (GBD2@E3£©IH (GBD2@E3£©9$ P Note&)(0£1¡¤23¦'!RS$ %'&)(0£1¡¤23¦'UWV"cHd%'&)(C£e¡'23¦¤!UYX7Hd%'&f(0£e¡'23¦'UYa7 Observations: (CBD2@E"£©UWV3cH (GBD2@E3£©UYXcH (CBD2@E"£g!UYa7 2 #12;Fault Models - Motivation (cont.) Conflicts

289

An expert system for fault detection and diagnosis  

E-print Network

Major Subject: Electrical Engineering AN EXPERT SYSTEM FOR I AIJLT DETECTION AND DIAGNOSIS A Thesis by PREDRAG SPASOJEVIC Approv d as to style and content by; (, . I Mladen Kezunovic (Chair of ommitt R. Don Russell Ali Abur (Member) / /$t.... Current Research Status B. Thesis Approach C. Thesis Organize. tion . DESIGN CONCEPT A. The Problem Formulation B. Design Requirements C. Proposed Expert System Organization D. Design Approach E. Conclusion KNOWLEDGE ACQUISITION (KA) A...

Spasojevic, Predrag

2012-06-07

290

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

NASA Astrophysics Data System (ADS)

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

Qin, Yi; Wang, Jiaxu; Mao, Yongfang

2013-11-01

291

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 ...

Charest, Jonathan; Cadorette, Jules; Lecomte, Roger; Brunet, Charles-Antoine; Fontaine, Réjean

2014-01-01

292

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

293

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

294

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

295

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

296

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

297

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

298

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

NASA Astrophysics Data System (ADS)

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

Khawaja, Taimoor Saleem

299

Fault diagnosis in nuclear power plants using an artificial neural network technique  

SciTech Connect

Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis.

Chou, H.P. (National Tsing-Hua Univ., Hsinchu (Taiwan, Province of China)); Prock, J.; Bonfert, J.P. (Institute of Safety Technology, Garching (Germany))

1993-01-01

300

Alarm processing and fault diagnosis using knowledge based systems for transmission and distribution network control  

SciTech Connect

This paper presents two expert system developments which are each connected with utilizing, to the best effect, the increasing volume of SCADA (Supervisory Control And Data Acquisition) system data available to power system control staff. The systems presented, APEX and RESPONDD, are aimed at the two related fields of alarm processing, and fault diagnosis respectively. The areas of commonality between these systems are discussed as well as details specific to each separate system, including a case study of practical operation of each.

McDonald, J.R.; Burt, G.M.; Young, D.J. (Center for Electrical Power Engineering, Univ. of Strathclyde, Glasgow G1 1XW (GB))

1992-08-01

301

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

302

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

303

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

304

Research on the selection of wavelet function for the feature extraction of shock fault in the bearing diagnosis  

Microsoft Academic Search

For the rolling bearing diagnosis, how to identify the fault feature effectively is the key issue. Due to the resonance modulation characteristic induced by shock fault of the rolling bearings, the wavelet transform technology can extract the modulation information effectively. On the other hand, as there are no fixed kernel functions in wavelet analysis, the transform results are closely related

Jian-Yu Zhang; Ling-Li Cui; Gui-Yan Yao; Li-Xin Gao

2007-01-01

305

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

306

Fault Pattern Recognition of Bearing Based on Principal Components Analysis and Support Vector Machine  

Microsoft Academic Search

State monitoring and fault diagnosing of rolling bearing by analyzing vibrating signal is one of the major problem which need to be solved in mechanical engineering. In this paper, a new method of fault diagnosis based on principal components analysis and support vector machine is presented on the basis of statistical learning theory and the feature analysis of vibrating signal

Lu Shuang; Yu Fujin

2009-01-01

307

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

308

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

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

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

312

A real-time fault diagnosis method of SACS based on combination of offline identification and online observing  

NASA Astrophysics Data System (ADS)

A novel fault diagnosis method based on the combination of offline identification and online observing is proposed in this paper, which can meet the requirement of both model complexity and real-time need for the satellite attitude control system. Accurate neural network models, both normal mode and faulty mode, can be obtained by off-line identification based on the data of fault simulation in different fault modes. With a parallel estimator derived from all models With all, fault determination based on threshold logic is designed for online fault detecting and isolating. Real-time simulation results, on the embedded fault simulation platform of satellite attitude control system, illustrate the effectiveness and superiority.

Cen, Zhao-Hui; Wei, Jiao-Long; Jiang, Rui; Liu, Xiong

2009-12-01

313

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

314

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

PubMed

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

315

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

316

Hybrid case-based reasoning for on-line product fault diagnosis  

Microsoft Academic Search

This paper presents a hybrid case-based reasoning system for on-line technical support of PC fault diagnosis. HyCase consists\\u000a of a natural language (keyword) input and the graph-theoretic constraint-net.\\u000a \\u000a Natural language or keyword inputs are parsed and then generated into a constraint-net. The constraint-net is validated and\\u000a its links rationalized and standardized to minimize ambiguity. Cases that partially match either the

S. G. Lee; Y. C. Ng

2006-01-01

317

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

318

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

319

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

320

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

321

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

322

Extraction of Gearbox Fault Features from Vibration Signal Using Wavelet Transform  

Microsoft Academic Search

Vibration signals from a gearbox are usually noisy. As a result, it is difficult to find early symptoms of a potential failure in a gearbox. Wavelet Transform is a powerful tool to signals de-noising and disclose transient information drown in nonstationary vibration signals. Combined with practice example, this paper shows the effectivity of the WT in two facets about signals

X P Ren; W Shao; W Z Yang; F Q Su

2006-01-01

323

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

324

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

325

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

NASA Astrophysics Data System (ADS)

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

He, Qingbo

2013-02-01

326

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

327

Design and implementation of virtual fault diagnosis system for photoelectric tracking devices based on OpenGL  

NASA Astrophysics Data System (ADS)

In view of the crucial deficiency of the traditional diagnosis approaches for photoelectric tracking devices and the output of more sufficient diagnosis information, in this paper, an virtual fault diagnosis system based on open graphic library(OpenGL) is proposed. Firstly, some interrelated key principles and technology of virtual reality, visualization and intelligent fault diagnosis technology are put forward. Then, the demand analysis and architecture of the system are elaborated. Next, details of interrelated essential implementation issues are also discussed, including the the 3D modeling of the related diagnosis equipments, key development process and design via OpenGL. Practical applications and experiments illuminate that the proposed approach is feasible and effective.

Hou, MingLiang; Li, Cunhua; Zhang, Yong; Su, Liyun

2009-10-01

328

Weibull distribution parameters for fault feature extraction of rolling bearing  

Microsoft Academic Search

A novel approach to fault feature extraction using Weibull distribution parameters is proposed. After the original signal of bearing vibration is modeled as the Weibull distribution, its scale parameter is extracted as a new feature vector for the bearing running state. The tests results of fault diagnosis of the rolling bearing verify that this new feature can catch the regularity

Peng Tao; Jiang Haiyan; Xie Yong

2011-01-01

329

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

330

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

PubMed

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. PMID:22734773

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

331

A new method of feature extraction for gearbox vibration signals  

Microsoft Academic Search

In this paper, a new method based on wavelet analysis for feature extraction of gearbox vibration signals is explored. The similarity of the power spectrums between gearbox vibration signals and 1\\/f processes signals makes natural the use of wavelet-based fractal analysis for gearbox fault diagnosis. Then the principle of this method was discussed. To verify the feasibility and practicability of

Peng Li; Qingbo He; Fanrang Kong

2010-01-01

332

To appear in Proceedings of the Fifth International Workshop on Principles of Diagnosis (1994). New Paltz, NY. Theory Revision in Fault Hierarchies  

E-print Network

Paltz, NY. Theory Revision in Fault Hierarchies Pat Langley, George Drastal, R. Bharat Rao and Russell provided by an expert. This paper rst de- scribes the algorithm for theory revision of fault hierarchies USA Abstract The fault hierarchy representation is widely used in expert systems for the diagnosis

Langley, Pat

333

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

E-print Network

and compared through tests in simulation and real buildings. The impacts of the factors including calibrated simulation model accuracy, fault severity, the time of fault occurrence, reference control change magnitude setting, and fault period length...

Lin, Guanjing

2012-12-07

334

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

335

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

336

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

PubMed

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

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

2014-05-01

337

Roller Bearing Fault Diagnosis Based on Nonlinear Redundant Lifting Wavelet Packet Analysis  

PubMed Central

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

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

2011-01-01

338

ON THE USE OF TIME SYNCHRONOUS AVERAGING, INDEPENDENT COMPONENT ANALYSIS AND SUPPORT VECTOR MACHINES FOR BEARING FAULT DIAGNOSIS  

Microsoft Academic Search

Condition monitoring of rolling elements bearings is investigated in this paper. Recently (11), we have shown that Time Synchronous Averaging combined with Support Vector Machines can lead to efficient bearing fault diagnosis. But the generalization performance of the SVM- boundaries was strongly affected by the transmission path of the signals. This paper is then concerned with the integration of Independent

Komgom N. Christian; Njuki Mureithi; Aouni Lakis; Marc Thomas

339

2954 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 6, DECEMBER 2007 Fault Diagnosis and Reconfiguration for Multilevel  

E-print Network

and Reconfiguration for Multilevel Inverter Drive Using AI-Based Techniques Surin Khomfoi, Member, IEEE, and Leon M-bridge multilevel inverter drive (MLID) using artificial-intelligence-based techniques is proposed in this paper, and reconfiguration. Index Terms--Fault diagnosis, multilevel inverter, neural net- work (NN), reconfiguration. I

Tolbert, Leon M.

340

www.cesos.ntnu.no Bo Zhao Centre for Ships and Ocean Structures Fault Diagnosis based on Particle Filter  

E-print Network

.cesos.ntnu.no Bo Zhao ­ Centre for Ships and Ocean Structures Data from: The Software Problem ++, Marine1 www.cesos.ntnu.no Bo Zhao ­ Centre for Ships and Ocean Structures Fault Diagnosis based on Particle Filter - with applications to marine crafts Bo Zhao CeSOS / Department of Marine Technology

Nørvåg, Kjetil

341

Induction Motor Fault Diagnosis Using a Hilbert-Park Lissajou's Curve Analysis and Neural Network-Based Decision  

E-print Network

Induction Motor Fault Diagnosis Using a Hilbert-Park Lissajou's Curve Analysis and Neural Network is simple. This analysis offers an easy interpretation to conclude on the induction motor condition and its with an aim of classifying automatically the various states of the induction motor. This approach was applied

Paris-Sud XI, Université de

342

Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection  

Microsoft Academic Search

Although a variety of methods have been proposed in the literature for machine fault detection, it still remains a challenge to extract prominent features from random and nonstationary vibratory signals, a typical representative of which are the resonance signatures generated by incipient defects on the rolling elements of ball bearings. Due to its random and nonstationary nature, the involved signal

Jie Liu

2012-01-01

343

Research on Fault Diagnosis Method of the Tower Crane Based on RBF Neural Network  

Microsoft Academic Search

As a result of the diversity of the tower crane faults, after the faults occurred, it is difficulty to accurately discriminate the fault type immediately. In this paper, the “clustering” of the RBF neural network effected on the input samples can be used to automatically realize the classification of the failure modes. Accordingly, the faults are diagnosed, and the specific

Xiaoyang Liu; Tingting Xue; Qing Jiang; Jian Li

2010-01-01

344

Joint diagnosis of process and sensor faults using principal component analysis  

Microsoft Academic Search

This paper presents a unified approach to process and sensor fault detection, identification, and reconstruction via principal component analysis. The principal component analysis model partitions the measurement space into a principal component subspace where normal variation occurs, and a residual subspace that faults may occupy. Both process faults and sensor faults are characterized by a direction vector, which describes the

Ricardo Dunia; S. Joe Qin

1998-01-01

345

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

346

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

347

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

348

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

SciTech Connect

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

Hester, G.L.

1988-09-01

349

Fault diagnosis of rolling bearing vibration based on particle swarm optimization-RBF neural network  

Microsoft Academic Search

The training procedures of RBF neural network are faster than BP neural network and it has the global optimal ability. However, a key problem by using the RBF neural network approach is about how to choose the optimal the parameters of RBF neural network. Particle swarm optimization is introduced to select the parameters of RBF neural network. In the paper,

Hui-li Zhang; Shou-gang Huang

2010-01-01

350

Tri-tier Immune System in Antivirus and Software Fault Diagnosis of Mobile Immune Robot Based on Normal Model  

Microsoft Academic Search

In this paper, anti-virus problem and software fault diagnosis of mobile robot, an immune robot, is discussed with proposal\\u000a of a novel tri-tier immune system (TTIS). TTIS is a novel artificial immune system, which is comprised of three computing\\u000a tiers and based on the normal model. The three tiers include inherent immune tier, adaptive immune tier and parallel immune\\u000a tier.

Tao Gong; Zixing Cai

2008-01-01

351

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

NASA Technical Reports Server (NTRS)

Twenty pilots with instrument flight ratings were asked to perform a fault-diagnosis task for which they had relevant domain knowledge. The pilots were asked to think out loud as they requested and interpreted information. Performances were then modeled as the activation and use of a frame system. Cognitive biases, memory distortions and losses, and failures to correctly diagnose the problem were studied in the context of this frame system model.

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

1986-01-01

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

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

NASA Astrophysics Data System (ADS)

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

Feng, Zhipeng; Liang, Ming; Chu, Fulei

2013-07-01

354

Inchoate Fault Detection Framework: Adaptive Selection of Wavelet Nodes and Cumulant Orders  

Microsoft Academic Search

Inchoate fault detection for machine health monitoring (MHM) demands high level of fault classification accuracy under poor signal-to-noise ratio (SNR) which persists in most industrial environment. Vibration signals are extensively used in signature matching for abnormality detection and diagnosis. In order to guarantee improved performance under poor SNR, feature extraction based on statistical parameters which are immune to Gaussian noise

M. F. Yaqub; Iqbal Gondal; Joarder Kamruzzaman

2012-01-01

355

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

Microsoft Academic Search

A Knowledged-Based Operator Advisor System has been developed for enhancing the complex task of maintaining safe and reliable operation of nuclear power plants. The operator's activities have been organized into the four tasks of data interpretation for abstracting high level information from sensor data, plant state monitoring for identification of faults, plan execution for controlling the faults, and diagnosis for

Bhatnagar

1989-01-01

356

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

357

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

PubMed

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

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

2014-08-01

358

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

Microsoft Academic Search

This paper presents two separate algorithms for estimating the running speed and the bearing key frequencies of an induction motor using vibration data. Bearing key frequencies are frequencies at which roller elements pass over a defect point. Most frequency domain-based bearing fault detection and diagnosis techniques (e.g. envelope analysis) rely on vibration measurements and the bearing key frequencies. Thus, estimation

Hasan Ocak; Kenneth A Loparo

2004-01-01

359

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

NASA Astrophysics Data System (ADS)

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

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

2009-07-01

360

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

361

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

362

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

PubMed Central

This paper studies an absolute positioning sensor for a high-speed maglev train and its fault diagnosis method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line diagnosis for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project. PMID:23112619

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

2012-01-01

363

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

PubMed

This paper studies an absolute positioning sensor for a high-speed maglev train and its fault diagnosis method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line diagnosis for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project. PMID:23112619

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

2012-01-01

364

Fault Diagnosis and Reconfiguration for Multilevel Inverter Drive Using AI-Based Techniques  

Microsoft Academic Search

A fault diagnostic and reconfiguration method for a cascaded H-bridge multilevel inverter drive (MLID) using artificial-intelligence-based techniques is proposed in this paper. Output phase voltages of the MLID are used as diagnostic signals to detect faults and their locations. It is difficult to diagnose an MLID system using a mathematical model because MLID systems consist of many switching devices and

Surin Khomfoi; Leon M. Tolbert

2007-01-01

365

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

366

Fault diagnosis of gear box based on multi-weight neural network  

Microsoft Academic Search

Based on a new theory model-BPR (biomimetic pattern recognition), a multi-weight neural network model is implemented to recognized vibration signal of gear box in wind turbines. Because of the complex working condition of wind turbines the vibration signal tends to be non-stationary and complex the difference of the spectrum energy distribution under different loads is remarkable, which causes that there

Zhigang Chen; Xiangjiao Lian

2009-01-01

367

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

368

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

NASA Astrophysics Data System (ADS)

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

Tsao, Wen-Chang; Pan, Min-Chun

2014-03-01

369

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

370

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

NASA Astrophysics Data System (ADS)

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

Elbhbah, Keri; Sinha, Jyoti K.

2013-05-01

371

Prediction and diagnosis of mine hoist fault based on wavelet neural network  

Microsoft Academic Search

The wavelet neural network is used to analyze and predict the time series of key characteristic parameters about the abradability of steel wire rope, time of idle motion, life of pad wear away, clearance of brake shoe, remnant oil pressure and deflection degree of brake disk for the mine hoist. Then the trend of mine hoist fault can be forecasted

Xijun Zhu; Jinyun Guo; Chongyu Wei

2008-01-01

372

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

Shengwei Wang; Jin-Bo Wang

1999-01-01

373

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

374

Demonstration of Fault Detection and Diagnosis Methods for Air-Handling Units  

Microsoft Academic Search

Results are presented from controlled field tests of two methods for detecting and diagnosing faults in HVAC equipment. The tests were conducted in a unique research building that featured two air-handling units serving matched sets of unoccupied rooms with adjustable internal loads. Tests were also conducted in the same building on a third air handler serving areas used for instruction

L. K. Norford; J. A. Wright; R. A. Buswell; D. Luo; C. J. Klaassen; A. Suby

2002-01-01

375

PCA and Local-Wave Method Analysis on Fault Diagnosis of Diesel  

Microsoft Academic Search

Extracting features from the vibration signals has been recognized to be a difficult issue, essentially because of the strong nonlinearity and nonstationary of the signals. In this paper, local wave method is combined with principal component analysis (PCA) and nonlinear dynamics as a model of feature extraction. In this model, reconstruction theory was used to extract dynamic space from time

Yuan Yu; Li Baoliang; Shang Jingshan

2009-01-01

376

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

377

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

378

Application of fuzzy neural network to the nuclear power plant in process fault diagnosis  

Microsoft Academic Search

The fuzzy logic and neural networks are combined in this paper, setting up the fuzzy neural network (FNN); meanwhile, the\\u000a distinct differences and connections between the fuzzy logic and neural network are compared. Furthermore, the algorithm and\\u000a structure of the FNN are introduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to the nuclear

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

2005-01-01

379

Research and Design of Tower Crane Condition Monitoring and Fault Diagnosis System  

Microsoft Academic Search

Tower cranes are playing an important role in hoisting apparatus, while reducing tower crane accident and improving crane safety performance are always urgent. C8051F020 SCM is selected as the core of this system, and many advanced technology such as multisensor data acquisition, expert system and neural network are to be used. The system has self-contained condition monitor in and fault

Yang Yu; Zhenlian Zhao; Liang Chen

2010-01-01

380

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

NASA Astrophysics Data System (ADS)

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

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

2014-01-01

381

A Cyclostationary Analysis Applied to Detection and Diagnosis of Faults in Helicopter Gearboxes  

Microsoft Academic Search

In several cases the vibration signals generated by rotating machines can be modeled as cyclostationary processes. A cyclostationary\\u000a process is defined as a non-stationary process which has a periodic time variation in some of its statistics, and which can\\u000a be characterized in terms of its order of periodicity. This study is focused on the use of cyclic spectral analysis, as

Edgar Estupiñan; Paul White; César San Martín

2007-01-01

382

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

PubMed

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

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

2013-01-01

383

ACTUATOR/SENSORS FAULT DIAGNOSIS FOR AN EXPERIMENTAL HOT ROLLING MILL A CASE STUDY D. THEILLIOL (), M. MAHFOUF(+), D. SAUTER(), J.C. PONSART()  

E-print Network

between two parallel rolls at a specific rolling speed and temperature. The plate thickness is controlledACTUATOR/SENSORS FAULT DIAGNOSIS FOR AN EXPERIMENTAL HOT ROLLING MILL ­ A CASE STUDY D. THEILLIOLJD, UK. email: didier.theilliol@cran.uhp-nancy.fr 1. INTRODUCTION Sensor or actuator failure

Paris-Sud XI, Université de

384

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

385

FAULT DIAGNOSIS IN NONLINEAR SYSTEMS THROUGH AN ADAPTIVE FILTER UNDER A CONVEX SET  

E-print Network

is or is not present (CHEN and PATTON, 1999). These diagnosis methods are based on residual generation. One solution a challeng- ing current research topic (FRANK et al., 2000). (CHEN and PATTON, 1999) have proposed FDI scheme is organised as follows: in the second section, the general problem of the robust selection in nonlinear

Paris-Sud XI, Université de

386

Analytic Redundancy, Possible Conflicts, and TCG-based Fault Signature Diagnosis  

E-print Network

. Model-based approaches to diagnosis are general, apply across multiple operating regions, and have) the Sys- tems Dynamics and Control Engineering (FDI) commu- nity (e.g., [Gertler, 1998] and [Patton, et al [Gertler, 1998] [Patton, et al., 2000]. The goal of this paper is to make a systematic comparison

Koutsoukos, Xenofon D.

387

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

388

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

Microsoft Academic Search

The effects of surficial geology on seismic motion (site effects) are considered one of the major controlling factors to the damage distribution during earthquakes. Qualitative and quantitative estimates of local site amplifications provide important information for the identification of potential high risk areas. In this context, the analysis of ambient vibrations is an attractive tool for the mapping of site

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

2001-01-01

389

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

390

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

2014-06-30

391

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

392

Fuzzy fault diagnostic system based on fault tree analysis  

Microsoft Academic Search

A method is presented for process fault diagnosis using information from fault tree analysis and uncertainty\\/imprecision of data. Fault tree analysis, which has been used as a method of system reliability\\/safety analysis, provides a procedure for identifying failures within a process. A fuzzy fault diagnostic system is constructed which uses the fuzzy fault tree analysis to represent a knowledge of

Zong-Xiao Yang; Kazuhiko SUZUKI; Yukiyasu SHIMADA; Hayatoshi SAYAMA

1995-01-01

393

Fault pattern classification of turbine-generator set based on artificial neural network  

Microsoft Academic Search

By combining wavelet analysis and fuzzy theory, a new approach is presented for vibration fault diagnosis of rotating machine. The wavelet transform has become a powerful alternative for the analysis of nonstationary signals whose spectral characteristics are changing over time, since the widely used spectral analysis method provides only the frequency contents of the signals without providing the time localizations

Yan Li; Baohe Yang; Zhian Wang; Xuhui Wang

2010-01-01

394

A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing  

Microsoft Academic Search

For rolling bearing fault detection, it is expected that a desired time–frequency analysis method should have good computation efficiency, and have good resolution in both time domain and frequency domain. As the best available time–frequency method so far, the wavelet transform still cannot fulfill the rolling bearing fault detection task very well since it has some inevitable deficiencies. The recent

Z. K. Peng; Peter W. Tse; F. L. Chu

2005-01-01

395

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

396

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

PubMed Central

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

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

2010-01-01

397

A method for time-frequency feature extraction from vibration signal based on Hilbert-Huang Transform  

Microsoft Academic Search

Based on the Hilbert-Huang transform (HHT), a method for time-frequency feature extraction from vibration signals was introduced into fault diagnosis of rotors. Firstly, the empirical mode decomposition (EMD) was implemented on vibration signals measured by sensors. As a result, a set of components with different time scales, i.e. intrinsic mode function (IMF), was extracted. Then, the Hilbert Transformation (HT) was

Weidong Jiao

2008-01-01

398

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

399

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

400

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

401

Development of a bridge fault extractor tool  

E-print Network

to process variations and the limitations of the patterning process. What is more important from a test and diagnosis viewpoint is ensuring that the more probable faults are on the fault list. ATPG will likely not target all possible realistic faults... to process variations and the limitations of the patterning process. What is more important from a test and diagnosis viewpoint is ensuring that the more probable faults are on the fault list. ATPG will likely not target all possible realistic faults...

Bhat, Nandan D.

2005-02-17

402

Bearing defect detection and diagnosis using a time encoded signal processing and pattern recognition method  

Microsoft Academic Search

Many new bearing monitoring and diagnosis methods have been explored in the last two decades to provide a technique that is capable of picking up an incipient bearing fault. Vibration analysis is a commonly used condition monitoring technique in world industry and has proved an effective method for rolling bearing monitoring systems. The focus of this paper is to combine

S Abdusslam; P Raharjo; F Gu; A Ball

2012-01-01

403

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

404

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

NASA Astrophysics Data System (ADS)

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

Ocak, Hasan; Loparo, Kenneth A.

2004-05-01

405

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

406

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

407

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.

408

SIMULTANEOUS FAULT DETECTION AND CLASSIFICATION FOR SEMICONDUCTOR MANUFACTURING TOOLS  

E-print Network

SIMULTANEOUS FAULT DETECTION AND CLASSIFICATION FOR SEMICONDUCTOR MANUFACTURING TOOLS Brian E, accurate, and sensitive detection of equipment and process faults to maintain high process yields and rapid fault classification (diagnosis) of the cause to minimize tool downtime in semiconductor manufacturing

Boning, Duane S.

409

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

NASA Astrophysics Data System (ADS)

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

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

2011-09-01

410

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

411

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

412

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

413

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.

Ormand, Carol

414

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....

2009-01-22

415

Active fault tolerant control for maglev train against accelerometer failure  

Microsoft Academic Search

Considering accelerometer failure of single electromagnet suspension system of maglev train, a sensor-fault diagnosis system based on unscented Kalman filter is designed. According to the results of fault detection and diagnosis on line, a improved PID regulator designed off line with tracking differentiators is adopted to tolerate the accelerometer failure. The simulation indicates that the fault diagnosis system designed can

Zhizhou Zhang; Lingling Zhang; Zhiqiang Long

2009-01-01

416

Singularity analysis of the vibration signals by means of wavelet modulus maximal method  

NASA Astrophysics Data System (ADS)

Machine fault diagnosis is vital for safe services and non-interrupted production. The key issue in fault diagnosis is the pattern recognition. A set of valid features will simplify the classifying operations and enhance the accuracy in diagnosis. In this paper, a novel singularity based fault features is presented. Vibration signals collected under different machine health conditions will show different patterns of singularities that can be measured quantitatively by the Lipschitz exponents. The wavelet transforms modulus maximal (WTMM) method provides a simple but accurate method in calculating the Lipschitz exponents. Therefore, the WTMM based Lipschitz exponent calculation as well as the method to select the appropriate wavelet function for WTMM and its range of scale are introduced. Three parameters about the singularity analysis are recommended. They are the number of Lipschitz exponents per rotation N¯, the mean value ?? and the relative standard deviation s of the Lipschitz exponents that are obtained from the extracted features. To verify the usefulness of the proposed methods, simulated signals and vibration signals generated by four types of faults commonly occurred in a rotating machine, including the imbalance, the oil whirl, the coupling misalignment and the rub-impact, had been used for testing purpose. The results show that the signal from the rub-impact possesses the highest singular value and the widest range of singularity. The signal of the coupling misalignment ranked the second. Whilst, the signal of imbalance is more regular or having the smallest singular value and the narrowest range of singularity. The results also prove that the three parameters are excellent fault features for pattern recognition as they can well separate the four fault patterns.

Peng, Z. K.; Chu, F. L.; Tse, Peter W.

2007-02-01

417

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

418

Proc. of Pacific Asian Conference on Expert Systems, pp.322-329, 1997 A Model-based Diagnosis with Fault Event Models  

E-print Network

chain of a fault in a super-heater and a turbine shown in Figure 1. First, in the super-heater and collide with the turbine blade, which results in "breakage" of the blade. Eventually, the revolution. In this example, only misbehavior of the turbine could be detected. The fault of the super-heater could

Mizoguchi, Riichiro

419

To appear in Proceedings of the Fifth International Workshop on Principles of Diagnosis (1994). New Paltz, NY. Theory Revision in Fault Hierarchies  

E-print Network

Paltz, NY. Theory Revision in Fault Hierarchies Pat Langley \\Lambda , George Drastal, R. Bharat Rao hierarchy , which differs from the standard Horn clause represen­ tation used in most theory revision work, NJ 08540 USA Abstract The fault hierarchy representation is widely used in expert systems

Langley, Pat

420

Development of extended MVEM based fault residue generators using UKF state observers  

Microsoft Academic Search

Mean Value Engine Models (MVEM) are used to model the averaged dynamics of an automobile engine system for control and fault diagnosis. One approach to automobile fault diagnosis is to employ the use of a bank of residual generators each of which use a fault model and produces fault residues. These fault residues could then be used to detect or

Jonathan Vasu; S. Sengupta; A. K. Deb; S. Mukhopadhyay

2011-01-01

421

Sensor active fault tolerant control of maglev suspension system based on tracking-differentiator  

Microsoft Academic Search

Based on tracking-differentiator, a new strategy of fault diagnosis and active fault tolerant control for sensor fault of maglev train was proposed. The model of maglev suspension system was established first, and the current control loop was introduced to degrade the system, optimal control algorithm then applied to design control code. For the sensor fault, Based on tracking-differentiator, fault diagnosis

Li Yun; Xue Song; Long Zhiqiang

2008-01-01

422

FOR AIRCRAFT ENGINE FAULT DIAGNOSTICS  

Microsoft Academic Search

Accurate and timely detection and diagnosis of aircraft engine fault is critical to the normal operation of engine\\/airplane and to maintain them in a healthy state. In engine fault diagnostics, engine gas path measurements, such as exhaust gas temperature (EGT), fuel flow (WF) and core speed (N2), etc. are frequently used. Some diagnostics models employ trend shift detection for these

Xiao Hu; Neil Eklund; Kai Goebel

423

Vibration manual  

NASA Technical Reports Server (NTRS)

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

Green, C.

1971-01-01

424

Current\\/Voltage-Based Detection of Faults in Gears Coupled to Electric Motors  

Microsoft Academic Search

Gears form a critical part of many electromechanical systems. Since gear faults cause vibrations, and vibration-based diagnostics are very reliable, this has traditionally been the most commonly used approach to detecting gear faults. However, it is expensive due to the use of high-priced accelerometers and sensor wiring. This paper proposes an alternative way of detecting faults in gears coupled to

Satish Rajagopalan; Thomas G. Habetler; Ronald G. Harley; Tomy Sebastian; Bruno Lequesne

2006-01-01

425

ROBUST FAULT DETECTION BASED ON MULTIPLE FUNCTIONAL SERIES TAR MODELS FOR STRUCTURES WITH TIME-DEPENDENT  

E-print Network

, analyzed and compared within the problem of vibration based fault detection on operating wind turbines-stationarity, uncertain operating conditions, functional series TARMA, fault detection. INTRODUCTION Vibration for engineering structures based on the features of the vibration response signals measured along the structure

Boyer, Edmond

426

Spectral compressor vibration analysis techniques  

Microsoft Academic Search

Studies at GAT have verified that the spectral distribution of energy in gaseous diffusion compressor vibrations contains information pertinent to the state of the compressor's ''health.'' Based on that conclusion, vibration analysis capabilities were included in the CUP computer data acquisition system. In order for that information to be used for diagnosis of incipient failure mechanisms, however, spectral features must

1982-01-01

427

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

428

The alarm problem and directed attention in dynamic fault management  

Microsoft Academic Search

This paper uses results of field studies from multiple domains to explore the cognitive activities involved in dynamic fault management. Fault diagnosis has a different character in dynamic fault management situations as compared to troubleshooting a broken device that has been removed from service. In fault management there is some underlying process (an engineered or physiological process that will be

DAVID D. WOODS

1995-01-01

429

Nonlinear Time Series Analysis of Transformer's Core Vibration  

Microsoft Academic Search

The vibration signal of transformer's core is correlative with the conditions of core's compression and insulation, therefore, vibration monitor of transformer's core is an effective method for on-line monitoring the power transformer delitescent faults. A new method of obtaining the energy distribution with time and frequency of transformer's core vibration based on Hilbert-Huang transformation (HHT) was developed. Firstly, empirical mode

Weihua Xiong; Ruisong Ji

2006-01-01

430

Fault finder  

DOEpatents

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

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

1986-01-01

431

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

NASA Astrophysics Data System (ADS)

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

Guo, Wei; Tse, Peter W.

2013-01-01

432

Vibration response of a cracked rotor in presence of rotor stator rub  

NASA Astrophysics Data System (ADS)

Fatigue crack and rotor-stator rub are two important faults in rotating machinery. Researchers have mostly studied the vibration behavior of a rotor with crack and rotor-stator rub separately. However, once the crack is developed in a rotor, the rotor is more likely to make contact with stator under tight clearance conditions, due to increased vibration level. The present study is aimed to examine vibration response of the cracked rotor in presence of common rotor faults such as unbalance and rotor stator rub. Numerical and experimental investigations are carried out and steady-state vibration analysis is presented. Experimental investigation for a multifault rotor system is attempted for the first time. The full spectrum analysis has been used effectively to extract the distinctive directional features of these rotor faults. The investigation focuses on directional nature of the higher harmonics for identification of rub in the cracked rotor. The study reveals that spectrum rich in spectral lines is a rub symptom. However, these higher harmonics are weaker than the 1X response. Rub in uncracked rotor excites forward and backward whirling frequency components almost equally. Cracked rotor without rub exhibits strongly forward whirling vibrations. Rotor rub in the cracked rotor reveals different response compared with the uncracked rotor, particularly the nature of 2X and higher harmonics at corresponding subharmonic resonances. Backward whirling nature of 2X frequency component as well as that of higher harmonic (that matches with the bending natural frequency) at corresponding subharmonic resonances, has been proposed for diagnosis of rotor rub in cracked rotor.

Patel, Tejas H.; Darpe, Ashish K.

2008-11-01

433

Physical-Defect Modeling and Optimization for Fault-Insertion Test  

Microsoft Academic Search

Hardware fault insertion is a promising method for system reliability assessment and fault isolation. It provides feedback on the fault tolerance of a large system, creates artificial faulty scenarios that can be used as reference points for fault diagnosis, and leads to a quality diagnostic program. Optimization of fault insertion location is critical for accelerating the assessment of system reliability

Zhaobo Zhang; Zhanglei Wang; Xinli Gu; Krishnendu Chakrabarty

2012-01-01

434

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

PubMed

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

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

2014-11-01

435

Real time automatic detection of bearing fault in induction machine using kurtogram analysis.  

PubMed

A proposed signal processing technique for incipient real time bearing fault detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary vibration signal and for real time diagnosis. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing fault automatic detection method and gives a good basis for an integrated induction machine condition monitor. PMID:23145702

Tafinine, Farid; Mokrani, Karim

2012-11-01

436

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

NASA Astrophysics Data System (ADS)

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

Wang, Yanxue; He, Zhengjia; Zi, Yanyang

2010-01-01

437

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

NASA Astrophysics Data System (ADS)

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

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

2014-07-01

438

Automatic translation of digraph to fault-tree models  

NASA Technical Reports Server (NTRS)

The author presents a technique for converting digraph models, including those models containing cycles, to a fault-tree format. A computer program which automatically performs this translation using an object-oriented representation of the models has been developed. The fault-trees resulting from translations can be used for fault-tree analysis and diagnosis. Programs to calculate fault-tree and digraph cut sets and perform diagnosis with fault-tree models have also been developed. The digraph to fault-tree translation system has been successfully tested on several digraphs of varying size and complexity. Details of some representative translation problems are presented. Most of the computation performed by the program is dedicated to finding minimal cut sets for digraph nodes in order to break cycles in the digraph. Fault-trees produced by the translator have been successfully used with NASA's Fault-Tree Diagnosis System (FTDS) to produce automated diagnostic systems.

Iverson, David L.

1992-01-01

439

Good Vibrations  

NSDL National Science Digital Library

This lesson (on pages 15-24 of PDF) explores how sound is caused by vibrating objects. It explains that we hear by feeling vibrations passing through the air. Learners take part in several demonstrations, making those vibrations visible. They put a tuning fork in a shallow pan of water and use it to bounce a ping-pong ball, showing the fact that the tuning fork is vibrating when it's making a sound. There are extensions described involving comb kazoos, rubber band guitars, and putting rice or cereal on top of a drum.

Omsi

2010-01-01

440

The fractal characteristic of vibration signals in different milling tool wear periods  

NASA Astrophysics Data System (ADS)

There are a wide variety of condition monitoring techniques currently used for the recognition and diagnosis of machinery faults. Tool wear often results in chaotics on milling process. Little research has been carried out about the occurrence and detection of chaotic behavior in time series signal of tool vibration. In the paper the vibration acceleration signal based on the operating stages of tool wear is established for the analysis of the correlation dimension of the operating stages of tool wear. Correlation dimension is calculated to recognize the tool wear operating conditions. Finally ,some experimental results from the fractal characteristic show that there are distinct differences in the correlation dimension in different tool wear conditions and close the correlation dimension in same tool wear conditions. The correlation dimension not only can be used as important scientific basis for monitoring tool wear, but also complement of other characteristic picking up method.

Xu, Chuangwen; Cheng, Hualing; Liu, Limei

2008-10-01

441

Vibration-Based Damage Diagnosis in a Laboratory Cable-Stayed Bridge Model via an RCP-ARX Model Based Method  

NASA Astrophysics Data System (ADS)

Vibration-based damage detection and identification in a laboratory cable-stayed bridge model is addressed under inherent, environmental, and experimental uncertainties. The problem is challenging as conventional stochastic methods face difficulties due to uncertainty underestimation. A novel method is formulated based on identified Random Coefficient Pooled ARX (RCP-ARX) representations of the dynamics and statistical hypothesis testing. The method benefits from the ability of RCP models in properly capturing uncertainty. Its effectiveness is demonstrated via a high number of experiments under a variety of damage scenarios.

Michaelides, P. G.; Apostolellis, P. G.; Fassois, S. D.

2011-07-01

442

Scalable robot fault detection and identification  

Microsoft Academic Search

Experience has shown that even carefully designed and tested robots may encounter anomalous situations. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner. Robot fault diagnosis typically requires tracking a very large number of possible faults in complex non-linear dynamic systems with noisy sensors. Traditional methods either ignore

Vandi Verma; Reid G. Simmons

2006-01-01

443

Building method of diagnostic model of Bayesian networks based on fault tree  

NASA Astrophysics Data System (ADS)

Fault tree (FT) is usually a reliability and security analysis and diagnoses decision model. It is also in common use that expressing fault diagnosis question with fault tree model. But it will not be changed easily if fault free model was built, and it could not accept and deal with new information easily. It is difficult to put the information which have nothing to do with equipment fault but can be used to fault diagnosis into diagnostic course. Bayesian Networks (BN) can learn and improve its network architecture and parameters at any time by way of practice accumulation, and raises the ability of fault diagnosis. The method of building BN based on FT is researched on this article, this method could break through the limitations of FT itself, make BN be more extensively applied to the domain of fault diagnosis and gains much better ability of fault analysis and diagnosis.

Liu, Xiao; Li, Haijun; Li, Lin

2008-10-01

444

Good Vibrations  

NSDL National Science Digital Library

In this activity, learners experiment with their voices and noisemakers to understand the connections between vibrations and the sounds created by those vibrations. This resource includes three quick demonstration activities that can be used independently or as a group to introduce learners to the basic elements of sound.

Omsi

2004-01-01

445

Vibrational Coupling  

SciTech Connect

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

None

2011-01-01

446

Multiple Fault Isolation in Redundant Systems  

NASA Technical Reports Server (NTRS)

We consider the problem of sequencing tests to isolate multiple faults in redundant (fault-tolerant) systems with minimum expected testing cost (time). It can be shown that single faults and minimal faults, i.e., minimum number of failures with a failure signature different from the union of failure signatures of individual failures, together with their failure signatures, constitute the necessary information for fault diagnosis in redundant systems. In this paper, we develop an algorithm to find all the minimal faults and their failure signatures. Then, we extend the Sure diagnostic strategies [1] of our previous work to diagnose multiple faults in redundant systems. The proposed algorithms and strategies are illustrated using several examples.

Shakeri, M.; Pattipati, Krishna R.; Raghavan, V.; Patterson-Hine, Ann; Iverson, David L.

1997-01-01

447

Fault mechanics  

SciTech Connect

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

Segall, P. (USAF, Geophysics Laboratory, Hanscom AFB, MA (United States))

1991-01-01

448

Machine learning techniques for fault isolation and sensor placement  

NASA Technical Reports Server (NTRS)

Fault isolation and sensor placement are vital for monitoring and diagnosis. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance.

Carnes, James R.; Fisher, Douglas H.

1993-01-01

449

Fault detection of large scale wind turbine systems  

Microsoft Academic Search

Fault diagnosis of large scale wind turbine systems has received much attention in the recent years. Effective fault prediction would allow for scheduled maintenance and for avoiding catastrophic failures. Thus the availability of wind turbines can be enhanced and the cost for maintenance can be reduced. In this paper, we consider the sensor and actuator fault detection issue for large

Xiukun Wei; Lihua Liu

2010-01-01

450

Fault estimation of large scale wind turbine systems  

Microsoft Academic Search

Fault diagnosis of large scale wind turbine systems has received much attention in the recent years. Effective fault prediction would allow for scheduled maintenance and for avoiding catastrophic failures. Thus the availability of wind turbines can be enhanced and the cost for maintenance can be reduced. In this paper, we consider the sensor and actuator fault detection issue for large

Wei Xiukun; Liu Lihua

2010-01-01

451

Detection of a static eccentricity fault in a closed loop driven induction motor by using the angular domain order tracking analysis method  

NASA Astrophysics Data System (ADS)

In this study, a new method was presented for the detection of a static eccentricity fault in a closed loop operating induction motor driven by inverter. Contrary to the motors supplied by the line, if the speed and load, and therefore the amplitude and frequency, of the current constantly change then this also causes a continuous change in the location of fault harmonics in the frequency spectrum. Angular Domain Order Tracking analysis (AD-OT) is one of the most frequently used fault diagnosis methods in the monitoring of rotating machines and the analysis of dynamic vibration signals. In the presented experimental study, motor phase current and rotor speed were monitored at various speeds and load levels with a healthy and static eccentricity fault in the closed loop driven induction motor with vector control. The AD-OT method was applied to the motor current and the results were compared with the traditional FFT and Fourier Transform based Order Tracking (FT-OT) methods. The experimental results demonstrate that AD-OT method is more efficient than the FFT and FT-OT methods for fault diagnosis, especially while the motor is operating run-up and run-down. Also the AD-OT does not incur any additional cost for the user because in inverter driven systems, current and speed sensor coexist in the system. The main innovative parts of this study are that AD-OT method was implemented on the motor current signal for the first time.

Akar, Mehmet

2013-01-01

452

Classification of Aircraft Maneuvers for Fault Detection  

NASA Technical Reports Server (NTRS)

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.

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

2002-01-01

453

Classification of Aircraft Maneuvers for Fault Detection  

NASA Technical Reports Server (NTRS)

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data is a reasonable match to known examples of proper operation. In our domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. We explain where this subsystem fits into our envisioned fault detection system as well its experiments showing the promise of this classification subsystem.

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

2002-01-01

454

Current\\/Voltage Based Detection of Faults in Gears Coupled to Electric Motors  

Microsoft Academic Search

Gears form a critical part of many electro-mechanical systems. Since gear faults cause vibrations, and vibration-based diagnostics is very reliable, this has traditionally been the most commonly used approach to detecting gear faults. However, it is expensive due to the use of high-priced accelerometers and sensor wiring. This paper proposes an alternative way of detecting faults in gears coupled to

S. Rajagopalan; T. G. Habetler; R. G. Harley; T. Sebastian; B. Lequesne

2005-01-01

455

Vibration generators  

SciTech Connect

Apparatus for generating vibrations in a medium, such as the ground, comprises a first member which contacts the medium, means , preferably electromagnetic, which includes two relatively movable members for generating vibrations in the apparatus and means operatively connecting the said two members to said first member such that the relatively amplitudes of the movements of said three members can be adjusted to match the impedances of the apparatus and the medium.

Lerwill, W.E.

1980-09-16

456

Application of the largest Lyapunov exponent for the diagnosis of rotor-to-stator rub in rotating machinery  

NASA Astrophysics Data System (ADS)

Vibration based diagnosis has played a vital role in the crucial analysis of faults in rotating machinery. The conventional vibration techniques currently being used in the industry are however not able to effectively identify nonlinear or complex signals due to, for example, rotor-to-stator rub. This paper reports on the application of the largest Lyapunov Exponent ?1 to diagnose the severity of rotor-to-stator rub in rotating machinery. The method of time delay was employed to reconstruct the vibration signal obtained from numerical simulation of a Jeffcott rotor subjected to rotor-to-stator rub. Numerical results show clear correlation between the increase of rub severity with the invariant quantity ?1 investigated in this work. Two different algorithms for the computation of ?1 were employed in this research, namely the Wolf algorithm and the Rosenstein algorithm.

ChangJie, Colin Heng; Inayat-Hussain, Jawaid I.

2014-10-01

457

Neural net and expert system diagnose transformer faults  

Microsoft Academic Search

Dissolved gas-in-oil analysis (DGA) is a common practice in transformer incipient fault diagnosis. The analysis techniques include the conventional key gas method, ratio methods, and artificial intelligence methods. Application of artificial intelligence (Al) techniques have shown very promising results. The methods include fuzzy logic, expert systems (EPS), evolutionary algorithms (EA), and artificial neural networks (ANN). A transformer incipient fault diagnosis

Zhenyuan Wang; Yilu Liu; P. J. Griffin

2000-01-01

458

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

NASA Astrophysics Data System (ADS)

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

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

2012-11-01

459

A Smart Sensing Unit for Vibration Measurement and Monitoring  

Microsoft Academic Search

A novel smart sensing unit is developed in this paper for vibration measurement and machinery condition monitoring. The microprocessor-based smart sensor can collect 2-D vibrations and conduct signal analysis. When mounted in proximity of a bearing housing (a general case), it can conduct online fault detection in shafts and bearings. A correlation spectrum method is proposed as a digital encoder

Wilson Wang; Ofelia Antonia Jianu

2010-01-01

460

Vibration sensors  

NASA Astrophysics Data System (ADS)

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

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

2003-10-01

461

Critical fault patterns determination in fault-tolerant computer systems  

NASA Technical Reports Server (NTRS)

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

Mccluskey, E. J.; Losq, J.

1978-01-01

462

Mutifractal Analysis of Diesel Cylinder Liner Fault  

Microsoft Academic Search

Vibration signal of diesel engine fault is nonstationary and nonlinear, and it is very difficult to analyse using traditional frequency spectrum method. In this paper, we try to analyse the signal with multifractal analysis which is from the viewing signal irregular behavior and self-similarity instead of frequency. Application indicates that the method described in the paper is a good way

Hongying Hu; Fuliang Yin; Jing Kang

2007-01-01

463

Timescale local approach for vibration monitoring  

Microsoft Academic Search

It is difficult to monitor machine condition and diagnose mechanical faults in mechanical equipment which have varied operational modes and whose dynamic signals are nonstationary. The identification of natural frequencies of vibration and modal damping is of fundamental engineering importance. The damage done to a particular structure under an unknown force might be ascribed to the relative damping or the

Ahmed Hambaba; E. Huff; U. Kaul

2001-01-01

464

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

SciTech Connect

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

NONE

1995-03-01

465

The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection. Part 2 of the two related manuscripts that have a joint title as "Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement—Parts 1 and 2"  

NASA Astrophysics Data System (ADS)

Rolling element bearings are the most important components used in machinery. Bearing faults, once they have developed, quickly become severe and can result in fatal breakdowns. Envelope spectrum analysis is one effective approach to detect early bearing faults through the identification of bearing fault characteristic frequencies (BFCFs). To achieve this, it is necessary to find a band-pass filter to retain a resonant frequency band for the enhancement of weak bearing fault signatures. In Part 1 paper, the wavelet packet filters with fixed center frequencies and bandwidths used in a sparsogram may not cover a whole bearing resonant frequency band. Besides, a bearing resonant frequency band may be split into two adjacent imperfect orthogonal frequency bands, which reduce the bearing fault features. Considering the above two reasons, a sparsity measurement based optimal wavelet filter is required to be designed for providing more flexible center frequency and bandwidth for covering a bearing resonant frequency band. Part 2 paper presents an automatic selection process for finding the optimal complex Morlet wavelet filter with the help of genetic algorithm that maximizes the sparsity measurement value. Then, the modulus of the wavelet coefficients obtained by the optimal wavelet filter is used to extract the envelope. Finally, a non-linear function is introduced to enhance the visual inspection ability of BFCFs. The convergence of the optimal filter is fastened by the center frequencies and bandwidths of the optimal wavelet packet nodes established by the new sparsogram. Previous case studies including a simulated bearing <