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A thorough understanding of the spectral structure of planetary gear system vibration signals is helpful to faultdiagnosis of planetary gearboxes. Considering both the amplitude modulation and the frequency modulation effects due to gear damage and periodically time variant working condition, as well as the effect of vibration transfer path, signal models of gear damage for faultdiagnosis of planetary gearboxes are given and the spectral characteristics are summarized in closed form. Meanwhile, explicit equations for calculating the characteristic frequency of local and distributed gear fault are deduced. The theoretical derivations are validated using both experimental and industrial signals. According to the theoretical basis derived, manually created local gear damage of different levels and naturally developed gear damage in a planetary gearbox can be detected and located.
Purpose – The purpose of this paper is to identify the efficiency of vibration signals for faultdiagnosis system of induction motors. Design\\/methodology\\/approach – A faultdiagnosis system for induction motors using vibration signals is designed based on pattern recognition. Genetic algorithm is used for feature reduction and neural network tuning. Findings – The usage of genetic algorithm improves the
Aiming at the characteristics of the surface vibration signals measured from the diesel engine, a novel method combining local wave decomposition (LWD) and lifting wavelet denoising is proposed, and is used for feature extraction and condition evaluation of diesel engine vibration signals. Firstly, the original data is preprocessed using the lifting wavelet transformation to suppress abnormal interference of noise, and avoid the pseudo mode functions from LWD. Obtaining intrinsic mode functions(IMFs) by using LWD, the instantaneous frequency and amplitude can be calculated by Hilbert transform. Hilbert marginal spectrum can exactly provide the energy distribution of the signal with the change of instantaneous frequency. The vibration signals of diesel engine piston-liner wear are analyzed and the results show that the method is feasible and effective in feature extraction and condition evaluation of diesel engine faults.
This paper presents a novel method to analyze the vibration signals in the faultdiagnosis of water hydraulic motor. The method of feature extraction from the vibration signals of the water hydraulic motor based on the second-generation wavelet is investigated. The second-generation wavelet consists of a lifting scheme. The algorithm and method of multi-decomposition based on the lifting scheme for vibration analysis is developed. The denoise method for the vibration signals is proposed on the lifting scheme and the generalized cross validation (GCV). The relationship between the signal-to-noise (SNR) and GCV is presented. The corrupted simulated signal is used to test the proposed denoise algorithm. The method for extracting the feature values from the impulse vibration signals based on the statistical method is studied. The results show the applicability of this method for faultdiagnosis of a water hydraulic motor.
There has been an increasing application of water hydraulics in industries due to growing concern on the environmental, health and safety issues. The faultdiagnosis of water hydraulic motor is important for improving water hydraulic system reliability and performance. In this paper, faultdiagnosis of water hydraulic motor in water hydraulic system is investigated based on adaptive wavelet analysis. A novel method for modelling the vibration signal based on the adaptive wavelet transform (AWT) is proposed. The linear combination of wavelets is introduced as wavelet itself and adapted for the particular vibration signal, which goes beyond adapting parameters of a fixed-shape wavelet. The AWT procedure based on the parametric optimisation by genetic algorithm (GA) is developed. The model-based method by AWT is applied to extract the features in the faultdiagnosis of the water hydraulic motor. This technique for de-noising the corrupted simulation signal shows that it can improve the signal-to-noise ratio of the vibration signal. The results of the experimental signal demonstrate the characteristic vibration signal details in fine resolution. The magnitude plots of the continuous wavelet transform (CWT) show the characteristic signal's energy in time and frequency domain which can be used as feature values for faultdiagnosis of water hydraulic motor.
There is a wide variety of condition monitoring techniques currently in use for the diagnosis and prediction of machinery faults, but little attention has been paid to the occurrence and detection of chaotic behaviour in time series vibration signals. This paper introduces some of the basic concepts of chaos theory, then details a method for quantifying a fractal dimension from
Pumps are the largest single consumer of power in industry. This means that faulty pumps cause a high rate of energy loss with associated performance degradation, high vibration levels and significant noise radiation. This paper investigates the correlations between pump performance parameters including head, flow rate and energy consumption and surface vibration for the purpose of both pump condition monitoring and performance assessment. Using an in-house pump system, a number of experiments have been carried out on a centrifugal pump system using five impellers: one in good condition and four others with different defects, and at different flow rates for the comparison purposes. The results have shown that each defective impeller performance curve (showing flow, head, efficiency and NPSH (Net Positive Suction Head) is different from the benchmark curve showing the performance of the impeller in good condition. The exterior vibration responses were investigated to extract several key features to represent the healthy pump condition, pump operating condition and pump energy consumption. In combination, these parameter allow an optimal decision for pump overhaul to be made .
A DSP-based measurement system dedicated to the vibration analysis on rotating machines was designed and realized. Vibration signals are on-line acquired and processed to obtain a continuous monitoring of the machine status. In case of fault, the system is capable of isolating the fault with a high reliability. The paper describes in detail the approach followed to built up fault
Giovanni Betta; Consolatina Liguori; A. Paolillo; A. Pietrosanto
A DSP-based measurement system dedicated to the vibration analysis on rotating machines was designed and realized. Vibration signals are on-line acquired and processed to obtain a continuous monitoring of the machine status. In case of a fault, the system is capable of isolating the fault with a high reliability. The paper describes in detail the approach followed to built up
Giovanni Betta; Consolatina Liguori; Alfredo Paolillo; Antonio Pietrosanto
There are many auxiliaries in a power plant (APP) with high rotating speed, such as pumps, fans, motors and so on. To warrant their safe and reliable operation, their state of vibration has to be monitored. But because of their scattered locations, the traditional way of online monitoring with shielded cable connections is costly and work expensive and the precision,
The wavelet transform is used to represent all possible types of transients in vibration signals generated by faults in a gearbox. It is shown that the transform provides a powerful tool for condition monitoring and faultdiagnosis. The vibration signal from a helicopter gearbox is used to demonstrate the application of the suggested wavelet by a simple computer algorithm. The
A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor faultdiagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor faultdiagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach.
Faultdiagnosis based on Principal Component Analysis (PCA) and Discrete Hidden Markov Model (DHMM) for engine are studied. First, the vibration signal feature extraction from the diesel engine is realized by PCA; next, the vibration signal feature extraction algorithm is designed; then DHMM is applied for faultdiagnosis; furthermore, a fault classifier based on DHMM with diagnostic databases is developed;
Reliability is an important research topic in distributed systems. To achieve suitable reliability, the fault tolerance of distributed systems must be studied. One of the most important issues surrounding fault tolerance is the Byzantine Agreement (BA) problem. The goal of BA is to achieve a common agreement among fault-free processors even where faults persist. Likewise, faultdiagnosis agreement (FDA) the
Faultdiagnosis is one of the key technologies of prognostic and health management system (PHM) of aircraft hydraulic system. Aiming at the strong coupling of various fault features of hydraulic pump when multiple faults occur simultaneously, a hiberarchy clustering faultdiagnosis strategy was proposed, in which three level fault reasoning machine was adopted for five kinds of failures for hydraulic
There is much difficulty in faultdiagnosis because of lacking of system fault samples. So this paper presents a mixed strategy of combining differential evolution algorithm with local enhanced operator with the optimization of the parameters of support vector machine. For the diesel engine valve clearance faultdiagnosis, the measured diesel engine valve vibration signals data after wavelet transform are
Cao Longhan; Wu Mingliang; He Junqiang; Liu Lu; Liu Xiaoli
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 faultsdiagnosis are therefore critical to machinery normal operation. The use of mechanical vibration signals for faultdiagnosis is significant and effective due to advances in
Efficient diagnosis is very important for improving reliability and performance of aircraft hydraulic piston pump, and it is one of the key technologies in prognostic and health management system. In practice, due to harsh working environment and heavy working loads, multiple faults of an aircraft hydraulic pump may occur simultaneously after long time operations. However, most existing diagnosis methods can only distinguish pump faults that occur individually. Therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic pump.In this paper, a new method based on the layered clustering algorithm is proposed to diagnose multiple faults of an aircraft hydraulic pump that occur simultaneously. The intensive failure mechanism analyses of the five main types of faults are carried out, and based on these analyses the optimal combination and layout of diagnostic sensors is attained. The three layered diagnosis reasoning engine is designed according to the faults' risk priority number and the characteristics of different fault feature extraction methods. The most serious failures are first distinguished with the individual signal processing. To the desultory faults, i.e., swash plate eccentricity and incremental clearance increases between piston and slipper, the clustering diagnosis algorithm based on the statistical average relative power difference (ARPD) is proposed. By effectively enhancing the fault features of these two faults, the ARPDs calculated from vibration signals are employed to complete the hypothesis testing. The ARPDs of the different faults follow different probability distributions. Compared with the classical fast Fourier transform-based spectrum diagnosis method, the experimental results demonstrate that the proposed algorithm can diagnose the multiple faults, which occur synchronously, with higher precision and reliability.
A fault signal diagnosis technique for internal combustion engines that uses a continuous wavelet transform algorithm is presented in this paper. The use of mechanical vibration and acoustic emission signals for faultdiagnosis in rotating machinery has grown significantly due to advances in the progress of digital signal processing algorithms and implementation techniques. The conventional diagnosis technology using acoustic and
As a part of a substation-level decision support system, a new intelligent Hierarchical FaultDiagnosis System for on-line faultdiagnosis is presented in this paper. The proposed diagnosis system divides the faultdiagnosis process into two phases. Using time-stamped information of relays and breakers, phase 1 identifies the possible fault sections through the Group Method of Data Handling (GMDH) networks, and phase 2 recognizes the types and detailed situations of the faults identified in phase 1 by using a fast bit-operation logical inference mechanism. The diagnosis system has been practically verified by testing on a typical Taiwan power secondary transmission system. Test results show that rapid and accurate diagnosis can be obtained with flexibility and portability for faultdiagnosis purpose of diverse substations.
Huang, Y.C.; Huang, C.L. [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Dept. of Electrical Engineering; Yang, H.T. [Chung Yuan Christian Univ., Chung-Li (Taiwan, Province of China). Dept. of Electrical Engineering
Most of the existing time series methods of feature extraction involve complex algorithm and the extracted features are affected by sample size and noise. In this paper, a simple time series method for bearing fault feature extraction using singular spectrum analysis (SSA) of the vibration signal is proposed. The method is easy to implement and fault feature is noise immune. SSA is used for the decomposition of the acquired signals into an additive set of principal components. A new approach for the selection of the principal components is also presented. Two methods of feature extraction based on SSA are implemented. In first method, the singular values (SV) of the selected SV number are adopted as the fault features, and in second method, the energy of the principal components corresponding to the selected SV numbers are used as features. An artificial neural network (ANN) is used for faultdiagnosis. The algorithms were evaluated using two experimental datasets—one from a motor bearing subjected to different fault severity levels at various loads, with and without noise, and the other with bearing vibration data obtained in the presence of a gearbox. The effect of sample size, fault size and load on the fault feature is studied. The advantages of the proposed method over the exiting time series method are discussed. The experimental results demonstrate that the proposed bearing faultdiagnosis method is simple, noise tolerant and efficient.
Muruganatham, Bubathi; Sanjith, M. A.; Krishnakumar, B.; Satya Murty, S. A. V.
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 faultdiagnosis. 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 faultdiagnosis.
This paper discusses condition monitoring and faultdiagnosis in Francis turbine based on integration of numerical modelling with several different artificial intelligence (AI) techniques. In this study, a numerical approach for fluid-structure (turbine runner) analysis is presented. The results of numerical analysis provide frequency response functions (FRFs) data sets along x-, y- and z-directions under different operating load and different position and size of faults in the structure. To extract features and reduce the dimensionality of the obtained FRF data, the principal component analysis (PCA) has been applied. Subsequently, the extracted features are formulated and fed into multiple artificial neural networks (ANN) and multiple adaptive neuro-fuzzy inference systems (ANFIS) in order to identify the size and position of the damage in the runner and estimate the turbine operating conditions. The results demonstrated the effectiveness of this approach and provide satisfactory accuracy even when the input data are corrupted with certain level of noise.
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
In order to overcomes some shortages of Belief Network dynamic causality diagram is put forward. Its knowledge expression, reasoning, probability computing and also the model of causality diagram used for system faultdiagnosis, the model constructing method and reasoning algorithm are proposed. At last, an application example in the faultdiagnosis of the nuclear power plant is given which shows
The Knitting, Lace and Net Industry Training Board has developed a training innovation called faultdiagnosis training. The entire training process concentrates on teaching based on the experiences of troubleshooters or any other employees whose main tasks involve faultdiagnosis and rectification. (Author/DS)
|The Knitting, Lace and Net Industry Training Board has developed a training innovation called faultdiagnosis training. The entire training process concentrates on teaching based on the experiences of troubleshooters or any other employees whose main tasks involve faultdiagnosis and rectification. (Author/DS)|
In this paper, a new technique is proposed for diagnosing multiple faults in a given erroneous circuit with improved diagnosis resolution The first technique is based on Single Location At a Time (SLAT) and path tracing techniques which starts with an imrial fault list obtained from an existing diagnosis method. The single observation - single location at a time (SOSLAT)
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 faultdiagnosis 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 faultdiagnosis.
Networked control systems (NCS) are feedback systems closed through data networks. NCS have many advantages compared with traditional systems; however, the network-induced delay and other characteristics of data networks may degrade the performance of feedback systems designed without taking the network into account. Supported by the National Nature Science Foundation of China, we studied the faultdiagnosis and fault-tolerant control
Over the past two decades, condition monitoring and faultsdiagnosis in rotating machinery have been widely studied and reported. In the present paper an algorithm for faultdiagnosis in industrial rotating machines facing new operating conditions emergence is developed on the basis of input indicators, extracted from vibrations spectrums. Indicators selection is used to improve diagnosis performances by the help of a hybrid approach using several selection criteria and different classifiers. To validate the performances of this algorithm, experimental tests were conducted on two industrial systems with various operating conditions. The results have proved the effectiveness of the developed algorithm compared to the "J48 decision tree" and also reveal the need to re-select the indicators for reliable monitoring of working conditions.
Khelf, Ilyes; Laouar, Lakhdar; Bouchelaghem, Abdelaziz M.; Rémond, Didier; Saad, Salah
A Hilbert-Huang transform (HHT) is a time-frequency technique and has been widely applied to analyzing vibration signals in the field of faultdiagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical
The BP algorithm and the theory of the artificial neural network are applied in the research of the faultdiagnosis of diesel engine fuel injection system. Through study on the diesel engine fuel injection system's work characteristics and the vibration signal characteristic parameter, gives the method and the step of gathering the vibration signal, set the parameter of BP neural
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 studied and compared. Signals were acquired from bearings working under different frequency and load conditions. An evaluation of fault recognition efficiency was performed for each combination of signal processing and pattern recognition techniques. All four schemes of classification yielded reasonably good results and are thus shown to be promising for rolling bearing fault monitoring and diagnosing.
de Moura, E. P.; Souto, C. R.; Silva, A. A.; Irmão, M. A. S.
This paper deals with a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification. Vibration signals for normal bearings, bearings with inner race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the accelerometer signals and to generate feature vectors. An adaptive neural-fuzzy inference system (ANFIS) was trained and used as a diagnostic classifier. For comparison purposes, the Euclidean vector distance method as well as the vector correlation coefficient method were also investigated. The results demonstrate that the developed diagnostic method can reliably separate different fault conditions under the presence of load variations.
In this study the features for bearing faultdiagnosis is investigated based on the analysis of temperature, vibration and current measurements of a 3 phase, 4 poles, 5 HP induction motors which are chemically, thermally and electrically aged by artificial aging methods. Then three adaptive neuro-fuzzy inference systems which takes the temperature, current and vibration measurements as inputs and the
Firstly, this research paper introduced the process of transformer faultdiagnosis and the theory of DFTA and then we attempt to apply DFTA to the field of transformer faultsdiagnosis. By establishing the fault tree of transformer, a practical, easily-extended, interactive and self-learning enabled faultdiagnosis system based on DFTA for transformer is designed and developed. With the implementation and
Jiang Guo; Kefei Zhang; Lei Shi; Kaikai Gu; Weimin Bai; Bing Zeng; Yajin Liu
This paper presents a novel time-frequency-based feature recognition system for gear faultdiagnosis using autocorrelation of continuous wavelet coefficients (CWC). Furthermore, it introduces an original mathematical approximation of gearbox vibration signals which approximates sinusoidal components of noisy vibration signals generated from gearboxes, including incipient and serious gear failures using autocorrelation of CWC. First, the drawbacks of the continuous wavelet transform (CWT) have been eliminated using autocorrelation function. Secondly, the autocorrelation of CWC is introduced as an original pattern for fault identification in machine condition monitoring. Thirdly, a sinusoidal summation function consisting of eight terms was used to approximate the periodic waveforms generated by autocorrelation of CWC for normal gearboxes (NGs) as well as occurrences of incipient and severe gear fault (e.g. slight-worn, medium-worn, and broken-tooth gears). In other words, the size of vibration signals can be reduced with minimal loss of significant frequency content by means of the sinusoidal approximation of generated autocorrelation waveforms of CWC as reported in this paper.
Vibration-based machine condition monitoring incorporates a number of machinery fault detection and diagnostic techniques. Many machinery fault diagnostic techniques use automatic signal classification in order to increase accuracy and reduce errors caused by subjective human judgment. In this paper, fuzzy logic techniques have been applied to classify frequency spectra representing various rolling element bearing faults. The frequency spectra representing a number of different fault conditions have been processed using a variety of fuzzy set shapes. The application of basic fuzzy logic techniques has allowed fuzzy numbers to be generated which represent the similarity between frequency spectra. Correct classification of different bearing fault spectra was observed when the correct combination of fuzzy set shapes and range of membership domains were used. The problem of membership overlapping found in previous studies, where classifying individual spectrum with respect to spectra that represent true fault classes was not conclusive, has been overcome. Further work is described which will extend this technique to other classes of machinery using generic software.
\\u000a Based on the construction feature and operating principle of the wet-shift clutch transmission, the condition monitoring and\\u000a faultdiagnosis for the transmission of the tracklayer with wet-shift clutch were implemented with using the oil analysis\\u000a technology, function parameter test method and vibration analysis technology. The new faultdiagnosis methods were proposed,\\u000a which are to build the gray modeling with the
Faultdiagnosis is essentially a kind of pattern recognition. How to implement feature extraction and improve recognition performance is a crucial task. In this paper, a new supervised manifold learning algorithm (S-LapEig) for feature extraction is proposed first. Via combining preserving the consistency of local neighbor information and class labels information, S-LapEig can not only gain a perfect approximation of low-dimensional intrinsic geometric structure within the high-dimensional observation data, but also enhance local within-class relations. Based on S-LapEig, a novel faultdiagnosis approach is proposed. The approach extracts the intrinsic manifold features from high-dimensional fault data by directly learning the data, and translates complex mode space into a low-dimensional feature space, in which pattern classification and faultdiagnosis are carried out easily. Comparing with other feature extraction methods such as PCA, LDA and Laplacian eigenmaps, the proposed method obviously improves the classification performance of fault pattern recognition. The experiments on benchmark data and engineering instance demonstrate the feasibility and effectiveness of the new approach.
The rapid growth of the solar industry over the past several years has expanded the significance of photovoltaic (PV) systems. One of the primary aims of research in building-integrated PV systems is to improve the performance of the system's efficiency, availability, and reliability. Although much work has been done on technological design to increase a photovoltaic module's efficiency, there is little research so far on faultdiagnosis for PV systems. Faults in a PV system, if not detected, may not only reduce power generation, but also threaten the availability and reliability, effectively the "security" of the whole system. In this paper, first a circuit-based simulation baseline model of a PV system with maximum power point tracking (MPPT) is developed using MATLAB software. MATLAB is one of the most popular tools for integrating computation, visualization and programming in an easy-to-use modeling environment. Second, data collection of a PV system at variable surface temperatures and insolation levels under normal operation is acquired. The developed simulation model of PV system is then calibrated and improved by comparing modeled I-V and P-V characteristics with measured I--V and P--V characteristics to make sure the simulated curves are close to those measured values from the experiments. Finally, based on the circuit-based simulation model, a PV model of various types of faults will be developed by changing conditions or inputs in the MATLAB model, and the I--V and P--V characteristic curves, and the time-dependent voltage and current characteristics of the fault modalities will be characterized for each type of fault. These will be developed as benchmark I-V or P-V, or prototype transient curves. If a fault occurs in a PV system, polling and comparing actual measured I--V and P--V characteristic curves with both normal operational curves and these baseline fault curves will aid in faultdiagnosis.
By incorporating digraph models, fault trees and fuzzy inference mechanisms in a unified framework, a novel approach for faultdiagnosis is developed in this work. To relieve the on-line computation load, the fault origins considered in diagnosis are limited to the basic events in the cut sets of a given fault tree. The symptom occurrence order associated with each root
One of the issues that impair the performance of aircraft engine faultdiagnosis is the flight regime. When an aircraft travels from one point to another in flight regime, engine performance parameters that are used for fault diagnosing change and such changes mask the parameter changes caused by engine faults, thus make the engine faultdiagnosis much more difficult. Properly
A faultdiagnosis technique for gearbox that uses a wavelet-AR model spectrum estimation method is presented in this paper. In the experimental work, the wavelet transform was used for original signal decomposition and de-noising to obtain fault signals, and the fault type was confirmed using AR model spectrum estimation method for gearbox fault signal diagnosis. The experimental results indicated that
Gui-Hong Zhou; Chun-Cheng Zuo; Jia-Zhong Wang; Shu-Xia Liu
The marine diesel engine is a complex system. Its mapping process of faultdiagnosis has multi-fault attributes, which means input and output of fault pattern attribute are the multi-mapping relations. An approach of intelligent faultdiagnosis using fuzzy neural networks and genetic algorithms to optimize and train is studied in this paper for this system. The structure and the model
This paper presents new techniques for locating and diagnosing faults on electric power distribution feeders. The proposed fault location and diagnosis scheme is capable of accurately identifying the location of a fault upon its occurrence, based on the integration of information available from disturbance recording devices with knowledge contained in a distribution feeder database. The developed fault location and diagnosis
Vehicle engine faults are serious faults that occur inside the engine, the ability to successfully perform faultdiagnosis is highly dependent on technician skills. Some experienced technicians have some failure rate, which can lead to serious waste in time and money. Accordingly, an improved diagnosing methods is highly needed. In this paper, we develop an algorithm for faultdiagnosis in
This paper presents genetic-based neural networks (GNNs) for faultdiagnosis of power transformers. The GNNs automatically tune the network parameters, connection weights and bias terms of the neural networks, to yield the best model according to the proposed genetic algorithm. Due to the global search capabilities of the genetic algorithm and the highly nonlinear mapping nature of the neural networks,
In order to diagnosis the complex airborne equipment faults with small samples and feebleness condition, a grey correlation fault tree identification method is proposed by combining the grey system theory with fault tree analysis method. Firstly, on the basis of the fault tree qualitative and quantitative analysis by using binary decision diagram (BDD), the standard fault modes are constructed based
Feature extraction is always a crucial step for faultdiagnosis in rotating machinery. When faults occur, rotating machinery always manifests nonlinear dynamic behavior. It is necessary to extract the nonlinear features hidden in the vibration signal for more accurate diagnosis. Approximate entropy (ApEn) is the nonlinear parameter identification method for measuring the irregularity of the stochastic signal or the stochastic process. In this paper, ApEn is used as a nonlinear feature parameter to measure the irregularity of the vibration signals for faultdiagnosis in rotating machinery. Four typical faults are considered, which are imbalance, misalignment, shaft rubbing and oil whirl. To improve the distinguishability of the ApEn values of the different faults, the empirical mode decomposition (EMD) method is used to remove the basic frequency component from the signals of the various faults. The experimental study results demonstrate that EMD can separate the basic frequency component from the original signals satisfactorily. After removing the basic frequency component, the distinguishability of the ApEn values of the residual signals is improved greatly. The proposed strategy for the ApEn calculation of the various faults is proved effective. In addition, the simulation study is presented to investigate some characteristics of ApEn, which will benefit better application of ApEn in the field of faultdiagnosis.
The faultdiagnosis model of steam turbine based on Bayesian network is direct impacts on the complexity of the diagnostic process, so the construction of Bayesian network model is the primary problem. According actual faultdiagnosis system of steam turbine containing redundancy and uncertain information, proposed attribute reduction method to fault feature, obtained the minimal diagnosis rules. Based on two-node
Multi-fault identification is a challenge for rotating machinery faultdiagnosis. The vibration signals measured from rotating machinery usually are complex, non-stationary and nonlinear. Especially, the useful multi-fault features are too weak to be identified at the early stage. In this paper, a novel method called improved EEMD with multiwavelet packet for rotating machinery multi-faultdiagnosis is proposed. Using multiwavelet packet as the pre-filter to improve EEMD decomposition results, multiwavelet packet decomposes the vibration signal into a series of narrow frequency bands and enhances the weak multi-fault characteristic components in the different narrow frequency bands. By selecting the proper added noise amplitude according to the vibration characteristics, EEMD is further improved to increase the accuracy and effectiveness of its decomposition results. The proposed method is applied to analyze the multi-fault of a blade rotor experimental setup and an industrial machine set, and the results confirm the advantage of the proposed method over EEMD, EEMD with multiwavelet packet, Hilbert-Huang transform and multiwavelet packet transform for multi-faultdiagnosis.
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 faultsdiagnosis are therefore critical to machinery normal operation. The use of mechanical vibration signals for faultdiagnosis 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-faultsdiagnosis 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 faultvibration 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.
An approximate-reasoning model for diagnosis of continuous dynamic systems is introduced based on a previously developed fuzzy extension of the fault tree analysis and synthesis approach. The concept of dynamic fuzzy fault tree naturally emerges from the act of matching the fuzzified fault tree with the dynamic symptoms. Management of the incipient fault dynamics via fuzzy information processing is illustrated
This paper presents an adaptive faultdiagnosis and accommodation scheme for aerodynamic actuators. The fault-tolerant control architecture consists of three main components: an online nonlinear fault detection and isolation scheme, a controller suite, and a reconfiguration supervisor which performs controller reconfiguration and control reallocation using online diagnostic information. The proposed scheme provides a unified architecture for fault detection, isolation and
Xiaodong Zhang; Marios M. Polycarpou; Roger Xu; Chiman Kwan
Intermittent scan chain hold-time fault is discussed in this paper and a method to diagnose the faulty site in a scan chain is proposed a s well. Unlike the previous scan chain diagnosis methods that targeted p ermanent faults only, the proposed method targets both permanent faults and intermittent faults. Three ideas are presented in this paper. First an enhanced
The paper utilizes ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for the faultdiagnosis of the reciprocating compressor on the offshore platform of WZ12-1, aiming at the non-stationary and nonlinear characteristics of vibration signals collected from the faulty compressor. First, the EEMD algorithm self-adaptively anti-aliasing decomposes the vibration signal into a set of intrinsic mode function of different
This paper presents DE\\/IFT, a new faultdiagnosis engine which is based on the authors' IFT algorithm for induction of fault trees. It learns from an examples database comprising sensor recordings, all of which have been classified as corresponding to either the normal behaviour of the system or to one or more fault states. The fault trees generated by IFT
Periodic impulses in vibration signals and its repeating frequency are the key indicators for diagnosing the localized damage of rolling element bearings. Traditional envelope spectrum is effective in faultdiagnosis of rolling element bearings, but it needs optimization of filter parameters which is complicated. A new method based on ensemble empirical mode decomposition (EEMD) and envelope spectral analysis is proposed
Based on the Morlet wavelet transformation and Wigner-Ville distribution (WVD), we present a wind turbine faultdiagnosis method in this paper. Wind turbine can be damaged by moisture absorption, fatigue, wind gusts or lightening strikes. Due to this reason, there is an increasing need to monitor the health of these structures. Vibration analysis is the best-known technology applied in wind
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 faultdiagnosis 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 faultdiagnosis 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 faultdiagnosis 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 faultdiagnosis and fault-tolerant control strategies. The faultdiagnosis 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 faultdiagnosis and fault-tolerant control of PMSM.
\\u000a A method based on fuzzy and support vector machine (SVM) is proposed to focus on the lack of samples in faultdiagnosis of\\u000a turbine. Typical fault symptoms firstly are normalized by the membership functions perceptively. Then some samples are used\\u000a to train SVM of faultdiagnosis. With the trained SVM, the correct fault type can be recognized. In the application
Fei Xia; Hao Zhang; Daogang Peng; Hui Li; Yikang Su
Bottom up self-assembly of nano-crossbars from carbon nano-tubes and semiconductor nano-wires has shown the potential to overcome the limitations of lithographic fabrication of CMOS for further down-scaling. However, very high permanent and transient fault rates necessitates the incorporation of efficient fault tolerance techniques, capable of handling multiple faults. Self repair provides fault tolerance through fault detection, diagnosis and reconfiguration to
This paper presents new techniques for locating and diagnosing faults on electric power distribution feeders. The proposed fault location and diagnosis scheme is capable of accurately identifying the location of a fault upon its occurrence, based on the integration of information available from disturbance recording devices with knowledge contained in a distribution feeder database. The developed fault location and diagnosis system can also be applied to the investigation of temporary faults that may not result in a blown fuse. The proposed fault location algorithm is based on the steady-state analysis of the faulted distribution network. To deal with the uncertainties inherent in the system modeling and the phasor estimation, the fault location algorithm has been adapted to estimate fault regions based on probabilistic modeling and analysis. Since the distribution feeder is a radial network, multiple possibilities of fault locations could be computed with measurements available only at the substation. To identify the actual fault location, a faultdiagnosis algorithm has been developed to prune down and rank the possible fault locations by integrating the available pieces of evidence. Testing of the developed fault location and diagnosis system using field data has demonstrated its potential for practical use.
Zhu, J. [Advanced Control Systems, Inc., Norcross, GA (United States); Lubkeman, D.L.; Girgis, A.A. [Clemson Univ., SC (United States). Dept. of Electrical and Computer Engineering
This paper introduces the design of an integrated framework for on-board faultdiagnosis and failure prognosis of a helicopter transmission component, and describes briefly its main modules. It suggests means to (1) validate statistically and pre-process sensor data (vibration), (2) integrate model-based diagnosis and prognosis, (3) extract useful features or condition indicators from data de-noised by blind deconvolution, and (4)
Romano Patrick; Marcos E. Orchard; Bin Zhang; Michael D. Koelemay; Gregory J. Kacprzynski; Aldo A. Ferri; George J. Vachtsevanos
Vibration testing is a viable method for structural faultdiagnosis. Different structural dynamic response variables and parameters that may be considered as candidate objects of interrogation are briefly reviewed. The faultdiagnosis process is broken down into three parts, and only the detection process is addressed in this paper. The frequency response function obtained by exciting the structure at a selected reference point is utilized as the preferred form of vibration signature to be used for interrogation. The chain code computer vision technique is modified to evaluate the frequency response function signature as a waveform. Signatures are obtained from a laboratory structure in the form of a ribbed plate, similar to a highway bridge. Cracks are simulated in the structure by cutting through splice plates with a jigsaw. By applying the computer vision technique on the signatures and comparing inspection signatures with benchmark signatures, small cracks are detected consistently. Results of the interrogation are interpreted in a graphical manner. In addition, an automated evaluation technique is presented. The robustness of the technique is verified by contaminating signals with synthetic noise. Successful performance of the technique in the presence of noise indicates the potential for faultdiagnosis of large outdoor structures.
Biswas, M.; Pandey, A. K.; Bluni, S. A.; Samman, M. M.
Bearings are commonly used in machine industry, and their faults may result in unexpected vibration and even cause breakdown of a whole rotating machine. This paper proposes a novel faultdiagnosis approach for bearings by using a sensitive feature decoupling technique. This approach does not require a training procedure as in machine learning methods and can classify the occurred faults by a simple algebraic computation. Firstly, the features of vibration signals which show the most significant difference under different bearing health conditions are selected and defined as sensitive features. Then those sensitive features under different health conditions are used to construct a feature matrix, and its left null space is computed to obtain the so-called feature decoupling vectors. The bearing faults are finally classified with the help of the decoupling vectors according to a simple decision logic. Since the obtained decoupling vectors may not be unique, we also propose an algorithm to select the optimal ones in order to improve the performance of faultdiagnosis. Experiments are carried out to test the proposed approach and the results show that the approach is feasible and effective for the faultdiagnosis of bearings.
In order to improve the robustness and recovering capability of the Web service, the faultdiagnosis becomes the key technology in the Web service management. On the basis of analyzing the features of the Web service fault, this paper proposes a Web service fault taxonomy that combines the Web service type with the implementation process and constructs a Web service
Liu Li; Kuang Xiaohui; Li Yuanling; Xu Fei; Zou Tao; Cui Yimin
A faultdiagnosis method of direct-drive wind turbine based on support vector machine (SVM) and feature selection is presented. The time-domain feature parameters of main shaft vibration signal in the horizontal and vertical directions are considered in the method. Firstly, in laboratory scale five experiments of direct-drive wind turbine with normal condition, wind wheel mass imbalance fault, wind wheel aerodynamic imbalance fault, yaw fault and blade airfoil change fault are carried out. The features of five experiments are analyzed. Secondly, the sensitive time-domain feature parameters in the horizontal and vertical directions of vibration signal in the five conditions are selected and used as feature samples. By training, the mapping relation between feature parameters and fault types are established in SVM model. Finally, the performance of the proposed method is verified through experimental data. The results show that the proposed method is effective in identifying the fault of wind turbine. It has good classification ability and robustness to diagnose the fault of direct-drive wind turbine.
The method of de-noising by wavelet transformation and the basic theory of wavelet transformation based on threshold de-noising are introduced in this paper, the characteristics of noise under the wavelet decomposition are discussed, and the gear faultdiagnosis of a gearbox is studied through the wavelet analysis. Experiment result demonstrates that this method can remove the strong noise and extract
Yanping Cai; Yanping He; Aihua Li; Jinru Zhao; Tao Wang
A method of faultdiagnosis was proposed for power electronics circuits based on S transforms similarity. At first, the standard module time-frequency matrixes of S transforms for all fault signals were constructed, then the similarity of fault signals' module time-frequency matrixes to standard module time-frequency matrixes were calculated, and according to the principle of maximum similarity, the faults were diagnosed. The simulation result of faultdiagnosis of a thyristor in a three-phase full-bridge controlled rectifier shows that the method can accurately diagnose faults and locate the fault element for power electronics circuits, and it has excellent performance for noise robustness and calculation complexity, thus it also has good practical engineering value in the solution to the fault problems for power electronics circuits.
In this paper, the Wigner Ville distributions (WVD) of vibration acceleration signals which were acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images; and the probabilistic neural networks (PNN) were directly used to classify the time frequency images after the images were normalized. By this way, the faultdiagnosis of valve train was transferred to the classification of time frequency images. As there is no need to extract further fault features (such as eigenvalues or symptom parameters) from time frequency distributions before classification, the faultdiagnosis process is highly simplified. The experimental results show that the faults of diesel valve trains can be classified accurately by the proposed methods.
Position sensor is the key of exact commutation for brushless motor. The commutation information is incorrect when position sensor is faulted. And the output torque is reduced, and the speed is decreased or zero. The study on faultdiagnosis and fault tolerant control of position signal is very necessary in order to increase system reliability. The method of real-time monitoring
A fault detection and diagnosis (FDD) and a fault-tolerant control (FTC) system for an unmanned aerial vehicle (UAV) subject to control surface failures are presented. This FDD\\/FTC technique is designed considering the following constraints: the control surface positions are not measured and some actuator faults are not isolable. Moreover, the aircraft has an unstable spiral mode and offers few actuator
In this paper we present the comparison results of induction motor fault detection using stator current, vibration, and acoustic methods. A broken rotor bar fault and a combination of bearing faults (inner race, outer race, and rolling element faults) were induced into variable speed three-phase induction motors. Both healthy and faulty signatures were acquired under different speed and load conditions.
In this work the principle of observer-based sensor fault detection and isolation is improved by the use of optimal parity relations. A parity check is performed on the obser- vation errors such that even in the case of multiple simultaneous sensor faults correct fault detection, isolation and identification can be achieved. In contrast to previous observer-based approaches for sensor-fault-diagnosis the
Physical defects cause behaviors unmodeled by even the best fault simulators, which complicates predictive diagnosis. This paper reports on a diagnosis procedure that uses modified composite signatures constructed from single stuck-at information combined with a lexicographic matching and ranking algorithm. The diagnosis procedure is used to perform high-quality bridging faultdiagnosis for more than 400,000 diagnostic experiments involving dropping or
In an increasingly competitive marketplace system complexity continues to grow, but time-to-market and lifecycle are reducing. The purpose of faultdiagnosis is the isolation of faults on defective systems, a task requiring a high skill set. This has driven the need for automated diagnostic tools. Over the last two decades, automated diagnosis has been an active research area, but the
William G. Fenton; T. Martin Mcginnity; Liam P. Maguire
This paper introduces artificial intelligence system method and describes the developing and application in transformer faultdiagnosis. An artificial intelligent system (TFDAI) design includes selection of input, network topology, synaptic connection weights, two-passageway, and output. This paper introduces the new intelligence technology in the transformer faultdiagnosis -TFDAI System. TFDAI based data processing and diagnostic techniques are described in detail.
A Hilbert-Huang transform (HHT) is a time-frequency technique and has been widely applied to analyzing vibration signals in the field of faultdiagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate this problem. The IMFs generated by EEMD have different sensitivity to faults. Some IMFs are sensitive and closely related to the faults but others are irrelevant. To enhance the accuracy of the HHT in faultdiagnosis of rotating machinery, an improved HHT based on EEMD and sensitive IMFs is proposed in this paper. Simulated signals demonstrate the effectiveness of the improved HHT in diagnosing the faults of rotating machinery. Finally, the improved HHT is applied to diagnosing an early rub-impact fault of a heavy oil catalytic cracking machine set, and the application results prove that the improved HHT is superior to the HHT based on all IMFs of EMD.
Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for faultdiagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the 'known-fault' classes while the low similarity points are labeled as 'unknown-faults'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%. PMID:22035775
Reversible logic is a promising design method- ology, particularly in the scope of quantum computing, for extremely low power consumption by elimination of power dissipation due to information loss. Anticipated high fault rates for future technologies raise demand for fault tolerance in reversible logic. In this paper we propose fault masking techniques (to prevent error propagation) for reversible logic. We
The problem of fault detection and location in tree networks of two input EXCLUSIVE-OR (EOR) gates is considered. The fault model assumes that an EOR gate can change to any other function of its two inputs except the equivalence function. An efficient procedure for single fault location is presented. In the worst case the number of tests necessary to locate
For asynchronous induction motor, it is necessary to carry out faultdiagnosis in time. The traditional faultdiagnosis methods have the shortcomings such as the diagnosis slow speed, low accuracy. In this paper, for the common fault characteristics of asynchronous induction motor, the faultdiagnosis method based on improved BP algorithm, by using of the diagnosis model, is adopted to
A signal analysis technique for bearing faultdiagnosis based on ensemble empirical mode decomposition (EEMD) and Hilbert-Huang transform (HHT) is presented. EEMD can adaptively decompose vibration signal into a series of zero mean Amplitude Modulation-Frequency Modulation (AM-FM) Intrinsic Mode Functions (IMFs) without mode mixing. Hilbert transform tracks the modulation energy of the interesting Intrinsic Mode Functions (IMFs) and estimates the
With study on fault tree analysis (FTA) and bidirectional associative memory (BAM) neural network, a new method of intelligent faultdiagnosis is proposed. All the knowledge on the happening of top events is stored in fault tree, in which the whole fault modes are obtained. The priori knowledge and experience of system diagnosis are introduced to FTA. The learning sample
A new statistical online diagnosis method for a batch process is proposed. The proposed method consists of two phases: offline model building and online diagnosis. The offline model building phase constructs an empirical model, called a discriminant model, using various past batch runs. When a fault of a new batch is detected, the online diagnosis phase is initiated. The behaviour
According to asynchronous motor's complex fault characteristics, and the combination of wavelet transform technique, an improved wavelet neural network for faultdiagnosis of asynchronous motor is proposed in this paper. Taking wavelet transform technique as wavelet neural network (WNN) the input vector of picking up asynchronous motor's the characteristic signal, and wavelet neural network algorithm is optimized, The self-adaptive wavelet
Recently, research has picked up a fervent pace in the area of faultdiagnosis 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 paper presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. A two-step ANN method is used to detect faults with or without cellulose involved. Good diagnosis accuracy is obtained with the proposed approach.
Detection and diagnosis of faults has become a critical issue in high performance engineering systems as well as in mass-produced equipment. It is particularly helpful when the diagnosis can be made at the initial design level with respect to a prospective fault list. A number of powerful methods have been developed for aiding in the general fault analysis of designs. Catastrophic faults represent the limit case of complete local failure of connections or components. They result in the interruption of energy transfer between corresponding points in the system. In this work the conventional approach to fault detection and diagnosis is extended by means of bond-graph methods to a wide variety of engineering systems. Attention is focused on catastrophic faultdiagnosis. A catastrophic fault dictionary is generated from the system model based on topological properties of the bond graph. The dictionary is processed by existing methods to extract a catastrophic fault report to aid the engineer in performing a design analysis.
Faults in automotive systems significantly degrade the performance and efficiency of vehicles, and often times are the major causes of vehicle break-down leading to large expenditure for repair and maintenance. An intelligent faultdiagnosis system can ensure uninterrupted and reliable operation of vehicular systems, and aid in vehicle health monitoring. Due to cost constraints, the current electronic control units (ECUs)
Kihoon Choi; Jianhui Luo; K. R. Pattipati; S. M. Namburu; Liu Qiao; S. Chigusa
Faultdiagnosis has particular importance in the context of field programmable gate arrays (FPGAs) because faults can be avoided by reconfiguration at almost no real cost. Cluster-based FPGA architectures, in which several logic blocks are grouped together into a coarse-grained logic block, are rapidly becoming the architecture of choice for major FPGA manufacturers. The high density interconnect found within clusters
Faultdiagnosis has particular importance in the context of field programmable gate arrays (FPGAs) because faults can be avoided by reconfiguration at almost no real cost. Cluster-based FPGA architectures, in which several logic blocks are grouped together into a coarse-grained logic block, are rapidly becoming the architecture of choice for major FPGA manufacturers. The high density interconnect found within clusters
An experimental verification of the influence matrix approach to faultdiagnosis and automatic tuning is proposed. The coefficients of the system transfer function are identified and the Jacobian of the coefficient vector with respect to physical parameters, termed the influence matrix, is computed. The influence matrix is used to detect and isolate faults. The optimal controller parameters are obtained using
This paper presents a new and efficient integrated neural fuzzy approach for transformer faultdiagnosis using dissolved gas analysis. The proposed approach formulates the modeling problem of higher dimensions into lower dimensions by using the input feature selection based on competitive learning and neural fuzzy model. Then, the fuzzy rule base for the identification of fault is designed by applying
A microcomputer system is introduced for condition monitoring and faultdiagnosis in the ground testing of a throttling liquid rocket engine. The system presented is capable of monitoring all conditions of the static and dynamic performance parameters of the tested engine under each condition. According to the original data and curves provided by the monitoring system, it is possible to realize faultdiagnosis for the throttling liquid rocket engine system in ground testing. The corresponding testing data and curves are presented.
For pt.I see Crossman, J.A. et al., ibid., p.1063-75. We describe a novel diagnostic architecture, distributed diagnostics agent system (DDAS), developed for automotive faultdiagnosis. The DDAS consists of a vehicle diagnostic agent and a number of signal diagnostic agents, each of which is responsible for the faultdiagnosis of one particular signal using either a single or multiple signals,
Yi Lu Murphey; Jacob A. Crossman; ZhiHang Chen; John Cardillo
This paper focuses on the application of self-organizing maps (SOM) in motor bearing faultdiagnosis and presents an approach for motor rolling bearing faultdiagnosis using SOM neural networks and time\\/frequency-domain bearing analysis. The SOM is a neural network algorithm which is based on unsupervised learning and combines the tasks of vector quantization and data projection. The objective of this
A study is made of the design of fault-tolerant array processors. It is shown how hardware redundancy can be used in the existing structures in order to make them capable of withstanding the failure of some of the array links and processors. Distributed fault-tolerance schemes are introduced for the diagnosis of the faulty elements, reconfiguration, and recovery of the array.
This paper explores the improved time-scale representation by considering the non-linear property for effectively identifying rotating machine faults in the time-scale domain. A new time-scale signature, called time-scale manifold (TSM), is proposed in this study through combining phase space reconstruction (PSR), continuous wavelet transform (CWT), and manifold learning. For the TSM generation, an optimal scale band is selected to eliminate the influence of unconcerned scale components, and the noise in the selected band is suppressed by manifold learning to highlight the inherent non-linear structure of faulty impacts. The TSM reserves the non-stationary information and reveals the non-linear structure of the fault pattern, with the merits of noise suppression and resolution improvement. The TSM ridge is further extracted by seeking the ridge with energy concentration lying on the TSM signature. It inherits the advantages of both the TSM and ridge analysis, and hence is beneficial to demodulation of the fault information. Through analyzing the instantaneous amplitude (IA) of the TSM ridge, in which the noise is nearly not contained, the fault characteristic frequency can be exactly identified. The whole process of the proposed faultdiagnosis scheme is automatic, and its effectiveness has been verified by means of typical faulty vibration/acoustic signals from a gearbox and bearings. A reliable performance of the new method is validated in comparison with traditional enveloping methods for rotating machine faultdiagnosis.
In this paper, a new method is proposed by combining ensemble empirical mode decomposition (EEMD) with order tracking techniques to analyse the vibration signals from a two stage helical gearbox. The method improves EEMD results in that it overcomes the potential deficiencies and achieves better order spectrum representation for faultdiagnosis. Based on the analysis, a diagnostic feature is designed
Luyang Guan; Yimin Shao; Fengshou Gu; Bruno Fazenda; Andrew Ball
Recently, the issue of machine condition monitoring and faultdiagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and faultdiagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and faultdiagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and faultdiagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and faultdiagnosis using SVM will be future works.
A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear faultdiagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faultsdiagnosis. Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox. The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum, and the ANN classification method has achieved high detection accuracy. Hence, the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases, and thus have application importance.
Recent developments in Artificial Intelligence (AI) have resulted in newer approaches using knowledge-based expert systems to problems in the design of automated process fault diagnostic systems. Despite the advantages offered by these first-generation systems over conventional methods such as fault tree analysis and signed digraphs there are some serious drawbacks. Owing to their complete reliance on heuristic or experiential knowledge, the first-generation systems are not flexible to accommodate even small changes in process configuration and are incapable of diagnosing unanticipated fault combinations. In this paper, the authors discuss a methodology that aids the development of expert systems which are process-independent, transparent in their reasoning, and capable of diagnosing a wide diversity of faults. A prototype expert system, called MODEX, has been implemented incorporating these ideas. The domain knowledge of the system is based on qualitative reasoning principles and captures physical interconnections between equipment units as well as casual relationships among process state variables. The inference strategy uses model-based reasoning for analyzing the plant behavior. Using a variant of the technique adopted from fault tree synthesis, an initially observed abnormal symptom is considered to be a top level event and a tree structure is constructed as the system searches for a basic event to which the fault can be traced. The diagnostic reasoning process is driven by a problem reduction strategy. The knowledge base is process-independent, thereby enhancing the generality of the expert system. Reasoning from first-principles with the aid of causal and fault models facilitates the diagnoses of novel or unanticipated faults.
Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent faultdiagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.
A new combined faultdiagnosis approach for turbo-generator set based on wavelet fuzzy network is proposed. The wavelet transform is used to extract fault characteristics and neural network is used to diagnose the faults. To improve the performance of applying traditional faultdiagnosis method to the vibrant faults, a novel method based on the statistic rule is brought forward to
Kang Shanlin; Pang Peilin; Fan Feng; Ding Guangbin
The faultdiagnosis of a marine's main engine is a significant but complicated problem, and artificial intelligence has been considered in this field for decades. This paper describes a faultdiagnosis method for main engine based on BP neural network with the system fault classified into hierarchies according to fault tree analysis. Sample data of supercharger's fault is collected and
The faultdiagnosis theory and its methods for inductor motor are summarized. Based on the method of current spectrum, a neural network method to diagnose the broken bar number of inductor motor is presented. The training patterns and the diagnosis results for the neural network are given. The broken bar number of inductor motor is diagnosed directly according to the
A novel approach for faultdiagnosis of rotating machine based on thermal image investigation using image histogram features is proposed in this paper. Herein, the machine learning and statistical approach are adopted along with thermal image signal to machine condition diagnosis. Using thermal images, the information of machine condition can be investigated more simply than other conventional methods of machine
Vibration signal analysis is the main technique in machine condition monitoring or faultdiagnosis, 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 faultdiagnosis. 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 faultdiagnosis, 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.
Gearbox faultdiagnosis 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 faultdiagnosis, 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.
A coupled rotor-fuselage vibration analysis for helicopter rotor system fault detection is developed. The coupled rotor/fuselage/vibration absorbers (bifilar type) system incorporates consistent structural, aerodynamic and inertial couplings. The aeroelastic analysis is based on finite element methods in space and time. The coupled rotor, absorbers and fuselage equations are transformed into the modal space and solved in the fixed coordinate system. A coupled trim procedure is used to solve the responses of rotor, fuselage and vibration absorber, rotor trim control and vehicle orientation simultaneously. Rotor system faults are modeled by changing blade structural, inertial and aerodynamic properties. Both adjustable and component faults, such as misadjusted trim-tab, misadjusted pitch-control rod (PCR), imbalanced mass and pitch-control bearing freeplay, are investigated. Detailed SH-60 helicopter fuselage NASTRAN model is integrated into the analysis. Validation study was performed using SH-60 helicopter flight test data. The prediction of fuselage natural frequencies show fairly large error compared to shake test data. Analytical predictions of fuselage baseline (without fault) 4/rev vibration and fault-induced 1/rev vibration and blade displacement deviations are compared with SH-60 flight test (with prescribed fault) data. The fault-induced 1/rev fuselage vibration (magnitude and phase) predicted by present analysis generally capture the trend of the flight test data, although prediction under-predicts. The large discrepancy of fault-induced 1/rev vibration magnitude at hover between prediction and flight test data partially comes from the variation of flight condition (not perfect hover) and partially due to the effect of the rotor-fuselage aerodynamic interaction (wake effect) at low speed which is not considered in the analysis. Also the differences in the phase prediction is not clear since only the magnitude and phase information were given instead of the original vibration time-history. The imbalanced mass fault causes higher 1/rev roll vibration that is insensitive to the airspeed. The misadjusted trim-tab fault induced 1/rev vertical vibration increases with airspeed. The misadjusted pitch-control rod fault causes high vibration at hover. A parametric study was conducted to identify key factors that affect the fault-induced fuselage vibration. Analysis show that elastic fuselage model and precise hub modeling (inclusion of vibration absorbers) are essential to the vibration pre diction. The analysis shows that a compound fault can be expressed as a linear combination of individual faults involved. Aircraft operational parameters, such as gross-weight; center of gravity location, flight speed, flight path and aircraft configuration, have significant impact on the fault-induced 1/rev vibration. Prediction show that there are certain patterns in the fault-induced 1/rev hub-loads. Thus measuring both fuselage vibration and hub loads may benefit rotor system fault detection.
This paper is concerned with the application of fuzzy neural networks to faultdiagnosis systems for rotary machines. In practical faultdiagnosis, it is very difficult to improve the recognition rate of pattern recognition, especially when the sample data are similar. To solve these difficulties, a faultdiagnosis system using fuzzy neural networks is proposed in this research. A fault
Theory and applications of model-based faultdiagnosis have progressed significantly in the last four decades. In addition, there has been increasing use of model-based design and testing in automotive industry to reduce design errors, perform real-time simulations for rapid prototyping, and hardware-in-the-loop testing. For vehicle diagnosis, a global diagnosis method, which collects the diagnostic information from all the subsystem electronic
Jianhui Luo; Krishna R. Pattipati; Liu Qiao; S. Chigusa
\\u000a A hybrid faultdiagnosis method is proposed in this paper which is based on analytical and fuzzy logic theory. Analytical\\u000a redundancy is employed by using statistical analysis. Fuzzy logic is then used to maximize the signal- to-threshold ratio\\u000a of the residual and to detect different faults. The method was successfully demonstrated experimentally on hydraulic actuated\\u000a system test rig. Real data
Seraphin C. Abou; Manali Kulkarni; Marian Stachowicz
This paper introduces a novel approach for fault diag- nosis based on probabilistic models. This approach is suitable for applications where reliable measurements are unlikely to occur or where a deterministic analyt- ical model is difficult to obtain. In particular, a com- bination of two Bayesian networks is used to detect and isolate faulty components. One Bayesian network, representing a
Pablo H. Ibarguengoytia; L. Enrique Sucar; Eduardo Morales
Kurtogram, due to the superiority of detecting and characterizing transients in a signal, has been proved to be a very powerful and practical tool in machinery faultdiagnosis. Kurtogram, based on the short time Fourier transform (STFT) or FIR filters, however, limits the accuracy improvement of kurtogram in extracting transient characteristics from a noisy signal and identifying machinery fault. Therefore, more precise filters need to be developed and incorporated into the kurtogram method to overcome its shortcomings and to further enhance its accuracy in discovering characteristics and detecting faults. The filter based on wavelet packet transform (WPT) can filter out noise and precisely match the fault characteristics of noisy signals. By introducing WPT into kurtogram, this paper proposes an improved kurtogram method adopting WPT as the filter of kurtogram to overcome the shortcomings of the original kurtogram. The vibration signals collected from rolling element bearings are used to demonstrate the improved performance of the proposed method compared with the original kurtogram. The results verify the effectiveness of the method in extracting fault characteristics and diagnosing faults of rolling element bearings.
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.
Impulsive sound and vibration signals in machinery are often caused by the impacting of components and are commonly associated with faults. It has long been recognized that these signals can be gainfully used for fault detection. However, it tends to be difficult to make objective measurements of impulsive signals because of the high levels of background noise. This paper presents
Automation of machine faultdiagnosis 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 faultdiagnosis 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 faultdiagnosis 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 faultdiagnosis 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 faultdiagnosis 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.
This paper provides a method based on Dezert-Smarandache theory (DSmT) for simultaneous faultsdiagnosis when evidence is dependent. Firstly, according to the characteristics of simultaneous faults, a frame of discernment is given for both single fault and simultaneous faultsdiagnosis, the DSmT combination rule applicable to simultaneous faultsdiagnosis is introduced. Secondly, the dependence of original evidence is classified according
The application of a new method for faultdiagnosis in an automotive diesel engine is presented. Two common types of fault are investigated: (i) sensor fault, caused by a bias in the inlet manifold pressure sensor and (ii) process fault, caused by small air leaks in the inlet manifold plenum chamber. Such faults may lead to increased emission levels which,
Induction motors are the most commonly used electrical drives, ranging in power from fractional horsepower to several thousand horsepowers. Several studies have been conducted to identify the cause of failure of induction motors in industrial applications. Recent activities indicate a focus towards building intelligence into the motors, so that a continuous on-line faultdiagnosis and prognosis may be performed. The
As a complicated mechanical component, gearbox plays a significant role in industrial field. Its faultdiagnosis benefits decision making of maintenance and avoids undesired downtime cost. Empirical mode decomposition (EMD) is a self-adaptive signal processing method, which has been applied in non linear and non stationary signal processing successfully. However, the EMD algorithm has its inherent drawbacks. Aiming at the
Faulty automotive systems significantly degrade the performance and efficiency of vehicles, and oftentimes are the major contributors of vehicle breakdown; they result in large expenditures for repair and maintenance. Therefore, intelligent vehicle health-monitoring schemes are needed for effective faultdiagnosis in automotive systems. Previously, we developed a data-driven approach using a data reduction technique, coupled with a variety of classifiers,
Kihoon Choi; Satnam Singh; Anuradha Kodali; Krishna R. Pattipati; John W. Sheppard; Setu Madhavi Namburu; S. Chigusa; D. V. Prokhorov; Liu Qiao
Faulty automotive systems significantly degrade the performance and efficiency of vehicles, and oftentimes are the major contributors of vehicle breakdown; they result in large expenditures for repair and maintenance. Therefore, intelligent vehicle health-monitoring schemes are needed for effective faultdiagnosis in automotive systems. Previously, we developed a data- driven approach using a data reduction technique, coupled with a variety of
Kihoon Choi; Satnam Singh; Anuradha Kodali; Krishna R. Pattipati; John W. Sheppard; Setu Madhavi Namburu; Shunsuke Chigusa; Danil V. Prokhorov; Liu Qiao
Purpose – Fixture failures are the main cause of the dimensional variation in the assembly process. The purpose of this paper is to focus on the optimal sensor placement of compliant sheet metal parts for the fixture faultdiagnosis. Design\\/methodology\\/approach – Based on the initial sensor locations and measurement data in launch time of the assembly process, the Bayesian network
Yinhua Liu; Sun Jin; Zhongqin Lin; Cheng Zheng; Kuigang Yu
A method for the incipient faultdiagnosis of industrial robot mechanics is proposed. It is based on mathematical models expressed in terms of nonlinear differential equations for a robot's different axes. The parameters of these models directly represent characteristic physical quantities (process coefficients), which are calculated by a suitable parameter estimation procedure. Additionally, a simple but efficient approach to the
The authors deal with a faultdiagnosis technique, particularly suitable for power electrical devices, based on the integration of simulation and identification methods. The device under analysis was simulated in faultless and faulty conditions and the experimental validation was carried out. Both the system parametric \\
A. Bernieri; G. Betta; C. De Capua; A. Pietrosanto
Design and development of faultdiagnosis schemes (FDS) for electric power distribution systems are major steps in realizing the self-healing function of a smart distribution grid. The application of the FDS in the electric power distribution systems is mainly aimed at precise detecting and locating of the deteriorated components, thereby enhancing the quality and reliability of the electric power delivered
Shahram Kazemi; Matti Lehtonen; Mahmud Fotuhi-Firuzabad
This paper proposes a feature extraction method based on information theory for faultdiagnosis 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.
This paper proposes a feature extraction method based on information theory for faultdiagnosis 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
The increasing need for the design of high-performance computers has led to the design of special purpose computers such as array processors. This paper studies the design of fault-tolerant array processors. First, it is shown how hardware redundancy can be employed in the existing structures in order to make them capable of withstanding the failure of some of the array links and processors. Then distributed fault-tolerance schemes are introduced for the diagnosis of the faulty elements, reconfiguration, and recovery of the array. Fault tolerance is maintained by the cooperation of processors in a decentralized form of control without the participation of any type of hardcore or fault-free central controller such as a host computer.
A single diagnostic agent is difficult in solving real world problems. In this paper, we will discuss integrated distributed faultdiagnosis system. Based on analyzing the failure mechanism actuation system of hydro turbine governors, a new approach combination of analyzing these trees qualitatively is offered with the aid of the tree's minimum cut-set matrix and model-based methods. The procedure is
This paper is concerned with the application of fuzzy neural networks to faultdiagnosis systems for rotary machines. In practical faultdiagnosis, it is very difficult to improve the recognition rate of pattern recognition, especially when the sample data are similar. To solve these difficulties, a faultdiagnosis system using fuzzy neural networks is proposed in this research. A faultdiagnosis system with fuzzy neural networks is based on a series of standard fault pattern pairings between fault symptoms and fault. Fuzzy neural networks are trained to memorize these standard pattern pairs. Unlike other neural networks, fuzzy neural networks adopt bi-directional association. They make use of information from both the fault symptoms and the fault patterns, which can improve recognition rate greatly. When an unknown sample becomes the input for a trained faultdiagnosis system, the faultdiagnosis system can make faultdiagnosis by bi-directional association of fuzzy neural networks. Through experiments with a rotor testing table and applications in monitoring and faultdiagnosis of water pump sets of oil plant, it is verified that fuzzy neural networks have a well distinguished ability and are effective to perform faultdiagnosis of rotary machines.
The multi-sensor information fusion theory has been widely used in the faultdiagnosis domain. In order to improve the reliability of faultdiagnosis of the variable frequency speed regulation system (VFSRS), this paper presents a new faultdiagnosis method of the VFSRS based on the multi-sensor information fusion. A calculating method of the basic probability assignment (BPA) for the VFSRS
The system described in this paper addresses the fault detection/diagnosis aspects of spacecraft electrical power system management. The Multiple Fault Diagnostic System (MFDS) project is a software implementation of actions performed at a typical ground segment operations center used to control and manage spacecraft subsystem operations. The MFDS software includes many concepts from the artificial intelligence research community including: model based reasoning, distributed knowledge bases, qualitative reasoning, and an assumption based truth maintenance system (ATMS). It is projected that software systems using techniques similar to those described herein will play an increasing role in spacecraft operations on future missions. It is expected that designs such as these will greatly enhance the reliability of ground operations decisions and operational availability of the controlled subsystems. The system is being implemented in Smalltalk/V on a PC clone platform. The model, fault detection system and user interface are now complete. The diagnostic system/ATMS and other MFDS components are now being implemented.
Timely and accurate condition monitoring and faultdiagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM.
A practical approach for diagnosis of analog circuits with focus on implementation systems is presented. A fault catalog, which allows a transparent access to the knowledge base, was developed using hierarchical error simulations, assuming a modular structure of the system to be diagnosed. This hierarchical dictionary contains references between the fault cause, some defective circuit components and their interaction, a state at the circuit interface which differs from the nominal behavior and measurable variations of the node voltages inside the circuit. Each fault is classified in a hierarchical structure, regarding its effect on the circuit, the immediate effect at the interface of circuit components and the effect on circuit functionalities. An integration of external knowledge such as accident statistical data from production final controls was made possible. A learning algorithm was added for future correction and enhancement of fault catalog. Object oriented knowledge representation and programming were used for implementing the system. A special expert system shell was implemented for testing the concept, with comfortable system interfaces. Passive systems are shown to be successfully diagnosed, but stability problems appear to be reinforced by fault simulation, because their efficiency strongly depends on individual properties of isolated circuits.
In this paper, a new vibration signal processing method, an adaptive narrow-band interference cancellation is developed to remove the periodic signals and background noises from the vibration signals. Narrow-band interference cancellation techniques are widely applied in signal processing of communication systems to remove the narrow-band interferences. The vibration signals of a gearbox with a damaged gear tooth contain periodic signals
The structural layers and methods of multi-sensor information fusion technology are analysed, and its application in faultdiagnosis of hydraulic system is discussed. Aiming at hydraulic system, a model of hydraulic faultdiagnosis system based on multi-sensor information fusion technology is presented. Choosing and implementing the method of information fusion reasonably, the model can fuse and calculate various fault characteristic parameters in hydraulic system effectively and provide more valuable result for faultdiagnosis of hydraulic system.
Zhang, L. Q.; Yang, G. L.; Zhang, L. G.; Zhang, S. Y.
A new method of faultdiagnosis based on feature weighted FCM is presented. Feature-weight assigned to a feature indicates the importance of the feature. This paper shows that an appropriate assignment of feature-weight can improve the performance of fuzzy c-means clustering. Feature evaluation based on class separability criterion is discussed in this paper. Experiment shows that the algorithm is able
Current spacecraft health monitoring and fault- diagnosis practices involve around-the-clock limit-checking and trend analysis on large amount of telemetry data. They do not scale well for future multiplatform space missions due the size of the telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the operations team personnel. The need for efficient utilization of telemetry
The diagnosis of faults in a first order ?-?converter is described. The circuit behaviour of fault-free circuits and circuits containing single faults were simulated and characterized by the output bitstream patterns. The latter were compared with that of the ideal fault-free circuit. A Simplified fuzzy ARTMAP was trained with metrics derived from the bitstreams and their assigned class. A diagnostic
In this paper, an effective fault location algorithm and intelligent faultdiagnosis scheme are proposed. The proposed scheme first identifies fault locations using an iterative estimation of load and fault current at each line section. Then an actual location is identified, applying the current pattern matching rules. If necessary, comparison of the interrupted load with the actual load follows and
Sensor faultdiagnosis 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
Faultdiagnosis of induction motor is gaining importance in industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. Due to environmental stress and many others reasons different faults occur in induction motor. Many researchers proposed different techniques for fault detection and diagnosis. However, many techniques available presently require a good
Heating, Ventilation and Air Conditioning (HVAC) systems constitute the largest portion of energy consumption equipment in residential and commercial facilities. Real-time health monitoring and faultdiagnosis is essential for reliable and uninterrupted operation of these systems. Existing fault detection and diagnosis (FDD) schemes for HVAC systems are only suitable for a single operating mode with small numbers of faults, and
Setu M. Namburu; Jianhui Luo; Mohammad Azam; Kihoon Choi; Krishna R. Pattipati
Network fault management is an important part of network management. The paper applies immunological principles and agent technology to the network faultdiagnosis. The structure of an immune agent is developed, the network faultdiagnosis system is built, and the immune agent workflow is described. To verify the feasibility of the system, some simulations are carried out, and Experimental results
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
The fault reasoning and diagnosis are carried out by means of analysis on the contents of CO, HC, CO2 and O2 in the exhaust gas discharged from gasoline engine. The support vector machine is applied to faultdiagnosis of the exhaust gas discharged from gasoline engine. Taken the fault data recorded the exhaust gas discharged from gasoline engine as the
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 faultdiagnosis, based on genetic algorithms (GAs) has been developed to resolve this problem. This NPP faultdiagnosis method combines GAs and classical probability with an expert
The authors set forth a modeling and fault information enhancing method based on faultdiagnosis of armored gearbox. Based on introducing the necessary on faultdiagnosis and prediction of the armored vehicles gearbox, the modeling and identification of armored vehicles gearbox is completed by using the forward householder real (FHR) algorithm. The concrete modeling process includes the test and pre-measured
In this paper, a CMAC (cerebellar model articulation controller) neural network application on faultdiagnosis for water circulation system is proposed. Firstly, we build a CMAC neural network based diagnosis system depending on the fault types. Secondly, the fault patterns, obtained from the China scholar's technical data, would be employed to train the CMAC neural network off-line. Thirdly, the learning
A new design approach for faultdiagnosis is developed for next generation nuclear power plants. In the nuclear reactor design phase, data reconciliation is used as an efficient tool to determine the measurement requirements to achieve the specified goal of faultdiagnosis. In the reactor operation phase, the plant measurements are collected to estimate uncertain model parameters so that a high fidelity model can be obtained for faultdiagnosis. The proposed algorithm of fault detection and isolation is able to combine the strength of first principle model based faultdiagnosis and the historical data based faultdiagnosis. Principal component analysis on the reconciled data is used to develop a statistical model for fault detection. The updating of the principal component model based on the most recent reconciled data is a locally linearized model around the current plant measurements, so that it is applicable to any generic nonlinear systems. The sensor faultdiagnosis and process faultdiagnosis are decoupled through considering the process faultdiagnosis as a parameter estimation problem. The developed approach has been applied to the IRIS helical coil steam generator system to monitor the operational performance of individual steam generators. This approach is general enough to design faultdiagnosis systems for the next generation nuclear power plants. (authors)
Zhao, K.; Upadhyaya, B.R. [The University of Tennessee, Nuclear Engineering Department, Knoxville, TN 37996-2300 (United States); Wood, R.T. [Oak Ridge National Laboratory, Nuclear Science and Technology Division, Oak Ridge, TN 37831-6010 (United States)
The need for economical, reliable and effective delivery of electric power leads to the search for new, efficient and effective methods for diagnosing the high voltage equipments in the industries all over the world. As the average usage period of transformers increases, the necessity to know the internal condition of transformers is increasing. It is therefore critically important to establish monitoring and diagnostic techniques that can perform transformer condition assessment. Frequency response analysis, generally known as FRA, is one of the technologies to diagnose transformers. Using case studies, this paper presents the effectiveness of FRA as measurements for detecting transformer faults. This paper introduces the fact that FRA waveforms have useful information about diagnosis of fault on winding shield and core earths, and that the condition outside transformers can affect frequency response characteristics. The FRA measurement results are further investigated through a simulation study using a computer model.
It is well known that the vibration signals are unstable when there is some failure in machinery. So in this paper, the cone-shaped kernel distributions (CKD) of vibration acceleration signals acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images. Meanwhile, non-negative matrix factorization (NMF) was used to decompose multivariate data, and neural network ensemble (NNE), which is of better generalization capability for classification than a single neural network, was used to perform intelligent diagnosis without further fault feature (such as eigenvalues or symptom parameters) extraction from time-frequency distributions. It is shown by the experimental results that the faults of diesel valve trains can be accurately classified by the proposed method.
A novel intelligent faultdiagnosis 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
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 faultdiagnosis 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 faultdiagnosis 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.
This paper discusses FBNEXPERT, an expert system designed to help operators in controlling and maintaining a FASTBUS data acquisition system; it can also assist human experts during trouble-shooting and faultdiagnosis. It is based on a shell (NEXPERT, by Neuron Data) which interacts with a knowledge base, where all the information about the FASTBUS system is collected, including the description of the configuration (from the files used for the initialization procedure) and the results of tests and previous diagnoses. During the diagnostic process, FBNEXPERT spans several levels of description of the FASTBUS system and applies various co-operating strategies.
Corazziari, F.; Falciano, S.; Luminari, L.; Savarese, M.; Trasatti, E. (INFN, Dipartimento di Fisica, P.le A. Moro 2, I-00185 Roma (IT)); Rimmer, E.M. (CERN, ECP Div., 1211 Geneva 23 (CH))
To realize the function for monitoring and faultdiagnosis real time, fulfil the request of celerity, maneuverability and high reliability of a weapon system and conquer the problem of the single function and the miscellaneous structure of tradition monitoring and diagnosis system, a new-style portable faultdiagnosis device for the complex weapon system based on PC104 bus is designed. The
The number and severity of earthquakes has led to a growing interest in control systems capable of improving the safety and integrity of buildings and other structures that are subject to violent vibration control on each building floor is considered as a local control problem within the hierarchical two-level structure. The distributed control system, based on receding horizon control, gives
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.
In this paper, we will treat the diagnosis problem to accurately determine fault types. The judgment of fault types is accomplished by observing the cluster newly formed with faults and clustering the input current waveforms to intrinsically show the conditions with the dignet that is a clustering algorithm. The types of input current waveforms are, however, constrained during normal operation,
The use of induction motors is widespread in industry. Many researchers have studied the condition monitoring and detecting the faults of induction motors at an early stage. Early detection of motor faults results in fast unscheduled maintenance. In this study, a new artificial immune based support vector machine algorithm is proposed for faultdiagnosis of induction motors. Support vector machines
Abstract: Precise failure analysis requires accurate faultdiagnosis. A previously proposed method for diagnosing bridging faults using single stuck-at dictionaries was applied only to small circuits, produced large and imprecise diagnoses, and did not take into account the Byzantine Generals Problem for bridging faults. We analyze the original technique and improve it by introducing the concepts of match restriction, match
Brian Chess; David B. Lavo; F. Joel Ferguson; Tracy Larrabee
This paper presents research in model based fault diagnostics for the power electronics inverter based induction motor drives. A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for faultdiagnosis. Instead of simple open-loop circuits, our research focuses on closed
M. Abul Masrur; ZhiHang Chen; Baifang Zhang; L. Murphey
Verification and correction of faults related to tooling design and tooling installation are important in the auto body assembly process launch. This paper introduces a Bayesian network (BN) approach for quick detection and localisation of assembly fixture faults based on the complete measurement data set. Optimal sensor placement for effective diagnosis of multiple faults, structure learning of the Bayesian network
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 faultdiagnosis fol- lowed by appropriate dynamic system recongur ation mitigate effects of faults and therefore enhance
Andreas Doblander; Arnold Maier; Bernhard Rinner; Helmut Schwabach
A nonlinear model of a hydraulic automatic gauge control (AGC) system is established for fault detection and isolation (FDI). By analyzing the relationship between faults and load uncertainties, a decoupling subsystem has been derived using a differential geometric approach. An exponential gain observer has been designed based on the observable decoupling subsystem. Diagnosis residual signal is sensitive to designated faults
It is difficult to extract the fault features of a rotating machine via vibration analysis due to interference from background noise. Stochastic resonance (SR), used as a method of utilising noise to amplify weak signals in nonlinear dynamical systems, can detect weak signals overwhelmed in the noise. However, the detection effect of current SR methods is still unsatisfactory. To further increase the output signal-to-noise ratio (SNR) and improve the detection effect of SR, the present study proposes an improved SR method with a multi-stable model for identifying the defect-induced rotating machine faults by analysing the influence relationship between the resonance model and the resonance effect. Due to the structural characteristics of three potential wells and two barriers, the proposed resonance model can not only further amplify weak signals, but also convert into a monostable model, a bistable model or a tristable model. This result is achieved by adjusting system parameters and thus obtaining a better matching of the input signals and resonance models. Therefore, the multi-stable SR method, combined with the characteristics of the multi-stable model, can both increase the output SNR and improve the detection effect and also detect the low SNR signals and enhance the processing capability of SR for weak signals. Finally, the proposed method is applied to a gearbox faultdiagnosis in a rolling mill in which two local faults located in the big gear and the pinion, respectively, are found successfully. It can be concluded that multi-stable SR method has practical value in engineering.
Objective For a reliable objective diagnosis of vascular injuries in hand-arm vibration syndrome (HAVS), the standardized cold provocation\\u000a tests—finger skin temperature measurement during hand(s) immersion in cold water (FST test) and finger systolic blood pressure\\u000a measurement during local cold exposure (FSBP test)—are widely used. In recent years there is a growing controversy regarding\\u000a the diagnostic value of these tests. The aim
This paper presents an assessment of which faults can be detected using frequency response analysis (FRA) and how different faults may be distinguished. The test method and the method used by the author for presenting the results are described. The results of an extensive fault simulation programme on a 100 kVA distribution transformer are presented and discussed. The faults simulated
An expert system for accident analysis and faultdiagnosis for the Loss Of Fluid Test (LOFT) reactor, a small scale pressurized water reactor, was developed for a personal computer. The knowledge of the system is presented using a production rule approach with a backward chaining inference engine. The data base of the system includes simulated dependent state variables of the LOFT reactor model. Another system is designed to assist the operator in choosing the appropriate cooling mode and to diagnose the fault in the selected cooling system. The response tree, which is used to provide the link between a list of very specific accident sequences and a set of generic emergency procedures which help the operator in monitoring system status, and to differentiate between different accident sequences and select the correct procedures, is used to build the system knowledge base. Both systems are written in TURBO PROLOG language and can be run on an IBM PC compatible with 640k RAM, 40 Mbyte hard disk and color graphics.
Effective detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance and improved operational efficiency of induction motors running off the power supply mains. In this paper, an empirical model-based faultdiagnosis system is developed for induction motors using recurrent dynamic neural networks and multiresolution signal processing methods. In addition to nameplate information required for
This paper presents a new method for faultdiagnosis using a newly developed method, support vector machine (SVM). First, the basic theory of the SVM is briefly reviewed. Next, a fast implementation algorithm is given. Then the method is applied for the faultdiagnosis in sheet metal stamping processes. According to the tests on two different examples, one is a
This paper provides a tutorial review of the state of the art in parity space faultdiagnosis approaches with particular emphasis on aerospace systems. The basic concepts and definitions are given and a consistent framework is presented to draw together the important links amongst the known methods for faultdiagnosis. Residual generation in the parity space has been recognized as
The quality consistence and machine utilization of a flexible manufacturing system (FMS) strongly depend on the statistical process control (SPC) and the correct faultdiagnosis of equipment. An FMS can be modelled by the colored timed Petri net (CTPN). However, most CTPN models of FMS lack the activities of SPC and faultdiagnosis, and they lead to incomplete FMS CTPN
Aiming to realize fast and accurate faultdiagnosis in complex network environment, this article proposes a set of anomaly detection algorithm and intelligent faultdiagnosis model. Firstly, a novel anomaly detection algorithm based on time series analysis is put forward to improve the generalized likelihood ratio (GLR) test, and thus, detection accuracy is enhanced and the algorithm complexity is reduced.
The fault detection and diagnosis methods based on principal component analysis (PCA) have been developed widely because they need no detailed information about the process mechanism model and really can detect faults promptly. However the existing diagnosis algorithms such as expert systems or contribution plots, etc. still have some trouble when they are applied in real industrial processes, which leads
An integrated approach to performance monitoring and faultdiagnosis was developed in this dissertation for nuclear power plants using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for faultdiagnosis. In the applications to nuclear power plants, on the one hand, routine operation data may
In order to improve diagnostic accuracy and quality of maintenance, it is very important to study faultdiagnosis method for automobile engine. Least-squares support vector machine called LSSVM is a modified SVM, which use a set of linear equations instead of a quadratic programming problem. In the paper, least-squares support vector machine is proposed to faultdiagnosis of automobile engine.
This paper presents a decentralized faultdiagnosis strategy of event-driven systems based on probabilistic inference and a method to construct the inference network, Bayesian network (BN), structure. First of all, the controlled plant is decomposed into some subsystems, and the global diagnosis is formulated using the Bayesian Network, which represents the causal relationship between the fault and observation in subsystems.
A new design approach for faultdiagnosis is developed for next generation nuclear power plants. In the nuclear reactor design phase, data reconciliation is used as an efficient tool to determine the measurement requirements to achieve the specified goal of faultdiagnosis. In the reactor operation phase, the plant measurements are collected to estimate uncertain model parameters so that a
This paper introduces faultdiagnosis modes and points out the source of trouble in grid-connected solar photovoltaic systems. It analyses and researches the structure and algorithm of BP neural network. After that, the paper brings forward faultdiagnosis method based on BP neural network for the grid-connected solar photovoltaic system. It shows this method is efficacious and earthly and attains
In this paper, our recent work on robust model- based faultdiagnosis (FD) for several satellite control systems using learning and sliding mode approaches are summarized. Firstly, a variety of nonlinear mathematical models for these satellite control systems are described and analyzed for the purpose of faultdiagnosis. These satellite control systems are classified into two classes of nonlinear dynamical
Wavelet neural network model is established, a sensor-online faultdiagnosis method based on this model is proposed, faultdiagnosis of multi-sensor is realized through the establishment of the separate neural network prediction model of every sensor. Simulation experiments are carried out with a large number of sensor data of modern strip mill HAGC (Hydraulic Automatic Gauge Control) system, The feasibility
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
In the faultdiagnosis of gearbox, the extraction of the fault signal is a key problem. The practical testing vibration signal of gearbox is no stable or Gauss distributing. In different fault states, the vibration signal has different Gauss property and symmetry property, usually including stronger noise and low SNR. Faint fault information is often totally flooded in the noise,
Huang Jinying; Bi Shihua; Pan Hongxia; Yang Xiwang
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 faultdiagnosis system adopts data fusion method and divides the faultdiagnosis 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 faultdiagnosis requirement of complicated system, and verify that the fusion faultdiagnosis system can realize the faultdiagnosis of NPP on line timely.
The performance of manufacturing systems or equipment is, to a great extent, dependent upon the condition of their components.\\u000a Closely monitoring the condition of the critical components and carrying out timely system diagnosis whenever a fault symptom\\u000a is detected would help to reduce system downtime and improve overall productivity. Fault tree analysis (FTA) is a powerful\\u000a tool for reliability studies
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.
A faultdiagnosis algorithm using a signed digraph as a model of a system is useful to real-time diagnosis of failures that occur in a chemical plant. It has been improved so much that it can find multiple origins of failures that occur in the plant at the same time. It is imperative that the diagnosis system be evaluated in
This paper presents a model based on neural network optimized by the ant colony optimization algorithm (ACOA) for fault section diagnosis in distribution systems of electric power systems, and the simulation results show that it can effectively improve the fault-tolerance ability of fault section diagnosis. It had better fault-tolerance ability in contrast with the BP-NN model and the DGA-NN model.
The architecturally relevant issues which arise in the development of both qualitative and quantitative fault-diagnostic systems are discussed, and the technical issues of acquiring the knowledge and using it effectively are addressed. A direct comparison is drawn between the performances of both fault-detection systems while detecting the same fault on a laboratory test rig. It is concluded that fault-diagnostic systems
This paper presents one method for faultdiagnosis of power distribution lines by using a decision tree. The conventional method, using a decision tree, applies only to discrete attribute values. To apply it to faultdiagnosis of power distribution lines, in practice it must be revised in order to treat attributes whose values range over certain widths. This is because the sensor value or attribute value varies owing to the resistance of the fault point or is influenced by noise. The proposed method is useful when the attribute value has such a property, and it takes into consideration the cost of acquiring the information and the probability of the occurrence of a fault.
One of the great challenges in Internet service fault management under noisy and uncertain environment lies in the difficulty of fault priori distribution acquisition. To address the problem, an active probing based approach is proposed for the Internet service in this paper. A hidden Markov model(HMM) based dynamic probabilistic dependency model is chosen to be the fault propagation model (FPM).
An innovative method based on support vector machines is presented to diagnose the fault of analog circuit. Firstly, in order to get enough fault samples, the circuit program is compiled in MATLAB software to obtain expressions of output signals. Secondly, fault samples are sent into Support Vector Machines to train Support Vector Machines. Thirdly, the test samples are classified by
Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox.
The paper describes a system for automated detection of incipient faults in induction machines. The system has been based on the Motor Current Signature Analysis method (MCSA) and aimed to be applied in a thermal electric power plant in south Brazil. First, the mechanism of fault evolution is introduced and clarified regarding the most common induction motor faults: stator winding
Daniel da S. Gazzana; Luis Alberto Pereira; Denis Fernandes
This site explains the three types of faults that result from plate movement. Animated diagrams are used to demonstrate strike-slip faults, normal faults, and reverse faults. There are also four photographs that show the results of actual earthquakes.
This paper presents a new fault detection and diagnosis approach for nonlinear dynamic plant systems with a neuro-fuzzy based\\u000a approach to prevent developing of fault as soon as possible. By comparison of plants and neuro-fuzzy estimator outputs in\\u000a the presence of noise, residual signal is generated and compared with a predefined threshold, the fault can be detected. To\\u000a diagnose the
A multi-net faultdiagnosis system designed to provide an early warning of combustion-related faults in a diesel engine is\\u000a presented. Two faults (a leaking exhaust valve and a leaking fuel injector nozzle) were physically induced (at separate times)\\u000a in the engine. A pressure transducer was used to sense the in-cylinder pressure changes during engine cycles under both of\\u000a these conditions,
Amanda J. C. Sharkey; Gopinath Odayammadath Chandroth; Noel E. Sharkey
Feedforward neural networks (FNNs) are developed and implemented to classify a four-stage high pressure air compressor into one of the following conditions: baseline, suction or exhaust valve faults. These FNNs are used for the compressor’s automatic condition monitoring and faultdiagnosis. Measurements of 39 variables are obtained under different baseline conditions and third-stage suction and exhaust valve faults. These variables
A new method has been developed for the detection and diagnosis of broken rotor bars faults in three-phase induction motors under no-load conditions. Early detection of faults is made by using a sliding window constructed by Hilbert transforms of one of the phases of the thee-phase currents and the size of a fault is diagnosed by motor current signature analysis
Recently, a decoupling-based (DB) fault detection and diagnosis (FDD) method was developed for diagnosing multiple-simultaneous faults in air conditioners (AC) and was shown to have very good performance. The method relies on identifying diagnostic features that are decoupled (i.e., insensitive) to other faults and operating conditions. The current paper extends the DB FDD methodology to heat pumps. Heat pumps have
The construction of efficient sequential fault location experiments for permanent faults, which is an NP-complete problem requiring a heuristic approach for the design of near-optimum sequential experiments, is considered. The approach is based on information-theoretic concepts, and the suggested algorithm for the construction of near-optimum sequential fault location experiments is (1) systematic, (2) has a sound theoretical justification, and (3) has low design complexity.
Varshney, P. K.; Hartmann, C. R. P.; de Faria, J. M., Jr.
The traditional method of spectrum analysis on the current signal via FFT is hard to diagnose the fault of the broken rotor bars. This paper presents a faultdiagnosis method using the RELAX algorithm in frequency domain. It can estimate the amplitude and phase values of various frequency components using coarse and fine estimation according to the criterion of minimum
Presents two new methods of fault localization and identification in linear electronic circuits, based on a bilinear transformation in multidimensional spaces. The conventional bilinear transformation maps changes of circuit component parameters pi into a family of pi-loci on the complex plane. The loci can be used for faultdiagnosis as well as parametrical identification measurements of objects modeled by electrical
Kurtogram, due to the superiority of detecting and characterizing transients in a signal, has been proved to be a very powerful and practical tool in machinery faultdiagnosis. Kurtogram, based on the short time Fourier transform (STFT) or FIR filters, however, limits the accuracy improvement of kurtogram in extracting transient characteristics from a noisy signal and identifying machinery fault. Therefore,
It is difficult to diagnose the faults of electromagnetic interference (EMI) since the knowledge of it is incomplete. In order to solve the problem, an intelligent faultdiagnosis method of electromagnetic interference is proposed based on the combination of CBR and RBR. In the proposed method CBR is relied on mainly and supplemented with the RBR, i.e., RBR is enabled
Gang Ming-Gang; Chen Jie; Yang Bo; Cai Tao; Cheng Lan
In this paper, an approach to faultdiagnosis in a nonlinear stochastic dynamic system is proposed using the interacting multiple particle filtering (IMPF) algorithm. The fault diagnostic approach is formulated as a hybrid multiple- model estimation scheme. The proposed diagnostic approach provides an integrated framework to estimate the system's current operational or faulty mode, as well as the unmeasured state
A faultdiagnosis system is an information system which contains knowledge about a process. It is a flexible tool in the analysis of the normal operation or fault situations of the process. It enables the process operator to monitor the process in an acti...
Gasoline engine is a complex power generating machines and used widely in automotive industry, which the failure rate is high. Carrying out the gasoline engine fault diagnostic methods have been studied and still a lasting topic for scientists. Crankshaft instantaneous angular acceleration, which evaluated by second derivative of the crankshaft rotation respect to time contains information for faultdiagnosis. In
Diesel engine is a kind of complex power generating machine, and play an important role in industry, which failure rate is high. How to utilize new science and technology to carry out diesel engine faultdiagnosis is a lasting topic. Instantaneous angular acceleration of diesel crankshaft contains a little information for diesel engine fault diagnosing and its power balance evaluating.
This paper describes a methodology that aims to detect and diagnosisfaults in lightning arresters, using the thermovision technique. Thermovision is a non-destructive technique used in diverse services of maintenance, having the advantage not to demand the disconnection of the equipment under inspection. It uses a set of neuro-fuzzy networks to achieve the lightning arresters fault classification. The methodology also
Carlos A. Laurentys Almeida; Walmir M. Caminhas; Antonio P. Braga; Vinicius Paiva; Helvio Martins; Rodolfo Torres
The classification accuracy and efficiency of multiclass SVMs are largely dependent on the SVM combination strategy in analog circuits faultdiagnosis. An optimized SVM extension strategy is presented in this paper, which uses minimum spanning tree (MST) algorithm to simplify the SVM structure and decrease the classification errors. By taking the separability measure of fault classes as edge weight of
Faultdiagnosis in digital hardware using AI techniques is good domain for basic and applied research. An intelligent frame work for fault detection and isolation in Philips 89v52 RD2 microcontroller based system is discussed in this paper. The main feature includes intelligent diagnostic assessment and effective management of the testing process using fuzzy approaches. Fuzzy modeling is used to derive
In this paper, we present an ongoing research effort on developing an automatic faultdiagnosis and prognosis service for large-scale computing systems, such as TeraGrid clusters. By leveraging the research on system health monitoring, the proposed service aims at automati- cally revealing fault patterns from historical data by applying data mining and machine learning techniques. To address key challenges posted
Zhiling Lan; Prashasta Gujrati; Yawei Li; Ziming Zheng; Rajeev Thakur; John White
This paper introduces a new fault model for system- level diagnosis and a class of on-line distributed diagno- sis algorithms that operate correctly under the model. The algorithms are guaranteed to operate cor rectly in the presence of faulty nodes that disseminate arbitrarily cor- rupted diagnostic information. The fault model addr esses the practical issue of designing an inter-node test
Based on the surprising development of information technology, there tends to be more electronic apparatus installed in automobiles which presents a new challenge for vehicle faultdiagnosis. Then how to locate the existence and type of the traditional faults that occur in automobile electronic control systems proves to be of great significance. This paper puts forward extraction condition characteristic signal
The fuzzy rule sets, which have been widely used in avionic faultdiagnosis system, have considerable redundancy that leads to time-consuming faults location process. In this paper, to reduce the redundant rules, a multiple objective genetic algorithm, MOGAII, is used to optimize a fuzzy rule set. The optimization problem with two objectives, the maximization diagnostic capability of the system and
In this paper, a competitive neural network architecture is used as an intelligent faultdiagnosis system to detect the fault sources in different subsystems or elements of a plant or any other device. The prioritized modification rule for connection weights is introduced and four different procedures are studied and compared from the viewpoint of their efficiency. It is shown that
Sohrab Khanmohammadi; Iraj Hassanzadeh; H. R. Zarei Poor
Diagnosis is a very complex and important task for finding the root cause of faults in nuclear power plants. The objective of this paper is to investigate the feasibility of using the combination of signed directed graph (SDG) and artificial neural networks for faultdiagnosis in nuclear power plants especially in U-Tube steam generator. Signed directed graph has been the most widely used form of qualitative based model methods for process faultdiagnosis. It is constructed to represent the cause-effect relations among the dynamic process variables. Signed directed graph consists of nodes represent the process variables and branches. The branch represents the qualitative influence of a process variable on the related variable. The main problem in faultdiagnosis using the signed directed graph is the unmeasured variables. Therefore, neural networks are used to estimate the values of unmeasured nodes. In this work, different four cases of faults in the steam generator ( SG) have been diagnosed, three of them are single fault and the fourth is multiple fault. The first three faults are by pass valve leakage (Vbp(+)), main feed water valve opening increase (Vfw(+)), main feed water valve opening decrease (Vfw (-)). The fourth fault is a multiple fault where by-pass valve leakage and main feed water valve opening decrease (Vbp(+) and Vfw (-)) in the same time. The used data are collected from a basic principle simulator of pressurized water reactor 925 Mwe. The signed directed graph of the steam generator is constructed to represent the cause-effect relations among SG variables. It consists of 26 nodes represent the SG variables, and 48 branches represent the cause effect relations among this variables. For each fault the values of measured nodes are coming from sensors and the values of unmeasured nodes are coming from the trained neural networks. These values of the nodes are compared by normal values to get the sign of the nodes. The cause-effect graph for each fault is constructed from the steam generator signed directed graph by removing the invalid (normal) nodes and inconsistent branches. Then in the cause-effect graph we search about the node which does not have an input branch. This node is the fault origin node. The result of this work demonstrated that this method can be used in nuclear power plant faultdiagnosis. The advantages of this method are, it enables us to diagnose a multi fault, it is not restricted by pre-defined faults, and it is fast method. (authors)
Aly, Mohamed N. [Nuclear Eng. Department, Fac. of Eng., Alex. Univ., Alex. (Egypt); Hegazy, Hesham N. [Nuclear Power Plants Authority, Cairo (Egypt)
The paper presents five different approaches to faultdiagnosis. While developing the methods, various kinds of pragmatic aspects and robustness had to be considered in order to achieve practical solutions. The presented methods are: classification of fau...
This study presents a methodology for faultdiagnosis using a Two-Stage Artificial Neural Network Clustering Algorithm. Previously, SPICE models of a 5-bus DC power distribution system with assumed constant output power during contingencies from the DDCU ...
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 faultdiagnosis 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: firstname.lastname@example.org; 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)
Based on the essential requirements of the CIPS, the paper presents an integrated intelligent fault detection and diagnosis system. A urea synthesis process is used as an illustration and the structure, functions and realization are discussed specifically
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 faultdiagnosis, neural-network approaches, nonlinear-static-models based methods, and knowledge-based intelligent approaches are presented. Keywords: Fault detection; Faultdiagnosis; 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:email@example.com
Variable speed drives have become industrial standard in many applications. Therefore faultdiagnosis of voltage source inverters is becoming more and more important. One possible fault within the inverter is an open circuit transistor fault. An overview of the different strategies to detect this fault is given, including the algorithms used to localize the open transistor. Previous work showed significant
Automotive engines is an important application for model-based diagnosis because of legislative regulations. A diagnosis system for the air-intake system of a turbo-charged engine is constructed. The design is made in a systematic way and follows a framework of hypothesis testing. Different types of sensor faults and leakages are considered. It is shown how many different types of fault models,
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
. In this paper we have used a combination of three algorithms: Dynamic time warping (DTW) and the coefficients of Mel frequency Cepstrum (MFC) and Fast Fourier Transformation (FFT) for classifying various engine faults. Dynamic time warping and MFCC (Mel Frequency Cepstral Coefficients), FFT are used usually for automatic speech recognition purposes. This paper introduces DTW algorithm and the coefficients extracted from Mel Frequency Cepstrum, FFT for automatic fault detection and identification (FDI) of internal combustion engines for the first time. The objective of the current work was to develop a new intelligent system that should be able to predict the possible fault in a running engine at different-different workshops. We are doing this first time. Basically we took different-different samples of Engine fault and applied these algorithms, extracted features from it and used Fuzzy Rule Base approach for fault Classification.
In this paper, our work on robust faultdiagnosis (FD) for satellite control systems using sliding mode and learning approaches are summarized. Firstly, a variety of nonlinear mathematical models for satellites are described and analyzed for the purpose of faultdiagnosis. Then, fault diagnostic sliding mode observer with time-varying gains is presented and analyzed. Two classes of learning estimators are
In the present study, a faultdiagnosis system is proposed for internal combustion engines using wavelet packet transform (WPT) and artificial neural network (ANN) techniques. In faultdiagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. The signal processing algorithm of the present system is gained from previous work used for speech recognition.
This research focuses on the development of an intelligent diagnostic system for rotating machinery. The system is composed of a signal processing module and a state inference module. In the signal processing module, the recursive least square (RLS) algorithm and the Kalman filter are exploited to extract the order amplitudes of vibration signals, followed by fault classification using the fuzzy
Mingsian Bai; Jiamin Huang; Minghong Hong; Fucheng Su
In order to extract the weak fault information submerged in strong background noise of the gearbox vibration signal, multiwavelet denoising method with adaptive threshold and envelope demodulation method are applied in this paper. Multiwavelets have many excellent properties that single wavelet can not satisfy simultaneously, such as symmetry, orthogonality, compact support and high vanishing moments etc, which make it can
Li Wenbin; Zhang Jianyu; Cui Lingli; Gao Lixin; Zhang Feibin
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
To increase the accuracy of applying traditional faultdiagnosis method to aeroengine vibrant faults, a novel approach based on wavelet neural network is proposed. The effective signal features are acquired by wavelet transform with multi-resolution analysis. These feature vectors then are applied to the neural network for training and testing. The synthesized method of recursive orthogonal least squares algorithm is used to fulfill the network structure and parameter initialization. By means of choosing enough practical samples to verify the proposed network performance, the information representing the faults is inputted into the trained network. According to the output result the fault pattern can be determined. The simulation results and actual applications show that the method can effectively diagnose and analyze the vibrant fault patterns of aeroengine and the diagnosis result is correct.
Submarine cable faults are not uncommon events in the Internet today. However, their impacts on end-to-end path quality have received almost no attention. In this paper, we report path-quality measurement results for a recent SEA-ME-WE 4 cable fault in 2010. Our measurement methodology captures the path-quality degradation due to the cable fault, in terms of delay, asymmetric packet losses, and correlation between loss and delay. We further leverage traceroute data to infer the root causes of the performance degradation.
Chan, Edmond W. W.; Luo, Xiapu; Fok, Waiting W. T.; Li, Weichao; Chang, Rocky K. C.
In this paper, using the information theory and the statistics analysis method, we have established the theory and method of faultsdiagnosis based on the multi-index fusion, including the information theory of multi-index diagnosis, diagnosis ability testing, indexes selecting, and Bayesian diagnosis modelling. And then, we applied the theories and methods to analyse the piston-liner wear condition of a diesel engine. In that, satisfactory results are achieved.
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 ...
W. F. Ramirez M. Skliar A. Narayan G. W. Morgenthaler G. J. Smith
There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of faultdiagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors. PMID:22346593
Martínez-Sibaja, Albino; Astorga-Zaragoza, Carlos M; Alvarado-Lassman, Alejandro; Posada-Gómez, Rubén; Aguila-Rodríguez, Gerardo; Rodríguez-Jarquin, José P; Adam-Medina, Manuel
There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of faultdiagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors.
Martinez-Sibaja, Albino; Astorga-Zaragoza, Carlos M.; Alvarado-Lassman, Alejandro; Posada-Gomez, Ruben; Aguila-Rodriguez, Gerardo; Rodriguez-Jarquin, Jose P.; Adam-Medina, Manuel
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
Faultdiagnosis is a critical task for autonomous opera- tion of systems such as spacecraft and planetary rovers, and must often be performed on-board. Unfortunately, these systems frequently also have relatively little compu- tational power to devote to diagnosis. For this reason, al- gorithms for these applications must be extremely efficient, and preferably anytime. In this paper we introduce the
Large rotating machinery such as turbines and compressors are the key equipment in oil refineries, power plants, and chemical engineering plants. To minimize the economic loss incurred because of the defects of malfunctions of these machines, diagnosis is very important. Currently, diagnosis is carried out mainly using spectral analysis. In spite of being effective in detecting the faults (monitoring), spectral
An approach for the diagnosis of faults in dynamic systems based on a neuro-fuzzy scheme is presented. The simple structure that represents fuzzy rules in a neural network uses a rule extraction mechanism varying from most other approaches as it is based on concepts of machine learning. An additional, straightforward optimization eventually enhances the performance of the diagnosis. The approach
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
The vibration data, especially those collected during the system run-up and run-down periods, contain rich information for gearbox condition monitoring. Time-frequency (TF) signal analysis is an effective tool to detect gearbox faults under varying shaft speed. However, the feature of the amplitude modulated-frequency modulated (AM-FM) gearbox fault signal usually cannot be directly extracted from the blurred time-frequency representation (TFR) caused by the time-varying frequency and noisy multicomponent measurement. As such, we propose to use a generalized synchrosqueezing transform (GST)-based TF method to detect and diagnose gearbox faults. With this method, the original vibration signal is first mapped into another analytical signal to facilitate synchrosqueezing of the TF picture. A time-scale domain restoration process is then applied to recover the instantaneous frequency profile with concentrated TFR. The gearbox fault, if any, can then be detected by observing the presence of the meshing frequency and sideband components in the TFR. The faulty gear can be identified via frequency relation analysis of AM-FM components. The proposed method is evaluated using both simulated and experimental gearbox vibration signals. The results show that the proposed approach is effective for gearbox condition monitoring.
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.
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 faultdiagnosis of on-spot low-speed heavy-duty gears and could be a significant supplement to vibration testing diagnosis.
A model-based approach for fault detection and diagnosis of nonlinear processes is presented. However, the supervision of nonlinear systems is often very difficult in view of the lack of accurate models. Neuro-fuzzy models may help to cope with this problem since they can be trained from measured data. In this paper the application of a multi-model approach for fault detection
The electronic stability program (ESP) is a driving dynamical control system that is used to support a driver in critical driving situations. A basic component integrated in the ESP system is an on-line sensor monitoring system that is used for detecting faults in sensors as early as possible to prevent a fail control. Objective of this paper is to present
The fault signal of rotor rub is a typical nonlinear and non-stationary data. HHT is considered an effective method on that kind signal and the crucial step is EMD. However, one of the major drawbacks of the EMD method is mode mixing. Ensemble Empirical Mode Decomposition (EEMD) has been proposed recently. This method overcomes the mode mixing and represents a
Aeroengine components performances deteriorate due to wearing, foreign object ingestion damage or other factors. The engine monitoring and fault diagnostics systems are developed to trace the components health condition and to help make the maintenance decision in order to avoid severe damages. Based on transient gas path measurements, the engine monitoring and diagnostics are carried out through the components health
This paper introduces a novel method of predicting future concentrations of elements in lubrication oil, for the aim of identifying possible anomalies in continued operation aboard a large marine vessel. The research carried out is supported by a discussion of previous work in the field of fault detection in tribological mechanisms, although with a focus upon two stroke marine diesel
Recently a new method called ATE assisted compaction for achieving test response compaction has been proposed. The method relies on testers to achieve additional compaction, without compromising fault coverage, beyond what may already be achieved using on-chip response compactors. The method does not add additional logic or modify the circuit under test or require additional tests and thus can be
J. M. Howard; Sudhakar M. Reddy; Irith Pomeranz; Bernd Becker
The authors present the pulsating parameter method in the frequency domain using two parameters of the pulsating model of an axial piston pump as major monitoring parameters to diagnose the fault of the pump. In order to apply the theory of transmission in a hydraulic pipeline to identify the model, a whole set of algorithms is also developed. Compared with
This paper deals with the design of robust model-based fault detection and isolation (FDI) systems for atmospheric reentry vehicles. This work draws expertise from actions undertaken within a project at the European level, which develops a collaborative effort between the University of Bordeaux, the European Space Agency, and European Aeronautic Defence and Space Company Astrium on innovative and robust strategies
Over the last few decades, the research for new fault detection and diagnosis techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the fault seeded in two different engine bearings. The success rate obtained in our tests for fault severity classification was above 95%. In addition to the fault severity, a location index was developed to determine the fault location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing fault with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.
The objective of this research is to investigate the feasibility of utilizing the hybrid method of ensemble empirical mode decomposition (EEMD) and pure empirical mode decomposition (EMD) to efficiently decompose the complicated vibration signals of rotating machinery into a finite number of intrinsic mode functions (IMFs), so that the fault characteristics of the misaligned shaft can be examined in the
The principle and algorithm of Empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) is introduced, and two methods are respectively used to analyze simulated signal and engine's vibration signals. Based on EEMD, the signal can be efficiently decomposed into a finite number of intrinsic mode functions (IMFs) by adding white noise, and the problem of mode mixing in
Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and non-stationary signals. It may decompose a complicated signal into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. The EMD method has attracted considerable attention and been widely applied to faultdiagnosis of rotating machinery recently. However, it cannot reveal the signal characteristic information accurately because of the problem of mode mixing. 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 EEMD-based method for faultdiagnosis of rotating machinery. First, a simulation signal is used to test the performance of the method based on EEMD. Then, the proposed method is applied to rub-impact faultdiagnosis of a power generator and early rub-impact faultdiagnosis of a heavy oil catalytic cracking machine set. Finally, by comparing its application results with those of the EMD method, the superiority of the proposed method based on EEMD is demonstrated in extracting fault characteristic information of rotating machinery.
The article describes the study of the vibration control of unbalanced shafts supported by (active-passive) or hybrid magnetic bearings (HMBs). The vibration control study is carried out on the magnetic active part (stator) of a HMB on the basis of a diagnosis of the shaft dynamics. Since the mechanical faults affecting an active magnetic bearing has its origin in the
Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in faultdiagnosis. 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 faultdiagnosis algorithm proposed in this paper.
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
The problem of failures in induction motors is a large concern due to its significant influence over industrial production. Therefore a large number of detection techniques were presented to avoid this problem. This paper presents the comparison results of induction motor rotor fault detection using three methods: motor current signature analysis (MCSA), surface vibration (SV), and instantaneous angular speed (IAS). These three measurements were performed under different loads with three rotor conditions: baseline, one rotor bar broken and two rotor bar broken. The faults can be detected and diagnosed based on the amplitude difference of the characteristic frequency components of power spectrum. However IAS may be the best technique because it gives the clearest spectrum representation in which the largest amplitude change is observed due to the faults.
A connectionist network is introduced for faultdiagnosis 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.
It is well known that expert systems are useful in capturing expertise and applying knowledge to chemical engineering problems such as diagnosis, process control, process simulation, and process analysis. Traditionally, expert system applications are limi...
Stator Winding Bar Hollow Strand Blockage (SWBHSB) is one of the main faults for large turbo-generators with water and hydrogen cooling system. It will lead to increasing water temperature at the bar exit which may cause hidden troubles for turbo-generator's security. According to a three-layer-structural model of data fusion, this paper presents a faultdiagnosis method for turbo-generators based on data fusion technology. Firstly, a bp network on pixel level fusion is set up, in which several temperature parameters at the bar exit are accurately computed. Then in feature level fusion, the fingerprints are distilled from the result of pixel level fusion. Finally, decision level fusion gives a faultdiagnosis for the measuring channels and thermometric components. This method can effectively avoid problems such as misinformation and fake report.
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 faultdiagnosis 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 faultdiagnosis, 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
An integrated approach to performance monitoring and faultdiagnosis was developed in this dissertation for nuclear power plants using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for faultdiagnosis. In the applications to nuclear power plants, on the one hand, routine operation data may not be able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components may experience degradation. On the other hand, physical models always have uncertainty and are often too complicated in terms of model structure to design residual generators for faultdiagnosis. Therefore, a realistic faultdiagnosis method needs to combine the strength of physical models in modeling a wide range of anticipated operation conditions and the strength of statistical data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using physical models and model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed method could eliminate false alarms due to model uncertainty and deal with operating condition changes of nuclear power plants. The developed algorithms were demonstrated using the International Reactor Innovative and Secure (IRIS) Helical Coil Steam Generator (HCSG) systems. A thermal hydraulic model was developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and faultdiagnosis module was developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operating conditions. This dissertation will bridge the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and faultdiagnosis for nuclear power plants. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors.
Squirrel-cage asynchronous motors are used widely in industry production process. It is significant to improve squirrel-cage asynchronous motors diagnosis technique in application. It helps to decrease the occurrence of accident and reduce the cost of maintenance. Based on the wavelet packet-neural network the scheme on the real-time diagnosis of the stator, bearing, and eccentricity fault of squirrel-cage asynchronous motors is
Multi-agent technology offers a number of characteristics that make it well suited for distributed process monitoring and faultdiagnosis tasks. In this paper we introduce a multi-agent architecture to implement distributed applications for chemical process monitoring and diagnosis as a set of cooperating intelligent agents. The agents appeared in this application are defined in ADL (Agent Description Language), a high-level
PSO (Particle Swarm Optimization)-RBF is widely used in intelligent faultdiagnosis for generator unit. Since PSO has slow convergence rate, low accuracy, and early-maturing problem which effect training speed and diagnosis accuracy of PSO-RBF, the operations of crossover and variation of genetic algorithm (GA) are introduced into PSO such that the performance of PSO can be improved. GA-PSO is employed
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.
Some large wind turbines use a low speed synchronous generator, directly-coupled to the turbine, and a fully rated converter to transform power from the turbine to mains electricity. This paper considers the condition monitoring and diagnosis of mechanical and electrical faults in such a variable speed machine. The application of wavelet transforms is investigated because of the disadvantages of conventional
Engineered Conditioning (EC) is a Genetic Algorithm operator that works together with the typical genetic algorithm operators: mate selection, crossover, and mutation, in order to improve convergence toward an optimal multiple faultdiagnosis. When incorporated within a typical genetic algorithm, the resulting hybrid scheme produces improved reliability by exploiting the global nature of the genetic algorithm as well as “local”
Walter D. Potter; John A. Miller; Bruce E. Tonn; Ravi V. Gandham; Chito N. Lapena
In industrial manufacturing rigorous testing is used to ensure that the delivered products meet their specifications. Mechanical maladjustment or faults often show their presence through abnormal acoustic signals. This is the same case in robot assembly - the application domain addressed in this paper. Manual diagnosis based on sound requires extensive experience, and usually such experience is acquired at the
Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and non-stationary signals. It may decompose a complicated signal into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. The EMD method has attracted considerable attention and been widely applied to faultdiagnosis of rotating machinery recently. However, it cannot reveal
The ensemble empirical mode decomposition (EEMD) can overcome the mode mixing problem of the empirical mode decomposition (EMD) and therefore provide more precise decomposition results. Wavelet neural network (WNN) possesses the advantages of both wavelet transform and artificial neural networks. This paper combines the merits of EEMD and WNN to propose an automated and effective faultdiagnosis method of locomotive
Empirical mode decomposition (EMD) is an adaptive time-frequency analysis method that has been widely employing for machinery faultdiagnosis. EMD is famous in revealing instantaneous change of frequency or time from non-linear sensory signal so that the occurrence of anomalous signal can be accurately detected. However, its shortcomings include mode mixing and end effects that often appear in its decomposed
Chillers constitute a significant portion of energy consumption equipment in heating, ventilating and air-conditioning (HVAC) systems. The growing complexity of building systems has become a major challenge for field technicians to troubleshoot the problems manually; this calls for automated ldquosmart-service systemsrdquo for performing fault detection and diagnosis (FDD). The focus of this paper is to develop a generic FDD scheme
Setu Madhavi Namburu; Mohammad S. Azam; Jianhui Luo; Kihoon Choi; Krishna R. Pattipati
By way of analyzing uncertainty on the classification of state parameters of Diesel Engine turbocharging system, this paper defines index distinguishable weight on basis of classification and builds the unascertained measure model of the comprehensive evaluation on faultdiagnosis of Diesel Engine turbocharging system. Then it explains diagnostic procedure and usage on index distinguished weight by means of a example.
In this paper, a decentralized faultdiagnosis approach of complex processes is proposed based on multiblock kernel partial least squares (MBKPLS). To solve the problem posed by nonlinear characteristics, kernel partial least squares (KPLS) approaches have been proposed. In this paper, MBKPLS algorithm is first proposed and applied to monitor large-scale processes. The advantages of MBKPLS are: 1) MBKPLS can
Yingwei Zhang; Hong Zhou; S. Joe Qin; Tianyou Chai
The air-conditioning systems of buildings have been diversified in recent years, and the complexity of the systems has increased. At the same time, stability in the system and low running cost are demanded. To solve these problems, various research projects have been done. The development of the energy load prediction systems and the fault detection and diagnosis systems have received
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
This paper presents a method of faultdiagnosis for the reciprocating air compressor valve based on the indicator diagram and the support vector machine (SVM). This paper strikes 7 invariant moments of the indicator diagram of reciprocating air compressor, using image processing methods, according to the same moment theory. Then the method can be used to extract effective features as
In this paper we present a Bayesian Network for faultdiagnosis used in an industrial tanks system. We obtain the Bayesian Network first and later based on this, we build a defined structure as Junction Tree. This tree is where we spread the probabilities with the algorithm known as LAZYAR (also Junction Tree). Nowadays the state of the art in
Bayesian Networks has been proven to be successful tool for faultdiagnosis. There are a variety of approaches for learning the structure of Bayesian Networks from data. This learning problem has been proven to be NP-hard hence none of the approaches are exact when no prior knowledge about the domain of the variables exists. Our approach is based on searching
M. Çetin Yavuz; Ferat Sahin; Ziya Arnavut; Önder Uluyol
A user-friendly, interactive software package is described that can be used for faultdiagnosis in dynamic systems. The methodology is based on the representation of system evolution in time as probability of transitions between sets of magnitude intervals in the state\\/parameter space. The software is developed in C++ for Windows NT platform. The display capabilities of the software and its
This paper proposes an information fusion method for diagnosis. Multilayer structure of information fusion included data proposing, feature extraction and decision making, is constructed for dealing with the objects such as current, voltage and wave, etc. A Petri-network and a fault matching method of WAMS data are used in characteristic fusion. The application of this fame in the simulation resolves
The intelligent computational tools of feedforward neural networks and genetic algorithms are used to develop a real-time detection and diagnosis system of specific mechanical, sensor and plant (biological) failures in a deep-trough hydroponic system. The capabilities of the system are explored and validated. In the process of designing the fault detection neural network model, a new technique for neural network
Coal mines require various kinds of machinery. The faultdiagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned
A novel hybrid symbolic-connectionist approach to machine learning is introduced and applied to faultdiagnosis of a hydrocarbon chlorination plant. The learning algorithm addresses the knowledge acquisition problem by developing and maintaining the knowledge base through instance based inductive learning. The performance of the learning system is discussed in terms of the knowledge extracted from example cases and its classification
B. Özyurt; A. K. Sunol; M. C. Çamurdan; P. Mogili; L. O. Hall
A noise identification and faultdiagnosis system for the new products of the automobile gearbox is introduced. The framework of the developed software is described, which includes function modules as data acquisition, feature extracting, time frequency transform, order analysis, learning and training, and so on. The prototype system has been partially put in practice in a certain automobile gear-box manufacture
This study concerns with faultdiagnosis of low speed bearing using multi-class relevance vector machine (RVM) and support vector machine (SVM). A low speed test rig was developed to simulate various types of bearing defects associated with shaft speeds as low as 10rpm under several loading conditions. The data was acquired from the low speed bearing test rig using acoustic
Achmad Widodo; Eric Y. Kim; Jong-duk Son; Bo-suk Yang; Andy Chit Chiow Tan; Dong-sik Gu; Byeong-keun Choi; Joseph Mathew
Dynamic trend analysis is an important technique for fault detection and diagnosis. Trend analysis involves hierarchical representation of signal trends, extraction of the trends, and their comparison (estimation of similarity) to infer the state of the process. In this paper, an overview of some of the existing methods for trend extraction and similarity estimation is presented. A novel interval-halving method
Mano Ram Maurya; Raghunathan Rengaswamy; Venkat Venkatasubramanian
An expert system for faultdiagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a
The marine diesel engine is a complex system, which has the important function to guarantee the marine security. In this paper a novel approach of optimizing and training fuzzy neural network based on the ant colony algorithm is proposed for the intelligent faultdiagnosis of this kind of diesel engine. The structure and the parameter of fuzzy neural network for
We present three architectures, drawn from intelligent control approaches, that represent our first investigations into the design of a fast-acting faultdiagnosis system for electrical power distribution networks. We present experiments on artificial neural network and fuzzy logic based approaches, then make proposals for a hybrid combination of the two that could have promising potential. All three techniques appear to
Konstantinos Stergiopoulos; Anthony G. Pipe; H. Nouri
The noise interference and end effect is irritating in the HHT processing of strong noise signals. In view of this situation, an improved HHT method based on the wavelet denoising and wave characteristic matching is proposed. Firstly, according to the analysis of the noise interference in wave characteristic matching extension and HHT itself, the paper adopts the wavelet threshold denoising to make a pretreatment of strong noise signal that can wipe off noise interference effectively. Then, extends and reconstructs data at both ends of signal to restrain end effect during EMD and Hilbert transform with the wave characteristic matching. Lastly, obtains the early fault feature of four-way valve from HHT time-frequency spectrum accurately. Simulation and instances show that the improved HHT method is able to reduce the false components resulted from decomposing useless noise, restrain the end effect, enhance the accuracy and timeliness of HHT, and thus make HHT arithmetic more practical.
Heating, Ventilation and Air Conditioning (HVAC) systems constitute the largest portion of energy consumption equipment in residential and commercial facilities. Real-time health monitoring and faultdiagnosis is essential for reliable and uninterrupted operation of these systems. Existing fault detection and diagnosis (FDD) schemes for HVAC systems are only suitable for a single operating mode with small numbers of faults, and most of the schemes are systemspecific. A generic real-time FDD scheme, applicable to all possible operating conditions, can significantly reduce HVAC equipment downtime, thus improving the efficiency of building energy management systems. This paper presents a FDD methodology for faults in centrifugal chillers. The FDD scheme compares the diagnostic performance of three data-driven techniques, namely support vector machines (SVM), principal component analysis (PCA), and partial least squares (PLS). In addition, a nominal model of a chiller that can predict system response under new operating conditions is developed using PLS. We used the benchmark data on a 90-ton real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables under nominal and eight fault conditions of different severities at twenty seven operating modes.
Namburu, Setu M.; Luo, Jianhui; Azam, Mohammad; Choi, Kihoon; Pattipati, Krishna R.
Metal stamping process plays a very important role in the modern manufacturing industry. Owing to an ever-increasing demand for better quality at reduced cost, a practical on-line monitoring and diagnosis system is of much appeal. However, the stamping process is a complicated transient process involving a large number of variables. It is rather difficult to monitor and diagnose by classical
It is well known that expert systems are useful in capturing expertise and applying knowledge to chemical engineering problems such as diagnosis, process control, process simulation, and process analysis. Traditionally, expert system applications are limited to knowledge domains that are heuristic and involve only simple mathematics. Neural networks, however, represent an emerging technology capable of rapid recognition of patterned behavior
This paper describes a methodology that aims to detect and diagnosisfaults in lightning arresters, using the thermovision technique. Thermovision is a non-destructive technique used in diverse services of maintenance, having the advantage not to demand the disconnection of the equipment under inspection. It uses a set of neuro-fuzzy networks to achieve the lightning arresters fault classification. The methodology also uses a digital image processing algorithm based on the Watershed Transform in order to get the segmentation of the lightning arresters. This procedure enables the automatic search of the maximum and minimum temperature on the lightning arresters. These variables are necessary to generate the diagnosis. By appling the methodology is possible to classify lightning arresters operative condition in: faulty, normal, light, suspicious and faulty. The computacional system generated by the proposed methodology train its neuro-fuzzy network by using a historical thermovision data. During the train phase, a heuristic is proposed in order to set the number of networks in the diagnosis system. This system was validated using a database provided by the Eletric Energy Research Center, with a hundreds of different faulty scenarios. The validation error of the set of neuro-fuzzy and the automatic digital thermovision imagem processing was about 10 percent. The diagnosis system described has been sucessefully used by Eletric Energy Research Center as an auxiliar tool for lightning arresters faultdiagnosis.
Laurentys Almeida, Carlos A.; Caminhas, Walmir M.; Braga, Antonio P.; Paiva, Vinicius; Martins, Helvio; Torres, Rodolfo
To make the complex mechanical equipment work in good service, the technology for realizing an embedded open system is introduced systematically, including open hardware configuration, customized embedded operation system and open software structure. The ETX technology is adopted in this system, integrating the CPU main-board functions, and achieving the quick, real-time signal acquisition and intelligent data analysis with applying DSP and CPLD data acquisition card. Under the open configuration, the signal bus mode such as PCI, ISA and PC/104 can be selected and the styles of the signals can be chosen too. In addition, through customizing XPE system, adopting the EWF (Enhanced Write Filter), and realizing the open system authentically, the stability of the system is enhanced. Multi-thread and multi-task programming techniques are adopted in the software programming process. Interconnecting with the remote faultdiagnosis center via the net interface, cooperative diagnosis is conducted and the intelligent degree of the faultdiagnosis is improved.
Background Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean network model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI3-Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean network model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer. Results In this paper, we have developed a Boolean network model for the oxidative stress response. This model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI3k/Akt pathway. Conclusions It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological network with feedback will need to be further simplified before it can be even considered for practical biological validation.
Wavelet analysis has been widely applied to mechanical faultdiagnosis. 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 faultdiagnosis of two gearboxes. The results show that the proposed framelets can be effectively applied to mechanical faultdiagnosis and is superior to other commonly-used framelets/wavelets.
A model based on PCA (principal component analysis) and a neural network is proposed for the multi-faultdiagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time. PMID:22315537
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. The analysis is performed using the PLS_Toolbox 2.01 with MATLAB, augmented with software which automates the analysis and implements a statistical enhancement that uses confidence limits on the residuals of each variable for fault detection rather than just confidence limits on an overall residual. The newly developed graphical interface identifies and displays each variable's contribution to the faulty behavior of the process; and it aids greatly in analyzing results. The case study analyzed within shows that using the statistical enhancement can reduce the fault detection time, and the automated graphical interface implements the enhancement easily. PMID:15535400
A new approach to faultdiagnosis of gear crack based on ensemble empirical mode decomposition (EEMD) and Hilbert-Huang transform (HHT) technique is presented. Firstly, the time-domain vibration signal of the gearbox with gear crack fault is measured. Then the original vibration signal is separated into intrinsic oscillation modes, using the ensemble empirical mode decomposition. Secondly, Hilbert transform tracks the modulation
The accuracy of fault diagnostic systems for diesel engine-type generators relies on a comparison of the currently extracted sensory features with those captured during normal operation or the so-called “baseline.” However, the baseline is not easily obtained without the required expertise. Even worse, in an attempt to save costs, many of the diesel engine generators in manufacturing plants are second hand or have been purchased from unknown suppliers, meaning that the baseline is unknown. In this paper, a novel vibration-based fault diagnostic method is developed to identify the vital components of a diesel engine that have abnormal clearance. The advantage of this method is that it does not require the comparison of current operating parameters to those collected as the baseline. First, the nominal baseline is obtained via theoretical modeling rather than being actually captured from the sensory signals in a healthy condition. The abnormal clearance is then determined by inspecting the timing of impacts created by the components that had abnormal clearance during operation. To detect the timing of these impacts from vibration signals accurately, soft-re-sampling and empirical mode decomposition (EMD) techniques are employed. These techniques have integrated with our proposed ranged angle (RA) analysis to form a new ranged angle-empirical mode decomposition method (RA-EMD). To verify the effectiveness of the RA-EMD in detecting the impacts and their times of occurrence, their induced vibrations are collected from a series of generators under normal and faulty engine conditions. The results show that this method is capable of extracting the impacts induced by vibrations and is able to determine their times of occurrence accurately even when the impacts have been overwhelmed by other unrelated vibration signals. With the help of the RA-EMD, clearance-related faults, such as incorrect open and closed valve events, worn piston rings and liners, etc., become detectable even without the comparison to the baseline. Hence, proper remedies can be applied to defective diesel engines to ensure that valuable fuel is not wasted due to the incorrect timing of combustion as well as unexpected fatal breakdown, which may cause loss of production or even human casualties, can be minimized.
Li, Yujun; Tse, Peter W.; Yang, Xin; Yang, Jianguo
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.
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.
Faultdiagnosis of rolling mills, especially the main drive gearbox, is of great importance to the high quality products and\\u000a long-term safe operation. However, the useful fault information is usually submerged in heavy background noise under the severe\\u000a condition. Thereby, a novel method based on multiwavelet sliding window neighboring coefficient denoising and optimal blind\\u000a deconvolution is proposed for gearbox fault
Spalling or pitting is the main manifestation of fault development in a bearing during the earlier stages. Previous studies indicated that the vibration signal of a bearing with a spall-like defect may be composed of two parts; the first part originates from the entry of the rolling element into the spall-like area, and the second part refers to the exit from the fault region. The quantitative diagnosis of a spall-like fault of the rolling element bearing can be realised if the entry-exit event times can be accurately calculated. However, the vibration signal of a faulty bearing is usually non-stationary and non-linear with strong background noise interference. Meanwhile, the signal energy from the early spall region is too low to accurately register the features of the entry-exit event in the time domain. In this work, the approximate entropy (ApEn) method and empirical mode decomposition (EMD) are applied to clearly separate the entry-exit events, and thus the size of the spall-like fault is estimated.First, the original acceleration vibration signal is decomposed by EMD, and some useful intrinsic mode function (IMF) components are obtained. Second, the concept of IMF-ApEn is introduced, which can directly reflect the complexity of the IMFs using the actual vibration signal. The IMF-ApEn distributions of different noise signals illustrate that the process of complexity changes when a full spectrum process is split into its IMFs. Third, a unit white noise IMF-ApEn distribution template serves as a sieve to extract the (effective intrinsic mode functions) EIMF components, and thus the entry and exit events in the response signal are distinguished.The IMF-ApEn method is further compared with a previous method (N. Sawalhi's method) to test its superiority. The dynamic effects are investigated when the ball element enters a spall-like region by computer simulation. The simulation and the experimental results show that the approach to the quantitative diagnosis of a rolling element bearing based on IMF-ApEn has higher veracity and good robustness.
Spalling or pitting is the main manifestation of fault development in a bearing during the earlier stages. Previous studies indicated that the vibration signal of a bearing with a spall-like defect may be composed of two parts; the first part originates from the entry of the rolling element into the spall-like area, and the second part refers to the exit from the fault region. The quantitative diagnosis of a spall-like fault of the rolling element bearing can be realised if the entry–exit event times can be accurately calculated. However, the vibration signal of a faulty bearing is usually non-stationary and non-linear with strong background noise interference. Meanwhile, the signal energy from the early spall region is too low to accurately register the features of the entry–exit event in the time domain. In this work, the approximate entropy (ApEn) method and empirical mode decomposition (EMD) are applied to clearly separate the entry–exit events, and thus the size of the spall-like fault is estimated.First, the original acceleration vibration signal is decomposed by EMD, and some useful intrinsic mode function (IMF) components are obtained. Second, the concept of IMF-ApEn is introduced, which can directly reflect the complexity of the IMFs using the actual vibration signal. The IMF-ApEn distributions of different noise signals illustrate that the process of complexity changes when a full spectrum process is split into its IMFs. Third, a unit white noise IMF-ApEn distribution template serves as a sieve to extract the (effective intrinsic mode functions) EIMF components, and thus the entry and exit events in the response signal are distinguished.The IMF-ApEn method is further compared with a previous method (N. Sawalhi's method) to test its superiority. The dynamic effects are investigated when the ball element enters a spall-like region by computer simulation. The simulation and the experimental results show that the approach to the quantitative diagnosis of a rolling element bearing based on IMF-ApEn has higher veracity and good robustness.
The operators of Hydro-Quebec's 9 Regional Control Centres (RCC) are sometimes overloaded by the number of alarm messages produced when automatic controls operate to clear faults. To help operators, Hydro-Quebec has developed an expert system to perform a continuous analysis of alarm messages, automatically detect the application of the protection or restoration control and then produce a concise, real-time diagnosis
The operators of Hydro-Quebec's 9 Regional Control Centres (RCC) are sometimes overloaded by the number of alarm messages produced when automatic controls operate to clear faults. To help operators, Hydro-Quebec has developed an expert system to perform a continuous analysis of alarm messages, automatically detect the application of the protection or restoration control and then produce a concise, real-time diagnosis
In this paper, an automated industrial fish cutting machine, which was developed and tested in the Industrial Automation Laboratory (IAL) of the University of British Columbia, is presented including its hardware structure, control sub-system, faultdiagnosis sub-system and the remote monitoring sub-system. First, the hardware of the machine including the mechanical conveyer system, pneumatic system and the hydraulic system, and
The signal is processed by using empirical mode decomposition (EMD) and Hilbert transformation (HT), which can obtain instantaneous\\u000a frequency, instantaneous amplitude and marginal spectrum as the basis of pattern matching. Simultaneously, the energy distribution\\u000a of signal at each frequency domain can be used to train a neural network as faultdiagnosis tool. However, the influence of\\u000a noise on EMD of
Ping-chen Shen; Yuan Kang; Chun-chieh Wang; Yeon-pun Chang; Hsing-han Lee
In this paper, how to achieve 3D visualization faultdiagnosis system for photoelectric tracking equipment based on open graphic library(OpenGL) is researched. To begin with, details of the system architecture design and implementation are presented. The 3D modelings of all the equipments are built by using 3DSMAX software. Then, the model is transformed into OpenGL programs. This method overcomes the
A robust faultdiagnosis (FD) scheme using Takagi-Sugeno (T-S) neural-fuzzy model and sliding mode technique is presented for a class of nonlinear systems that can be described by T-S fuzzy models. A neural-fuzzy observer and neural-fuzzy sliding mode observer are constructed respectively. A modified back-propagation (BP) algorithm is used to update the parameters of the two observers. Stability of the
In this paper, we present some results obtained from the application of a class of sliding mode observers to the model-based faultdiagnosis problem in non-linear dynamic systems. A Takagi-Sugeno fuzzy model is used to describe the system and then sliding mode observers are designed to estimate the system state vector, from this the diagnostic signal-residual is generated by the
In this paper, a self-learning system based on objectives used in faultdiagnosis expert systems is presented. It depends on the deep knowledge model of the diagnosed system and can improve diagnostic capability by expanding and satisfying the shallow knowledge base. Algorithms and principle of the self-learning system are described in detail. As an application, the self-learning system has been
C. S. Xu; Z. M. Xu; P. D. Xiao; Z. Y. Zhou; S. X. Liu; Z. H. Jiang
Based on the sound intensity analysis, discrete wavelet transform (WT) and the neural network (NN) technique, a combined intelligent method for engine faultdiagnosis (EFD), the so-called wavelet pre-processing neural network (WT-NN), is presented in this paper. Based on the measured multi-condition engine noise signals, a wavelet-based 21-point model for feature extraction of engine noise is established, as is a
Current spacecraft health monitoring and faultdiagnosis practices that involve around-the-clock limit-checking and trend analysis on large amount of telemetry data, do not scale well for future multi-platform space missions due to the presence of larger amount of telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the size of the operations team. The need
Most model-based approaches to faultdiagnosis of discrete-event systems (DESs) require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In this
Faultdiagnosis in reciprocating air compressors is essential for continuous monitoring of their performance and thereby ensuring quality output. Support Vector Machines (SVMs) are machine learning tools based on structural risk minimization principle and have the advantageous characteristic of good generalization. For this reason, four well-known and widely used SVM based methods, one-against-one (OAO), oneagainst-all (OAA), fuzzy decision function (FDF),
When VLSI design and process enter the stage of ultra deep submicron (UDSM), process variations, signal integrity (SI) and design integrity (DI) issues can no longer be ignored. These factors introduce some new problems in VLSI design, test and diagnosis, which increase lime-to-market, time-to-volume and cost for silicon debug. Intermittent scan chain hold-time fault is one of such problems we
The error measurement and diagnosis process of roll grinder NC has dynamic complexity, non-linearity, and comprehensive characteristics. However, presently roll error measurement examination mostly uses the manual examination or single parameter optimization, and the efficiency of faultdiagnosis is also inefficient. In this study, the multi-objective intelligence optimization model (MIOM) is applied to the roller error measurement and diagnosis. The
The objective of this research is to develop to the proof-of-concept stage, a fault tolerant diagnosis system for the RADARSAT-1 attitude control system (ACS) telemetry. The proposed system is using computational intelligence (CI) to detect and isolate faults and also to infer cause of failures from the telemetry data time series history using functional models of satellite ACS. The proposed
A. Joshi; V. Gavriloiu; A. Barua; A. Garabedian; P. Sinha; K. Khorasani
This article presents a robust fault detection and diagnosis scheme for any abrupt and incipient class of faults that can affect the state of a class of nonlinear systems. A nonlinear observer which synthesizes sliding mode techniques and neural state space models is proposed for the purpose of online health monitoring. The sliding mode term is utilized to eliminate the
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 faultdiagnosis of hydraulic pump which are the heart of hydraulic tube tester. Considering that the signal of the hydraulic pump
The core content of rough set theory is introduced, and the discrete method of continuous attribute value based on the Kohonen neural network is given. The rough set theory is used to simplify the attribute parameter reflecting operating conditions of a diesel engine, and a RBF neural network is used to realize automatic fault classification and faultdiagnosis for the
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.
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
The Kurtogram is based on the kurtosis of temporal signals that are filtered by the short-time Fourier transform (STFT), and has proved useful in the diagnosis of bearing faults. To extract transient impulsive signals more effectively, wavelet packet transform is regarded as an alternative method to STFT for signal decomposition. Although kurtosis based on temporal signals is effective under some conditions, its performance is low in the presence of a low signal-to-noise ratio and non-Gaussian noise. This paper proposes an enhanced Kurtogram, the major innovation of which is kurtosis values calculated based on the power spectrum of the envelope of the signals extracted from wavelet packet nodes at different depths. The power spectrum of the envelope of the signals defines the sparse representation of the signals and kurtosis measures the protrusion of the sparse representation. This enhanced Kurtogram helps to determine the location of resonant frequency bands for further demodulation with envelope analysis. The frequency signatures of the envelope signal can then be used to determine the type of fault that has affected a bearing by identifying its characteristic frequency. In many cases, discrete frequency noise always exists and may mask the weak bearing faults. It is usually preferable to remove such discrete frequency noise by using autoregressive filtering before the enhanced Kurtogram is performed. At last, we used a number of simulated bearing fault signals and three real bearing fault signals obtained from an experimental motor to validate the efficiency of these proposed modifications. The results show that both the proposed method and the enhanced Kurtogram are effective in the detection of various bearing faults.
Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate faultdiagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-faultdiagnosis, especially for
Zhixiong Li; Xinping Yan; Chengqing Yuan; Zhongxiao Peng; Li Li
The paper presents the structure and usage mode for an integrated fault-diagnosis and control system for transformers. The faultdiagnosis and control system allows online analysis on a desktop application, a Web application and offline analysis for determining the transformer faults and remedies based on certain symptoms observed on the equipment and their comparison with the survey results of cooling
Vibration signals from the gearbox of a wind turbine are essentially non-stationary and nonlinear in both time and frequency. Empirical Mode Decomposition (EMD) is an ideal method for dealing with this type of signal. Yet the signal containing the fault information was contaminated by the noise, which contains two different types of white noise and impact noise. This makes it
The objective of this research is to investigate the feasibility of utilizing the hybrid method of ensemble empirical mode decomposition (EEMD) and pure empirical mode decomposition (EMD) to efficiently decompose the complicated vibration signals of rotating machinery into a finite number of intrinsic mode functions (IMFs), so that the fault characteristics of the misaligned shaft can be examined in the time-frequency Hilbert spectrum as well as the marginal Hilbert spectrum. The intrawave frequency modulation (FM) phenomenon, which indicates the nonlinear vibration behavior of a misaligned shaft, can be observed in the time-frequency Hilbert spectrum through the Hilbert-Huang transform (HHT) technique. The fault characteristic of shaft misalignment is also featured in terms of the amplitude modulation (AM) phenomenon in the information-containing IMF components that are extracted by the significance test. Through performing the envelope analysis on the information-containing IMF, the marginal Hilbert spectrum of the envelope signal of this IMF component exhibits that the level of shaft misalignment is presented by the level of AM in the IMF. A test bed of a rotor-bearing system is performed experimentally to illustrate both the parallel and angular shaft misalignment conditions as well as the healthy condition. The analysis results show that the proposed approach is capable of diagnosing the misaligned fault of the shaft in rotating machinery and providing a more meaningful physical insight compared with the conventional methods.
In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive faultdiagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.
Zhang Yumin; Lum, Kai-Yew [Temasek Laboratories, National University of Singapore, Singapore 117508 (Singapore); Wang Qingguo [Depa. Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)
In this paper an improved bearing model is developed in order to investigate the vibrations of a ball bearing during run-up. The numerical bearing model was developed with the assumptions that the inner race has only 2 DOF and that the outer race is deformable in the radial direction, and is modelled with finite elements. The centrifugal load effect and the radial clearance are taken into account. The contact force for the balls is described by a nonlinear Hertzian contact deformation. Various surface defects due to local deformations are introduced into the developed model. The detailed geometry of the local defects is modelled as an impressed ellipsoid on the races and as a flattened sphere for the rolling balls. With the developed bearing model the transmission path of the bearing housing can be taken into account, since the outer ring can be coupled with the FE model of the housing. The obtained equations of motion were solved numerically with a modified Newmark time-integration method for the increasing rotational frequency of the shaft. The simulated vibrational response of the bearing with different local faults was used to test the suitability of the envelope analysis technique and the continuous wavelet transformation was used for the bearing-fault identification and classification.
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
1337 miners of iron-ore mines in Krivoi Rog were examined. 1163 of them underwent out-patient and the rest (174 patients) in-patient examination. 28% of miners were found to have peripheral neurovascular disorders. Main clinical signs of peripheral neurovascular syndromes of occupational origin and criteria of the diagnostics were defined. The application of the worked-out pathometric diagnostic tables will considerably increase the accuracy and the safety of the diagnosis (up to 94%), the efficacy of the treatment and quality of prognosis for many occupational diseases presented clinically with peripheral neurovascular syndromes. PMID:15916003
A hard competitive growing neural network (HC-GNN) with shrinkage learning is put forward to detect and diagnose small bearing faults. Structure determination based on supervised learning is an important issue in pattern classification. For that reason, the proposed approach introduces new hidden units whenever necessary and adjusts their shapes to minimize the risk of misclassification. This leads to smaller networks compared to classical radial basis functions or probabilistic neural networks and therefore enables the use of large data sets with satisfactory classification accuracy. This technique is based on the following concepts: (1) growing architecture, (2) dynamic adaptive learning, (3), convergence by means of several criteria, (4) embedded weighted feature selection, and (5) optimized network structure. HC-GNN consists of two main stages and runs in an iterative way. The first stage learns weighted selected parameters to well-known classes while the second stage associates the testing parameters of unknown samples to the learned classes. This approach is applied on a machinery system with different small bearing faults at various speeds and loads. The challenge is to detect and diagnose these faults regardless of the motor’s shaft speed. Obtained results are analyzed, explained and compared with various techniques that have been widely investigated in diagnosis area.
Low frequent vibrations may cause from disturbing up to damaging effects. There is no precise distinction between structure-borne sound and vibrations. However - depending on the frequency range - measurements and predictions require different techniques. In a wide frequency range, the generation, transmission and propagation of vibrations can be investigated similar to structure-borne sound (see Chap. 9).
This paper mainly introduces the basic principles, the methods and the applications of infrared technique in the diagnosis and prediction of diesel engine exhaust faults. The test-bed for monitoring diesel engine exhaust faults by thermal infrared imager has been designed. In different running conditions, the exterior surface radiation temperatures of the exhaust pipe of the 6135G-1 diesel engine have been measured by infrared imaging system. According to the principle of infrared temperature measurement, the real temperatures of the exterior surface of the exhaust pipe have been calculated. Based on the principle of heat transfer, the method of calculating the exhaust temperatures according to the exterior surface radiation temperatures of exhaust pipe measured by thermal infrared imager is built. The relationship between diesel engine exhaust temperatures and faults has been analyzed. It is shown that the application of infrared inspection and diagnosis to the identifying of diesel engine exhaust faults is feasible and effective.
The use of magnetic bearings in rotating machinery provides contact-free rotor support, and allows vibration control using both closed-loop and open-loop strategies. One of the simplest and most eVective methods to reduce synchronous lateral vibration when using magnetic bearings is through an open-loop adaptive control technique, in which the amplitude and phase of synchronous magnetic control forces are adjusted automatically
The torsional vibrations calculation of Diesel engines is usually performed for different speeds of revolutions but for uniform\\u000a operation and behaviour of each cylinder. This condition is true only for new of very well maintained engines but generally\\u000a the different cylinders operate with considerable deviations from its design conditions. This situation may influence strongly\\u000a the torsional vibrations of the system,
The traction power supply system of the High-speed Maglev Transrapid plays an important role as the power supply interface\\u000a between a general power system and the Maglev Transrapid. Most conventional diagnosis systems are expert systems based on\\u000a experiences which can not diagnose faults beyond those captured by the experiences. In this paper, model-based diagnosis (MBD)\\u000a as a cognitive diagnosis method
A measurement instrument for on-line fault detection and diagnosis is proposed. It is based on the implementation of a neural network algorithm on a processor specialized in digital signal processing and provided with suitable data acquisition and generation units. Two specific implementations are detailed. The former uses the neural-network to simulate on-line the correct system behavior, thus allowing the fault
Andrea Bernieri; Giovanni Betta; Consolatina Liguori
The vibration characteristics of a rub-impact rotor system with different parameters are investigated using Hil-bert-Huang Transform (HHT)-a relative novel time-frequency analysis method. Firstly, the rotor with rub-impact at fixed limiter is modeled by finite element method (FEM). Then, system simulation signals at different rotating speeds, rub-impact clearances and rub-impact rod stiffness are obtained based on the FE model, moreover which are analyzed by using HHT along with Fast Fourier Transform (FFT) and shaft center orbit methods. The results show that the effects of variables on system motion patterns, response properties and rub-impact severity differ in degrees. Some conclusions are achieved which may be useful to detect rub-impact fault accurately and effectively.
Since there are not enough fault data in historical data sets, it is very difficult to diagnose faults for batch processes. In addition, a complete batch trajectory can be obtained till the end of its operation. In order to overcome the need for estimated or filled up future unmeasured values in the online faultdiagnosis, sufficiently utilize the finite information
This paper presents a variable speed ac drive based on a permanent magnet synchronous motor, supplied by a three-phase fault-tolerant power converter. In order to achieve this, beyond the main routines, the control system integrates a reliable and simple algorithm for real-time diagnostics of inverter open-circuit faults. This algorithm performs an important role since it is able to detect an
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 faultdiagnosis of the roller bearings of the finishing mills in a plant. This application proved the validity and practicality of this method.
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.
This paper deals with the use of a new diagnostic technique, based on the multiple reference frame theory, for the diagnosis of stator winding faults in a direct torque controlled (DTC) induction motor drive. The theoretical aspects underlying the use of this diagnostic technique are presented but a major emphasis is given to the integration of the diagnostic system into
This paper presents an incipient faultdiagnosis 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
This paper proposes a robust loss function that penalizes hybrid noise (i.e., Gaussian noise, singularity points, and larger magnitude noise) in a complex fuzzy fault-diagnosis system. A mapping relationship between fuzzy numbers and crisp real numbers that allows a fuzzy sample set to be transformed into a crisp real sample set is also presented. Furthermore, the paper proposes a novel
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.
The numerical modelling and process simulation for the faultdiagnosis 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
Machinery condition monitoring is rapidly finding applications in all branches of industry. In particular, vibration monitoring is playing an increasingly important role as a tool for assisting with predictive and preventive maintenance and for improving operation efficiency of plant. Condition monitoring systems are used for the detection of incipient failure and the diagnosis of the nature of faults in operating machinery. However, for these systems to be reliable an improved understanding is required of the vibration signatures produced by machinery failure mechanisms and of methods for the interpretation of these signals. Many types of fault produce vibration signals which are impulsive in nature and which may be buried in background noise. A method is described for simulating this type of signal and modelling the various stages of incipient failure. Statistical and spectral analysis are used to describe the fault development. The influence of machinery frequency response characteristics on signal transmission from the damaged are to the measurement point are also considered.
Conventional fault diagnostic system design is typically performance-driven, which has several drawbacks. The most significant one is that the performance-driven design may result in an increase on overall maintenance costs. To address these problems, this thesis introduces a novel design philosophy for fault diagnostic systems. Under this new design philosophy, a fault diagnostic system design is casting as an optimization
A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, analytical redundant relations (ARR) and observer designs. Structural considerations on the system are used to identify four subsystems each sensitive to a subset of the faults under consideration. Either an
C. S. Kallesoe; Roozbeh Izadi-Zamanabadi; Henrik Rasmussen; Vincent Cocquempot
The paper describes a system for automated detection of incipient faults in induction machines. The system is based on the Motor Current Signature Analysis method (MCSA) and aimed to be applied in a thermal electric power plant in south Brazil. First, the mechanism of fault evolution is introduced and clarified regarding the most common induction motor faults: stator winding short-circuits,
Daniel da Silva Gazzana; LuAlberto Pereira; D. Fernandes
Online monitoring of rotary machines, like induction motors, can effectively diagnosis electrical and mechanical faults. The origin of most recurrent faults in rotary machines is in the components: bearings, stator, rotor and others. Different methodologies based on current and vibration monitoring have been proposed using FFT and wavelet analysis for preventive monitoring of induction motors resulting in countless techniques for
E. Cabal-Yepez; Roque A. Osornio-Rios; René de Jesús Romero-Troncoso; J. R. Razo-Hernandez; R. Lopez-Garcia
Aiming at the features of the engaging vibration's direct effects on the rotary speed of axis, this paper puts forward the faultsdiagnosis method of gearbox using amplitude demodulation technology to analyze the signals of rotary speed. First, the signals of rotary speed of gearbox are synchronously sampled in time domain; Next, the signals of torsional vibration are worked out
Zhang Qing-feng; Tang Li-wei; Cui Xiu-mei; Hou Cai-hong
The authors studied the innovative applications of the inductively coupled plasma-atomic emission spectrometry in automotive hydraulic power steering system faultdiagnosis. After having determined Fe, Cu and Al content in the four groups of Buick Regal 2.4 main metal power-steering fluid whose travel course was respectively 2-9 thousand kilometers, 11-18 thousand kilometers, 22-29 thousandkilometers, and 31-40 thousand kilometers, and the database of primary metal content in the Buick Regal 2.4 different mileage power-steering fluid was established. The research discovered that the main metal content increased with increasing mileage and its normal level is between the two trend lines. Determination of the power-steering fluid main metal content and comparison with its database value can not only judge the wear condition of the automotive hydraulic power steering system and maintain timely to avoid the traffic accident, but also help the automobile detection and maintenance personnel to diagnose failure reasons without disintegration. This reduced vehicle maintenance costs, and improved service quality. PMID:23586258
This paper deals with the use of a new diagnostic technique based on the multiple reference frames theory for the diagnosis of stator winding faults in a direct-torque-controlled (DTC) induction motor drive. The theoretical aspects underlying the use of this diagnostic technique are presented but a major emphasis is given to the integration of the diagnostic system into the digital-signal-processor
Sérgio M. A. Cruz; Hamid A. Toliyat; A. J. Marques Cardoso
A robust faultdiagnosis (FD) scheme integrating Takagi-Sugeno (T-S) fuzzy-neural models and sliding mode technique is presented for a class of nonlinear systems that can be described by T-S fuzzy models. A fuzzy-neural observer and a fuzzy-neural sliding mode observer are constructed respectively. A modified back-propagation (BP) algorithm is used to update the parameters of these two observers. Finally, the
Through the analysis of electric drive system of a cold-rolling steel plant and selecting detection signal reasonably, the on-line monitoring system has been exploited. It possesses the functions of real-time data display, alarm, sample data storage, data acquisition, parameter setting and others. By using MATLAB-Simulink tools, the simulation system has been built, which is for faultdiagnosis of three-phase induction
Nonstationary signal analysis is one of the main topics in the field of machinery faultdiagnosis. 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 faultdiagnosis. 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 faultdiagnosis of machinery have been examined. Some examples have also been provided to illustrate their performance.
This paper describes the use of multiple reference frames for the diagnosis of stator, rotor, and eccentricity faults in line-fed and direct torque controlled (DTC) inverter-fed induction motors. The use of this new technique, which was proposed by the authors for the diagnosis of inter-turn short circuits, is extended for the detection and classification of different types of faults. Each
Dissolved gas analysis (DGA) is one of the most useful techniques to detect the incipient faults of power transformer. 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 natures. In this paper, a novel cerebellar model articulation controller (CMAC) neural network (NN) method
Dissolved gas analysis (DGA) of transformer oil has been one of the most useful techniques to detect the incipient faults. Various methods, such as the IEC codes, have been developed to interpret DGA results directly obtained from a chromatographer. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when more than one fault exists
In this research, we modeled fault identification performance in a dynamic process control task using a multivariate Lens Model. This research demonstrates how multivariate Lens Model can be applied to capture dynamic aspects and policies of individuals when they are making judgments over multiple criteria - in this case, multiple categories of faults. Results and modeling analysis to date indicate
In an AC motor, the quick detection of an initially small fault is important for preventing any consequent large fault. Various detection approaches have been proposed in previous papers, for example, by the Park vector (PV), AI techniques, wavelet analysis, and negative-sequence analysis. This paper proposes a method for diagnosing the stator-winding faults of an induction motor by the direct detection of its negative-sequence current. Before starting the diagnosis, the asymmetry admittances for the considered fault cases are obtained by analysis or simulation. The amplitude and phase of the positive-sequence voltage, Vp, and of the positive-sequence current, Ip, are extracted from the voltage PV and current PV, respectively. The amplitude and phase of the negative-sequence, In, are extracted from the residue. The asymmetry admittance, Ya, is calculated from In and Vp. When the positive-sequence admittance is known, Ya can also be calculated from Yp, Ip, and In. These steps are repeated for each sample time and the motor condition is diagnosed according to the variations in the Ya values. The simulation and experimental results are also shown and the proposed method is investigated and validated.
In this paper the emf induced in a search coil is measured in order to detect faults in an induction motor. Anomalous operations caused by a broken rotor bar or a faulty stator cutting phase are analysed. Starting from a theoretical analysis of the radial field spectrum associated with these faults, the measurement of the corresponding emf in the search antenna is examined. The saturation and harmonic components of the permeance produced by the slotting effect are taken into account. Their interactions are analysed, allowing the identification of the frequencies which are of interest for the detection of stator cutting phase faults in a working induction motor.
Belkhayat, D.; Romary, R.; El Adnani, M.; Corton, R.; Brudny, J. F.
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 dea...
The fuzzy logic and neural networks are combined in this paper, setting up the fuzzy neural network (FNN); meanwhile, the distinct differences and connections between the fuzzy logic and neural network are compared. Furthermore, the algorithm and structure of the FNN are introduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to the nuclear power plant, and the intelligence fault diagnostic system of the nuclear power plant is built based on the FNN. The fault symptoms and the possibility of the inverted U-tube break accident of steam generator are discussed. In order to test the system’s validity, the inverted U-tube break accident of steam generator is used as an example and many simulation experiments are performed. The test result shows that the FNN can identify the fault.
A new combined method based on wavelet transformation, fuzzy logic and neuro-networks is proposed for faultdiagnosis of a triplex. The failure characteristics of the fluid- and dynamic-end can be divided into wavelet transform in different scales at the same time (in: Jun Zhu et al. (Eds.), Proceedings of an International Conference on Condition Monitoring. National Defense Industry Press, Beijing, 1997, pp. 271-275). Therefore, the characteristic variables can be constructed making use of the coefficients of Edgeworth asymptotic spectrum expansion formula and fuzzified to train the neuro-network to identify the faults of fluid- and dynamic-end of triplex pump in fuzzy domain. Tests indicate that the information of wavelet transformation in scale 2 is related to the meshing state of the gear and the information in scales 4 and 5 is related to the running state of fluid-end. Good agreement between analytical and experimental results has been obtained.
High-frequency stress wave analysis was used as characteristic parameter to detect the early stages of the loss of mechanical integrity in low-speed machinery in the paper. The background noise was eliminated using wavelet decomposition and the feature frequency of fault stress waves was extracted. Firstly, according to the characters of the fault stress waves obtained from a steel mill, db6
In the paper new methods of fault localisation and identification in linear electronic circuits (two-port or multi-port type) based on bilinear transformations in multi-dimensional spaces are presented. The novelty of these methods lies in transferring a family of identification loci from a plane to multi-dimensional spaces. It implies greater distances between the loci and, in consequence, better fault resolution as
This paper studies an absolute positioning sensor for a high-speed maglev train and its faultdiagnosis 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.
Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the faultdiagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the faultdiagnosis and the condition monitoring of machine tools.
An advanced vibration monitoring system (VMS) consisting of on-board and on-ground tasks is presented in this paper. The on-board part of the VMS includes the detection of vibration incidents by monitoring of defined vibration amplitude values and compari...
This article makes use of marine diesel engine local vibration signals. These signals have properties of non-stationary, non-Gaussian and low signal-to-noise. These unknown sources about diesel vibration signals can be estimated and recurrent according to blind source separation (bss) algorithm and combining other time domain, frequency domain analysis method. The independent signal source can also be identified through the linear
In order to compensate the worldwide energy consumption that is still rising, the wind energy is becoming more and more important. For this reason, the analysis of wind energy conversion systems, in which the occurrence of faults has a high negative impact, becomes a very important issue. Considering this, the aim of this paper is to present some diagnostic methods
Nuno M. A. Freire; Jorge O. Estima; A. J. Marques Cardoso
The bond graph method represents a unified approach for modeling engineering systems. The main idea is that power transfer bonds the components of a system. The bond graph model is the same for both quantitative representation, in which parameters have numerical values, and qualitative approach, in which they are classified qualitatively. To infer the cause of faults using a qualitative
In this paper, faults in induction motors were diagnosed by using the Common Vector Approach (CVA). CVA is a well-known subspace-based pattern recognition method that is widely used in speech recognition, speaker recognition, and image recognition problems. In order to analyze the performance of CVA, a database including the current signals of six identical induction motors were used. One of
This paper proposes the use of the park transformation mass center applied to the stator currents as a method for diagnosing the occurrence of stator winding faults in induction motor. Induction motor stator currents are first measured and recorded. Then, the park transform is applied to the obtained currents in order to obtain a specific pattern that allows the identification
Based on improve the drawbacks of Ensemble Empirical Mode Decomposition (EEMD), such as mode mixing and end effect problem, post-processing of EEMD which was improved with HHT approach to solve the problem in this paper. Once the Intrinsic Mode Functions (IMFs) are obtained from the decomposition process, the crucial step is to extract the fault features from the information-contained IMFs.
A method for diagnosing component faults of jet engines is presented. It uses nonlinear gas path analysis techniques to determine the values of health parameters, with the help of a suitably formulated engine simulation model. The incentive of the method is to achieve the determination of the values of component health indices when a limited number of measured quantities is
The benefits of machine condition monitoring have been widely recognized as superior with respect to other alternative maintenance approaches. Condition monitoring is an operational strategy for machine integrity assessment, fault identification and life extension. The cost-benefit ratio will be reduced in progress owing to the commercial diagnostic environment availability. This paper presents the implementation of a diagnostic procedure to detect
L Collamatit; F. Filippetti; G. Franceschini; S. Pirani; C. Tassoni
In actural measurement, it is difficult to find the useful signal among background noise. This paper suggests a new method to pick up signals and find fault information by using Hilbet Huang Transmation. In signal processing, at first small wave transmation was used as low-pass filter, thus part of noise can be filtrated. Then some of intrinsic mode functions have
In this paper, BP neural network is applied to fault pattern recognition of pipeline leakage. When the pipeline pressure falls suddenly, the pressure sensors on both sides of the pipeline get pressure signals. The fundamental principal of using wavelet transform to decompose the pressure signal is introduced, using wavelet transform in pressure de-noising and pipeline feature vector extraction, and the
The accuracy of fault diagnostic systems for diesel engine-type generators relies on a comparison of the currently extracted sensory features with those captured during normal operation or the so-called “baseline.” However, the baseline is not easily obtained without the required expertise. Even worse, in an attempt to save costs, many of the diesel engine generators in manufacturing plants are second
We have developed a neural-network based on fault diagnostic system for diesel engine combustion system. Grounded on the presented evolutionary algorithm, neural networks automatically adjust the network parameters (connection weights and bias terms). Computer simulation experimental results confirm that the proposed method has high diagnostically accuracy.
This paper describes the development and evaluation of features and virtual sensors that form the basis of a methodology for detecting and diagnosing multiple-simultaneous faults in vapor compression air conditioning equipment. The features were developed based upon a physical understanding of the system, cost considerations, and heuristics derived from experimental data and modeling results. Virtual sensors were developed in order
The FDI and DX communities have developed complementary approaches that exploit structural relations in the system model to find ecient solutions for the residual generation and residual evaluation steps in fault detection and isolation in dynamic systems. This paper compares three dierent structural techniques, two from the DX community and one from the FDI community. To simplify our comparison, we
In order to prevent the harm of corona, the power sector needs to detect and analyze the fault of corona discharge. This paper put forward a single-channel structure corona detection system based on solar-blind UV detection technology, which is able to locate corona discharge effectively. And the absolute discharge area formula has been derived to quantify the corona discharge intensity.
Ru-jun Xu; Li-xin Ma; Bo Hu; He-ran Ma; Bo-hao Tao
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 faultdiagnosis 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.
In this paper, the Gabor wavelet is used for wavelet filter based de-noising the vibration signal measured from faulty bearings. In this approach the parameters of the daughter wavelet corresponding to center frequency and bandwidth namely scale and shape-factor should be selected properly. The ratio of the geometric mean to the arithmetic mean of the wavelet coefficient moduli called smoothness
? Abstract - This paper investigates the recent advances on digital signal processing techniques for induction machines diagnosis. Since non-invasive sensors offer a relatively simple and cost effective faultdiagnosis, more emphasis is given to stator current analysis rather than vibration or acoustic analysis in induction machines. Here, further interest has been paid on modern signal processing techniques with a
Misalignment is one of the most commonly observed faults in rotating machines. However, there have been relatively limited research efforts in the past to understand its effect on overall dynamics of the rotor system. In the existing literature, there is confusing spectral information on the rotor vibration characteristics of misalignment. The present study is aimed at understanding the dynamics of misaligned rotors and reducing the ambiguity so as to improve the reliability of the misalignment faultdiagnosis. Influence of misalignment and its type on the forcing characteristics of flexible coupling is investigated followed by experimental investigation of the vibration response of misaligned coupled rotors supported on rolling element bearings. Steady-state vibration response at integer fraction of the first bending natural frequency is investigated. Effects of types of misalignments, i.e. parallel and angular misalignments, are investigated. The conventional Fourier spectrum (i.e. FFT) has limitations in revealing the directional nature of the vibrations arising out of rotor faults. In addition, it has been observed that several other rotor faults generate higher harmonics in the Fourier spectrum and hence there could be a level of uncertainty in the diagnosis when other faults are also suspect. The present work through use of full spectra has shown possibility of diagnosing misalignment through unique vibration features exhibited in the full spectra (i.e. forward/backward whirl). This provides an important tool to separate faults that generate similar frequency spectra (e.g. crack and misalignment) and lead to a more reliable misalignment diagnosis. Full spectra and orbit plots are efficiently used to reveal the unique nature of misalignment fault not clearly brought out by the previous studies, and new misalignment diagnostics recommendations are proposed.
Reliable methods for diagnosing faults and detecting degraded performance in gas turbine engines are continually being sought. In this paper, a model-based technique is applied to the problem of detect in degraded performance in a military turbofan engine from take-off acceleration-type transients. In the past, difficulty has been experienced in isolating the effects of some of the physical processes involved. One such effect is the influence of the bulk metal temperature on the measured engine parameters during large power excursions. It will be shown that the model-based technique provides a simple and convenient way of separating this effect from the faster dynamic components. The important conclusion from this work is at good fault coverage can be gleaned from the resultant pseudo-steady-state gain estimates derived in this way.
Merrington, G.L. (Aeronautical Research Lab., Victoria (Australia))
A computer-aided diagnostic technique has been applied to on-line signal validation in an operating nuclear reactor. To avoid installation of additional redundant sensors for the sole purpose of fault isolation, a real-time model of nuclear instrumentation and the thermal-hydraulic process in the primary coolant loop was developed and experimentally validated. The model provides analytically redundant information sufficient for isolation of
\\u000a Recent shop floor paradigms and approaches increasingly advocate the use of distributed systems and architectures. Plug-ability,\\u000a Fault Tolerance, Robustness and Preparedness are characteristics believed to emerge by instantiation of these fundamentally\\u000a new design approaches. However these features, when effectively present, often come at the cost of a greater system complexity.\\u000a Enclosed in this complexity increase is a plethora on unforeseen
Luis Ribeiro; José Barata; Bruno Alves; João Ferreira
Yield analysis of sub-micron devices has become an ever-increasing challenge. The difficulty is compounded by the lack of in-line inspection data as many companies adopt foundry or fab-less model. In this scenario failure analysis is becoming increasingly critical to help drive yields. Failure analysis is a process of fault isolation or a method of isolating failures as precisely as possible
This paper proposes a new methodology for detecting and diagnosing faults found in heavy-duty diesel engines based upon spectrometric analysis of lubrication samples and is compared against a conventional method, the redline limits, which is utilized in a number of major laboratories in the U.K. and across Europe. The proposed method applies computational power to a well-known maintenance technique and
Ian Morgan; Honghai Liu; Bernardo Tormos; Antonio Sala
\\u000a The main purpose of this paper is to propose an intelligent fault diagnostic method for photovoltaic (PV) systems. First,\\u000a Solar Pro software package was used to simulate a photovoltaic system for gathering power generation data of photovoltaic\\u000a modules during normal operations and malfunctions. Then, the collected power generation data was used to construct matter-element\\u000a models based on extension theory for
A new statistical diagnosis method for a batch process is proposed. The proposed method consists of two phases: off- line model building and on-line diagnosis. The off-line model building phase constructs an empirical model, called a discriminant model, using various past batch runs. When an out-of-control state of a new batch is detected, the on-line diagnosis phase is initiated. The
The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results.
The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results. PMID:22163938
A method is presented for process faultdiagnosis 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
Rolling element bearings are among the most common components to be found in industrial rotating machinery. They are found in industries from agriculture to aerospace, in equipment as diverse as paper mill rollers to the Space Shuttle Main Engine Turbomachinery. There has been much written on the subject of bearing vibration monitoring over the last twenty five years. This report attempts to summarize the underlying science of rolling element bearings across these diverse applications from the point of view of machine condition monitoring using vibration analysis. The key factors which are addressed in this report include the underlying science of bearing vibration, bearing kinematics and dynamics, bearing life, vibration measurement, signal processing techniques and prognosis of bearing failure.
Most high-rise buildings constructed of steel or steel reinforced concrete have to install various vital equipments. Among\\u000a these equipments machinery noise is especially annoying for accommodation close to them. In attempting to control the machine-induced\\u000a structure-borne noise and vibration, the methodology by employing mobility functions to identify the dominant frequency band\\u000a of vibrational power flow transmission and to assess the
In this study, a novel faultdiagnosis system for rotating machinery using thermal imaging is proposed. This system consists of bi-dimensional empirical mode decomposition (BEMD) for image enhancement, a generalized discriminant analysis (GDA) for feature reduction, and a relevance vector machine (RVM) for fault classification. Firstly, the thermal image obtained from machine conditions is decomposed into intrinsic mode functions (IMFs) by using BEMD. At each decomposed level, the IMF is expanded and fused with the residue by gray-scale transformation and principal component analysis fusion technique, respectively. The enhanced image is then formed by the improved IMFs in reconstruction process. Subsequently, feature extraction is applied for the enhanced images to obtain histogram features which characterize the thermal image and contain useful information for diagnosis. The high dimensionality of the achieved feature set can be reduced by GDA implementation. Moreover, GDA also assists in the increase of the feature cluster separation. Finally, the diagnostic results are performed by RVM. The proposed system is applied and validated with the thermal images of a fault simulator. A comparative study of the classification results obtained from RVM, support vector machines, and adaptive neuro-fuzzy inference system is also performed to appraise the accuracy of these models. The results show that the proposed diagnosis system is capable of improving the classification accuracy and efficiently assisting in rotating machinery faultdiagnosis.
Tran, Van Tung; Yang, Bo-Suk; Gu, Fengshou; Ball, Andrew
The paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem. A short description of the theory of adaptive filters is presented. When an adaptive filter uses the delayed version of the input signal as the reference signal, it is possible to divide the signal into a deterministic (gear and shaft related)
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
This paper presents results obtained in an AI research effort in the industrial field of Nuclear Power Plants (NPP): malfunction diagnosis of the Emergency Feedwater System (EFWS) of a NPP. An expert system was developed which utilizes qualitative techniques for modeling the system and heuristic rules for generating causal explanations of an observed malfunction. The operation of the system, the
Many methods of diagnosing internal combustion engines have been already worked out. They can be divided into methods using working processes and methods using leftover processes. Working processes give information about general condition of internal combustion engine. Leftover processes give information about condition of particular subassemblies and kinematic couples; hence they are used as autonomous processes or as processes supporting other diagnostic methods. Methods based on analysis of vibrations and noise changes to determine technical condition of object are named as vibroacoustic diagnostics. In papers about vibroacoustic diagnostics of engine, problems connected with difficulty to select test point and to define diagnostic parameters containing essential information about engine's condition, are most often omitted. Selection of engine's working parameters and conditions of taking measurements or registering vibration signal are usually based on references, researcher's experience or intuition. General assumptions about taking measurements of signal closest to its source are most often used. Application of vibrations and noise generated by working combustion engine to assess correctness of its work and technical condition has many advantages. Vibroacoustic processes are a good carrier of diagnostic information for the following reasons: - high information capacity, - high speed of data transfer (signal's component describing change in object's condition is visible the moment the inefficiency occurs), - vibration signal reflects all significant processes in combustion engine, - measurement of vibrations and noise does not require special preparations of technical object for tests and can be carried out during regular operations. This article presents a new approach to vibroacoustic diagnostics of internal combustion engine. Selection of test points of vibration on the basis of impact tests' results was suggested. Those results were applied to build dynamic models of systems of combustion engines. Such model was used to assess condition of the systems.
Among the topics discussed are: turbomachinery tip rubs and interactive casting resonances; the transverse vibrational characteristics of an externally damaged pipe and performance of vibration monitoring for the prevention of gas turbine airfoil failures. Consideration is also given to: velocity response analysis of a spherical roller bearing; vibration monitoring of large pumps via a remote satellite stations; dynamic edge strain prediction in stiffened honeycomb panels; and fault-diagnosis for turbo-machines by means of vibration monitoring. Additional topics discussed include: early detection and diagnosis of faults rolling element bearings; spectral analysis of damped vibration by means of a modified version of the Prony method and guidelines for forced vibration in machine tools for use in protective maintenance and analysis.
Dependability is an important attribute for microfluidic biochips that are used for safety-critical applications such as point-of-care health assessment, air-quality monitoring, and food-safety testing. Therefore, these devices must be adequately tested after manufacture and during bioassay operations. Moreover, since disposable biochips are being targeted for a highly competitive and low-cost market segment, test and diagnosis methods should be inexpensive, quick,
Rolling element bearings are among the most common components to be found in industrial rotating machinery. They are found in industries from agriculture to aerospace, in equipment as diverse as paper mill rollers to the Space Shuttle Main Engine Turbomachinery. There has been much written on the subject of bearing vibration monitoring over the last twenty five years. This report
In this paper, we introduce a modified form of the correlation integral developed by Grassberger and Procaccia referred to as the partial correlation integral, which can be computed in real time. The partial correlation integral algorithm is then used to analyze machine vibration data obtained throughout a life test of a rolling element bearing. From the experimental results, the dimensional
The main contribution in the hereby presented paper is to investigate the fault detection capability of a motor current signature analysis by expanding its scope to include the gearbox, and not only the induction motor. Detecting bearing faults outside the induction motor through the stator current analysis represents an interesting alternative to traditional vibration analysis. Bearing faults cause changes in the stator current spectrum that can be used for faultdiagnosis purposes. A time-domain simulation of the drivetrain model is developed. The drivetrain system consists of a loaded single stage gearbox driven by a line-fed induction motor. Three typical bearing faults in the gearbox are addressed, i.e. defects in the outer raceway, the inner raceway, and the rolling element. The interaction with the fault is modelled by means of kinematical and mechanical relations. The fault region is modelled in order to achieve gradual loss and gain of contact. A bearing fault generates an additional torque component that varies at the specific bearing defect frequency. The presented dynamic electromagnetic dq-model of an induction motor is adjusted for diagnostic purpose and considers such torque variations. The bearing fault is detected as a phase modulation of the stator current sine wave at the expected bearing defect frequency.
Cibulka, Jaroslav; Ebbesen, Morten K.; Robbersmyr, Kjell G.
As an adaptive time-frequency-energy representation analysis method, empirical mode decomposition (EMD) has the attractive feature of robustness in the presence of nonlinear and non-stationary data. It is evident that an appropriate definition of baseline (or called mean curve) of data plays a crucial role in EMD scheme. By defining several baselines, an adaptive data-driven analysis approach called generalized empirical mode decomposition (GEMD) is proposed in this paper. In the GEMD method, different baselines are firstly defined and separately subtracted from the original data, and then different pre-generated intrinsic mode functions (pre-GIMFs) are obtained. The GIMF component is defined as the optimal pre-GIMF among the obtained ones with the smallest rate of frequency bandwidth to center frequency. Next, the GIMF is subtracted from the original data and a residue is obtained, which is further regarded as the original data to repeat the sifting process until a constant or monotonic residue is derived. Since the GIMF in each frequency-band is the best among different pre-GIMFs derived from EMD and other EMD like methods, the GEMD results are best as well. Besides, a demodulating method called empirical envelope demodulation (EED) is introduced and employed to analyze the GIMFs in time-frequency domain. Furthermore, GEMD and EED are contrasted with the original Hilbert-Huang Transform (HHT) by analyzing simulation and rolling bearing vibration signals. The analysis results indicate that the proposed method consisting of GEMD and EED is superior to the original HHT at least in restraining the boundary effect, gaining a better frequency resolution and more accurate components and time frequency distribution.
Diagnostics of rolling element bearings involves a combination of different techniques of signal enhancing and analysis. The most common procedure presents a first step of order tracking and synchronous averaging, able to remove the undesired components, synchronous with the shaft harmonics, from the signal, and a final step of envelope analysis to obtain the squared envelope spectrum. This indicator has been studied thoroughly, and statistically based criteria have been obtained, in order to identify damaged bearings. The statistical thresholds are valid only if all the deterministic components in the signal have been removed. Unfortunately, in various industrial applications, characterized by heterogeneous vibration sources, the first step of synchronous averaging is not sufficient to eliminate completely the deterministic components and an additional step of pre-whitening is needed before the envelope analysis. Different techniques have been proposed in the past with this aim: The most widely spread are linear prediction filters and spectral kurtosis. Recently, a new technique for pre-whitening has been proposed, based on cepstral analysis: the so-called cepstrum pre-whitening. Owing to its low computational requirements and its simplicity, it seems a good candidate to perform the intermediate pre-whitening step in an automatic damage recognition algorithm. In this paper, the effectiveness of the new technique will be tested on the data measured on a full-scale industrial bearing test-rig, able to reproduce the harsh conditions of operation. A benchmark comparison with the traditional pre-whitening techniques will be made, as a final step for the verification of the potentiality of the cepstrum pre-whitening.
Borghesani, P.; Pennacchi, P.; Randall, R. B.; Sawalhi, N.; Ricci, R.
Human space travel is inherently dangerous. Hazardous conditions will exist. Real time health monitoring of critical subsystems is essential for providing a safe abort timeline in the event of a catastrophic subsystem failure. In this paper, we discuss a practical and cost effective process for developing critical subsystem failure detection, diagnosis and response (FDDR). We also present the results of a real time health monitoring simulation of a propellant ullage pressurization subsystem failure. The health monitoring development process identifies hazards, isolates hazard causes, defines software partitioning requirements and quantifies software algorithm development. The process provides a means to establish the number and placement of sensors necessary to provide real time health monitoring. We discuss how health monitoring software tracks subsystem control commands, interprets off-nominal operational sensor data, predicts failure propagation timelines, corroborate failures predictions and formats failure protocol.
Edwards, John L.; Beekman, Randy M.; Buchanan, David B.; Farner, Scott; Gershzohn, Gary R.; Khuzadi, Mbuyi; Mikula, D. F.; Nissen, Gerry; Peck, James; Taylor, Shaun
In this paper, a vibration testing and health monitoring system based on an impulse response excited by laser ablation is proposed to detect bolted joint loosening. A high power Nd: YAG pulse laser is used to generate an ideal impulse on a structural surface which offers the potential to measure high frequency vibration responses on the structure. A health monitoring apparatus is developed with this vibration testing system and a damage detecting algorithm. The joint loosening can be estimated by detecting fluctuations of the high frequency response with the health monitoring system. Additionally, a finite element model of bolted joints is proposed by using three-dimensional elements with a pretension force applied and with contact between components taken into account to support the bolt loosening detection method. Frequency responses obtained from the finite element analysis and the experiments using the laser excitation are in good agreement. The bolt loosening can be detected and identified by introducing a damage index by statistical evaluations of the frequency response data using the Recognition-Taguchi method. The effectiveness of the present approach is verified by simulations and experimental results, which are able to detect and identify loose bolt positions in a six-bolt joint cantilever.
This paper investigates the faults of ball bearings and their effect on both machine vibration and stator current. Bearing faults are categorized into two main groups according to the fault signature produced and appear in machine vibration and stator current. They are single-point defect and generalized roughness. Single-point defect produces one of the four predictable characteristic fault frequencies depending on
Fault symptoms of running gearboxes must be detected as early as possible to avoid serious accidents. Diverse advanced methods are developed for this challenging task. However, for multiwavelet transforms, the fixed basis functions independent of the input dynamic response signals will possibly reduce the accuracy of faultdiagnosis. Meanwhile, for multiwavelet denoising technique, the universal threshold denoising tends to overkill important but weak features in gear faultdiagnosis. To overcome the shortcoming, a novel method incorporating customized (i.e., signal-based) multiwavelet lifting schemes with sliding window denoising is proposed in this paper. On the basis of Hermite spline interpolation, various vector prediction and update operators with the desirable properties of biorthogonality, symmetry, short support and vanishing moments are constructed. The customized lifting-based multiwavelets for feature matching are chosen by the minimum entropy principle. Due to the periodic characteristics of gearbox vibration signals, sliding window denoising favorable to retain valuable information as much as possible is employed to extract and identify the fault features in gearbox signals. The proposed method is applied to simulation experiments, gear faultdiagnosis and normal gear detection to testify the efficiency and reliability. The results show that the method involving the selection of appropriate basis functions and the proper feature extraction technique could act as an effective and promising tool for gear fault detection.
This paper describes the use of the extended Park's vector approach (EPVA) for diagnosing the occurrence of stator winding faults in operating three-phase synchronous and asynchronous motors. The major theoretical principles related with the EPVA are presented and it is shown how stator winding faults can be effectively diagnosed by the use of this noninvasive approach. Experimental results, obtained in
This paper describes the use of the extended Park's vector approach (EPVA) for diagnosing the occurrence of stator winding faults in operating three-phase synchronous and asynchronous motors. The major theoretical principles related with the EPVA are presented and it is shown how stator winding faults can be effectively diagnosed by the use of this noninvasive approach. Experimental results, obtained in
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 faultdiagnosis, 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 faultdiagnosis.
We present a label-free, chemically-selective, quantitative imaging strategy to identify breast cancer and differentiate its subtypes using coherent anti-Stokes Raman scattering (CARS) microscopy. Human normal breast tissue, benign proliferative, as well as in situ and invasive carcinomas, were imaged ex vivo. Simply by visualizing cellular and tissue features appearing on CARS images, cancerous lesions can be readily separated from normal tissue and benign proliferative lesion. To further distinguish cancer subtypes, quantitative disease-related features, describing the geometry and distribution of cancer cell nuclei, were extracted and applied to a computerized classification system. The results show that in situ carcinoma was successfully distinguished from invasive carcinoma, while invasive ductal carcinoma (IDC) and invasive lobular carcinoma were also distinguished from each other. Furthermore, 80% of intermediate-grade IDC and 85% of high-grade IDC were correctly distinguished from each other. The proposed quantitative CARS imaging method has the potential to enable rapid diagnosis of breast cancer.
Yang, Yaliang; Li, Fuhai; Gao, Liang; Wang, Zhiyong; Thrall, Michael J.; Shen, Steven S.; Wong, Kelvin K.; Wong, Stephen T. C.
This paper presents a unified methodology for detecting, isolating and accommodating faults in a class of nonlinear dynamic systems. A faultdiagnosis component is used for fault detection and isolation. On the basis of the fault information obtained by the fault-diagnosis procedure, a fault-tolerant control component is designed to compensate for the effects of faults. In the presence of a
Xiaodong Zhang; Thomas Parisini; Marios M. Polycarpou
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.
The authors consider the development of a knowledge base branch related to rotor electrical faults in squirrel cage machines, to be implemented in an expert system (ES), utilizing instantaneous values as input data. The knowledge base is organized in two levels: in the first level diagnostic indexes for the orientation of the ES inference engine toward the appropriate branch of
F. Filippetti; M. Martelli; G. Franceschini; C. Tassoni
A method for diagnosing component faults of jet engines is presented. It uses non-linear gas path analysis techniques to determine the values of health parameters, with the help of a suitably formulated engine simulation model. The incentive of the method is to achieve the determination of the values of component health indices when a limited number of measured quantities is
This paper introduces a new approach, based on the average motor supply current Park's vector monitoring, for diagnosing voltage source inverter faults in variable speed AC drives. Both simulation and laboratory tests results demonstrate the effectiveness of the proposed on-line diagnostic technique
An envelope order tracking analysis scheme is proposed in the paper for the fault detection of rolling element bearing (REB) under varying-speed running condition. The developed method takes the advantages of order tracking, envelope analysis and spectral kurtosis. The fast kurtogram algorithm is utilized to obtain both optimal center frequency and bandwidth of the band-pass filter based on the maximum spectral kurtosis. The envelope containing vibration features of the incipient REB fault can be extracted adaptively. The envelope is re-sampled by the even-angle sampling scheme, and thus the non-stationary signal in the time domain is represented as a quasi-stationary signal in the angular domain. As a result, the frequency-smear problem can be eliminated in order spectrum and the faultdiagnosis of REB in the varying-speed running condition of the rotating machinery is achieved. Experiments are conducted to verify the validity of the proposed method.
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.
In this paper, an automatic algorithm based an unsupervised neural network for an on-line diagnostics of three-phase induction motor stator fault is presented. This algorithm uses the alfa-beta stator currents as input variables. Then, a fully automatic unsupervised method is applied in which a Hebbian-based unsupervised neural network is used to extract the principal components of the stator current data.
Diagnostic fault simulation can generate enormous amounts of data. The techniques used to manage this data can have signi cant e ect on the outcome of the faultdiagnosis procedure. We rst demonstrate that if information is removed from a fault dictionary, its ability to diagnose unmodeled faults may be severely curtailed even if dictionary quality metrics remain una ected;
The objective of this on-going research is to develop a design methodology to increase the availability for offshore wind farms, by means of an intelligent maintenance system capable of responding to faults by reconfiguring the system or subsystems, without increasing service visits, complexity, or costs. The idea is to make use of the existing functional redundancies within the system and sub-systems to keep the wind turbine operational, even at a reduced capacity if necessary. Re-configuration is intended to be a built-in capability to be used as a repair strategy, based on these existing functionalities provided by the components. The possible solutions can range from using information from adjacent wind turbines, such as wind speed and direction, to setting up different operational modes, for instance re-wiring, re-connecting, changing parameters or control strategy. The methodology described in this paper is based on qualitative physics and consists of a faultdiagnosis system based on a model-based reasoner (MBR), and on a functional redundancy designer (FRD). Both design tools make use of a function-behaviour-state (FBS) model. A design methodology based on the re-configuration concept to achieve self-maintained wind turbines is an interesting and promising approach to reduce stoppage rate, failure events, maintenance visits, and to maintain energy output possibly at reduced rate until the next scheduled maintenance.
Echavarria, E.; Tomiyama, T.; van Bussel, G. J. W.
Vibration analysis is an effective tool for the condition monitoring and faultdiagnosis 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
Vibration analysis is an effective tool for the condition monitoring and faultdiagnosis 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.
In recent years, control system reliability has received much attention with increase of situations where computer-controlled systems such as robot control systems are used. In order to improve reliability, control systems need to have abilities to detect a fault (fault detection) and to maintain the stability and the control performance (fault tolerance). In this paper, we address the vibration suppression
The signals that can be obtained from rotating machines convey information on a machine operating condition. For example, if the machine has faults, it generates a signal that is usually composed of pulse signals. This paper addresses the way in which we can find the faults for periodic pulse signals. Specifically, we have an interest in the case that it is embedded in noise. How well we can detect the fault signal in noise directly determines the quality of faultdiagnosis of rotating machines. We propose a signal processing method to detect fault signals in noisy environments. The proposed method is 'minimum variance cepstrum' because it minimizes the variance of the signal power in its cepstrum representation. To test the performance of this technique, various experiments have been performed for ball bearing elements that have man-made faults. Results show that the proposed technique is quite powerful in the detection of faults in noisy environments. In other words, it is possible to detect faults earlier than with conventional methods (McFadden and Smith 1984 J. Sound Vib. 96 69-82, Ho and Randall 1999 6th Int. Congress on Sound and Vibration pp 2943-50, Lee and White 1998 J. Sound Vib. 217 485-505, Kim et al 1991 Mech. Syst. Signal Process. 5 461-73, Staszewski and Tomlinson 1997 Mech. Syst. Signal Process. 11 331-50).
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....
Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. Complex systems often use fault trees to analyze the faults. Faultdiagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space
We propose the term “extraction fault” for a planar structure that forms at the trailing edge of a discrete block when it is forced or extracted out of the surrounding material. This process results in the merging of two block-bounding faults with opposite senses of displacement. An extraction fault differs fundamentally from other faults in that its two sides have approached each other substantially in the direction perpendicular to the fault. The fault-parallel displacement may be either zero (pure extraction faults) or not (mixed extraction faults). Pure small-scale extraction faults can result from boudinage. A large-scale example may be the S-reflector of the Galicia passive continental margin which is related to rifting and continental breakup. When the strong portion of the lithosphere, i.e. the upper mantle and the lower crust, underwent necking, thermally weak mantle from below and upper crust from above collapsed into the opening gap in the rift centre and an extraction fault formed at the trailing edge of the strong lithosphere. Extraction faults are also potentially important in the exhumation of high-pressure metamorphic rocks in collisional orogens. We propose that the Combin fault on top of the eclogite-facies Zermatt-Saas ophiolites in the Penninic Alps, earlier interpreted either as a normal fault or as a thrust, is in fact an extraction fault.
Froitzheim, Nikolaus; Pleuger, Jan; Nagel, Thorsten J.
Considering accelerometer failure of single electromagnet suspension system of maglev train, a sensor-faultdiagnosis 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 faultdiagnosis system designed can
Stewart platform is widely used for vibration isolation and precise pointing. As it is a statically determinate structure, if any strut has fault, a disaster could be unavoidable. In the present paper, an octo-strut passive vibration isolation platform with redundancy is introduced and applied to whole-spacecraft vibration isolation. This platform is modeled with the Newton Euler method. To avoid such
The present work describes current electrodynamic vibrators and vibration rigs for investigating materials, structural elements, machine parts, and certain biological objects subjected to vibrations and large accelerations. Methods of increasing the thrusting force and amplitude of oscillations in electrodynamic vibrators are discussed along with broadening of the frequency range. The characteristics of commercial vibrators are examined. Ways of preventing vibrations
5 degrees C-water 10-minute immersion test, generally used in Japan, is useful to diagnose vibration diseases. But severe pains during the immersion is troublesome. We studied the availability of 10 degrees C-water 10-minute immersion test to reduce the pain during the test. Subjects were forty-nine chainsaw operators, nineteen patients with vibration disease, and twelve controls. The same subject underwent both 5 degrees C and 10 degrees C immersion tests. The following results were obtained. 1) Skin temperatures in the highest score group after the immersion tests both at 5 degrees C and 10 degrees C was lower than that in the control group. Mean skin temperatures for the last five minutes during the immersion and the recovery activity in both the immersion tests showed a similar trend among subjects groups. Skin temperatures in patients under medical treatment (R'group) did not differ from those in the control group. 2) Hyperemia time by nail press test in the R'group and in the high score group after both immersion tests was longer than that in the control group. But this difference between chainsaw operators and the control group after 5 degrees C immersion test was more marked than that after 10 degrees C immersion test. 3) Vibratory sense as well as pain sense in the R'group and in the high score group after both immersion tests were less sharp than those in the control group. 4) Skin temperatures, nail press test, vibratory sense, and pain sense after 5 degrees C immersion test and those after 10 degrees C immersion test showed statistically significant positive correlation. 5) 10 degrees C immersion test is as effective as 5 degrees C immersion test in finding nervous disorders, but 5 degrees C immersion test is more effective than 10 degrees C immersion test in finding circulatory disorders. However patients with Raynaud's phenomena or moderate circulatory disorders can also be found even by 10 degrees C immersion test. 6) Cold water immersion test revealed disorders not only in skin temperature and by nail press test but revealed also disorders in vibratory sense and pain sense, therefore it is desirable that cold water immersion test should be done in the examination of vibration diseases. PMID:6304386
Sakakibara, H; Miyao, M; Kanada, S; Kobayashi, F; Nakagawa, T; Yamada, S
Fault detection and diagnosis (FDD) is applied to mechanical-pneumatic systems to perform intelligent diagnosis of various faults in the system by utilizing the sensory information commonly found in typical systems, such as pressures and flow rates. In this paper, we present research results on intelligent FDD and characterization of MEMS flow sensor. Vectorized maps are created and calibrated for the purpose of intelligent FDD. In addition, maps of N-manifold can be used for redundancy in diagnosis to improve the accuracy and reliability of the methodology. Such redundant vectorized maps provide for explanation of physical significance of the behavior of the system and the formation or detection of faults. As a result, both physical-based and signal-based intelligent fault detection and diagnosis techniques and methodology can be applied for various types of applications. Experimental results suggest that intuitive choices of parameters and features, based on the understanding of physics of the mechanical-pneumatic system, can be applied with success to intelligent detection and diagnosis of faults. Furthermore, with miniaturization, sensors can be readily made and integrated for intelligent diagnosis. Characterization and modeling of such innovative sensor designs are presented. Using new smart multi-function, telemetric, and integrated sensors as "intelligent nodes" in systems will provide necessary sensory information (e.g., pressure, flow, and temperature) for the next-generation diagnosis. The characterization and study of MEMS sensor include: correlation of flow and deflection of sensory element, analysis and modeling, vibration characteristics, fatigue tests, backflow characterization,... etc. Specifically, the results of fatigue tests provide information and feedback for the design and fabrication of the MEMS sensors; more importantly, long fatigue life is essential for the flow sensors to sustain as a transducer. Results of the findings are presented.
Kao, Imin; Li, Xiaolin; Kumar, Abhinav; Boehm, Christian; Binder, Josef
Faults in automated processes will often cause undesired reactions and shut-down of a controlled plant, and the consequences could be damage to technical parts of the plant, to personnel or the environment. Fault-tolerant control combines diagnosis with control methods to handle faults in an intelligent way. The aim is to prevent that simple faults develop into serious failure and hence
Successful failure analysis requires accurate faultdiagnosis. This paper presents a method for diagnosing bridging faults that improves on previous methods. The new method uses single stuck-at fault signatures, produces accurate and precise diagnoses, and takes into account imperfect fault modeling; it accomplishes this by introducing the concepts of match restriction, match requirement, and match ranking
David B. Lavo; Brian Chess; Tracy Larrabee; F. Joel Ferguson
This study presents the vibration of an adaptive sliding mode fault-tolerant control for MR suspension system considering the partial fault of MR dampers. After formulating a full car dynamic model featuring four MR dampers, the fault model of the MR dampers due to the varying working temperature is derived. An adaptive sliding model fault-tolerant control strategy is then proposed after
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.
A number of substation integrated control and protection systems (ICPS) are being developed around the world, where the protective relaying, control, and monitoring functions of a substation are implemented using microprocessors. In this design, conventional relays and control devices are replaced by clusters of microprocessors, interconnected by multiplexed digital communication channels using fibre optic, twisted wire pairs or coaxial cables.
B. Jeyasurya; S. S. Venkata; S. V. Vadari; J. Postforoosh
Faultdiagnosis in geared transmissions is traditionally based on vibration monitoring but, in a number of cases, sensor implementation and signal transfer from rotary to stationary parts can cause problems. This paper presents an original integrated electro-mechanical model aimed at testing the possibility and the interest of tooth fault detection based on electric measurements on the motor stator. The motor is simulated using Kron's transformation while the mechanical transmission is accounted for by a lumped parameter model. Tooth defects are assimilated to distributions of initial separations between the mating flanks whose positions and shapes are controlled. A unique non-linear parametrically excited differential system is obtained, which provides direct access to both the electrical and mechanical variables. A number of results are presented, which illustrate the possibility of tooth fault detection by stator current measurements with regard to the position and dimensions of the defect.
In the method of detecting a localized sun gear fault, in the operation of an epicyclic gear train having ring, planet and sun gears, and a planet carrier, the steps that include detecting sun gear vibrations transmitted through each planet gear, computing separated averages of such detected vibrations, phase shifting the averages to account for the differences in gear meshing positions, and re-combining the phase shifted averages to produce a modified average value of the sun gear vibration.
With the growing intolerance to failures within systems, the issue of faultdiagnosis has become ever prevalent. Information concerning these possible failures can help to minimise the disruption to the functionality of the system by allowing quick rectification. Traditional approaches to faultdiagnosis within engineering systems have focused on sequential testing procedures and real-time mechanisms. Both methods have been predominantly
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
Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery faultdiagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017
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 faultdiagnosis.