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Sample records for bearing fault detection

  1. Bearing Fault Detection in Induction Motor-Gearbox Drivetrain

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

    2012-05-01

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

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

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

    2016-05-01

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

  3. Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm

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    Samanta B

    2004-01-01

    Full Text Available A study is presented to compare the performance of bearing fault detection using three types of artificial neural networks (ANNs, namely, multilayer perceptron (MLP, radial basis function (RBF network, and probabilistic neural network (PNN. The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to all three ANN classifiers: MLP, RBF, and PNN for two-class (normal or fault recognition. The characteristic parameters like number of nodes in the hidden layer of MLP and the width of RBF, in case of RBF and PNN along with the selection of input features, are optimized using genetic algorithms (GA. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine with and without bearing faults. The results show the relative effectiveness of three classifiers in detection of the bearing condition.

  4. Stochastic Resonance algorithms to enhance damage detection in bearing faults

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    Castiglione Roberto

    2015-01-01

    Full Text Available Stochastic Resonance is a phenomenon, studied and mainly exploited in telecommunication, which permits the amplification and detection of weak signals by the assistance of noise. The first papers on this technique are dated early 80 s and were developed to explain the periodically recurrent ice ages. Other applications mainly concern neuroscience, biology, medicine and obviously signal analysis and processing. Recently, some researchers have applied the technique for detecting faults in mechanical systems and bearings. In this paper, we try to better understand the conditions of applicability and which is the best algorithm to be adopted for these purposes. In fact, to get the methodology profitable and efficient to enhance the signal spikes due to fault in rings and balls/rollers of bearings, some parameters have to be properly selected. This is a problem since in system identification this procedure should be as blind as possible. Two algorithms are analysed: the first exploits classical SR with three parameters mutually dependent, while the other uses Woods-Saxon potential, with three parameters yet but holding a different meaning. The comparison of the performances of the two algorithms and the optimal choice of their parameters are the scopes of this paper. Algorithms are tested on simulated and experimental data showing an evident capacity of increasing the signal to noise ratio.

  5. Repetitive transients extraction algorithm for detecting bearing faults

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

    2017-02-01

    Rolling-element bearing vibrations are random cyclostationary. This paper addresses the problem of noise reduction with simultaneous components extraction in vibration signals for faults diagnosis of bearing. The observed vibration signal is modeled as a summation of two components contaminated by noise, and each component composes of repetitive transients. To extract the two components simultaneously, an approach by solving an optimization problem is proposed in this paper. The problem adopts convex sparsity-based regularization scheme for decomposition, and non-convex regularization is used to further promote the sparsity but preserving the global convexity. A synthetic example is presented to illustrate the performance of the proposed approach for repetitive feature extraction. The performance and effectiveness of the proposed method are further demonstrated by applying to compound faults and single fault diagnosis of a locomotive bearing. The results show the proposed approach can effectively extract the features of outer and inner race defects.

  6. A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

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    Hu Aijun

    2017-01-01

    Full Text Available The fault signals of rolling element bearing are often characterized by the presence of periodic impulses, which are modulated high-frequency harmonic components. The features of early fault in rolling bearing are very weak, which are often masked by background noise. The impulsiveness of the vibration signal has affected the identification of characteristic frequency for the early fault detection of the bearing. In this paper, a novel approach based on morphological operators is presented for impulsive signal extraction of early fault in rolling element bearing. The combination Top-Hat (CTH is proposed to extract the impulsive signal and enhance the impulsiveness of the bearing fault signal, and the envelope analysis is applied to reveal the fault-related signatures. The impulsive extraction performance of the proposed CTH is compared with that of finite impulse response filter (FIR by analyzing the simulated bearing fault signals, and the result indicates that the CTH is more effective in extracting impulsive signals. The method is evaluated using real fault signals from defective bearings with early rolling element fault and early fault located on the outer race. The results show that the proposed method is able to enhance the impulsiveness of early bearing fault signals.

  7. Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition

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    Georgoulas, George; Loutas, Theodore; Stylios, Chrysostomos D.; Kostopoulos, Vassilis

    2013-12-01

    Aiming at more efficient fault diagnosis, this research work presents an integrated anomaly detection approach for seeded bearing faults. Vibration signals from normal bearings and bearings with three different fault locations, as well as different fault sizes and loading conditions are examined. The Empirical Mode Decomposition and the Hilbert Huang transform are employed for the extraction of a compact feature set. Then, a hybrid ensemble detector is trained using data coming only from the normal bearings and it is successfully applied for the detection of any deviation from the normal condition. The results prove the potential use of the proposed scheme as a first stage of an alarm signalling system for the detection of bearing faults irrespective of their loading condition.

  8. Multi fault detection of the roller bearing using the wavelet transformand principal component analysis

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    Jaafar Khalaf Ali, Qusai Talib Abdulwahab, Sajjad Nayyef Abdul kareem

    2016-01-01

    Full Text Available Vibration monitoring and analysis techniques are the key features of successful predictive and proactive maintenance programs. In this work, advanced vibration analysis techniques like Wavelet transform, Principle Component Analysis (PCA and Squared Prediction Error (SPE have been used to detect the faults in bearing. Discrete Wavelet Transforms (DWT decomposes signal to high and low frequencies. PCA is employed to extract important feature and reduce dimension. SPE is used to detect the bearing faults. The experimental data is collected from SpectraQuest's Machine Fault Simulator (MFS-4 apparatus. In this study, four rollers were bearing defects (ball defect, outer race defect, inner race defect and combined defect for 1" and 3/4" bearing. From the results, the suggestion techniques can be used to detect multi-faults in the bearings. The results show that the best wavelet function is Coiflets4 in this method.

  9. Early fault detection in automotive ball bearings using the minimum variance cepstrum

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    Park, Choon-Su; Choi, Young-Chul; Kim, Yang-Hann

    2013-07-01

    Ball bearings in automotive wheels play an important role in a vehicle. They enable an automobile to run and simultaneously support the vehicle. Once faults are generated, even if they are small, they often grow fast even under normal driving condition and cause vibration and noise. Therefore, it is critical to detect faults as early as possible to prevent bearings from generating harsh noise and vibration. How early faults can be detected is associated with how well a detecting method finds the information of early faults from measured signal. Incipient faults are so small that the fault signal is inherently buried by noise. Minimum variance cepstrum (MVC) has been introduced for the observation of periodic impulse signal under noisy environments. We are particularly focusing on the definition of MVC that goes back to the original definition by Bogert et al. in comparison with the recently prevalent definition of cepstral analysis. In this work, the MVC is, therefore, obtained by liftering a logarithmic power spectrum, and the lifter bank is designed by the minimum variance algorithm. Furthermore, it is also shown how efficient the method is for detecting periodic fault signal made by early faults by using automotive ball bearings, with which an automobile is equipped under running conditions. We were able to detect incipient faults in 4 out of 12 normal bearings which passed acceptance test as well as in bearings that were recalled due to noise and vibration. In addition, we compared the results of the proposed method with results obtained using other older well-established early fault detection methods that were chosen from 4 groups of methods which were classified by the domain of observation. The results demonstrated that MVC determined bearing fault periods more clearly than other methods under the given condition.

  10. Detection of Naturally Occurring Gear and Bearing Faults in a Helicopter Drivetrain

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    2014-01-01

    Annual Forum, Montreal, Canada, 2002. 3. Samuel, P. D.; Pines, D. J. A Review of Vibration Based Techniques for Helicopter Transmission Diagnostics...Detection of Naturally Occurring Gear and Bearing Faults in a Helicopter Drivetrain by Kelsen E. LaBerge, Eric C. Ames, and Brian D. Dykas...5066 ARL-TR-6795 January 2014 Detection of Naturally Occurring Gear and Bearing Faults in a Helicopter Drivetrain Kelsen E. LaBerge

  11. Detection and Quantization of Bearing Fault in Direct Drive Wind Turbine via Comparative Analysis

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    Wei Teng

    2016-01-01

    Full Text Available Bearing fault is usually buried by intensive noise because of the low speed and heavy load in direct drive wind turbine (DDWT. Furthermore, varying wind speed and alternating loads make it difficult to quantize bearing fault feature that indicates the degree of deterioration. This paper presents the application of multiscale enveloping spectrogram (MuSEnS and cepstrum to detect and quantize bearing fault in DDWT. MuSEnS can manifest fault modulation information adaptively based on the capacity of complex wavelet transform, which enables the weak bearing fault in DDWT to be detected. Cepstrum can calculate the average interval of periodic components in frequency domain and is suitable for quantizing bearing fault feature under varying operation conditions due to the logarithm weight on the power spectrum. Through comparing a faulty DDWT with a normal one, the bearing fault feature is evidenced and the quantization index is calculated, which show a good application prospect for condition monitoring and fault diagnosis in real DDWT.

  12. DWT based bearing fault detection in induction motor using noise cancellation

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    K.C. Deekshit Kompella

    2016-12-01

    Full Text Available This paper presents an approach to detect the bearing faults experienced by induction machine using motor current signature analysis (MCSA. At the incipient stage of bearing fault, the current signature analysis has shown poor performance due to domination of pre fault components in the stator current. Therefore, in this paper domination of pre fault components is suppressed using noise cancellation by Wiener filter. The spectral analysis is carried out using discrete wavelet transform (DWT. The fault severity is estimated by calculating fault indexing parameter of wavelet coefficients. It is further proposed that, the fault indexing parameter of power spectral density (PSD based wavelet coefficients gives better results. The proposed method is examined using simulation and experiment on 2.2 kW test bed.

  13. Comprehensive bearing condition monitoring algorithm for incipient fault detection using acoustic emission

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    Amit R. Bhende

    2014-09-01

    Full Text Available The bearing reliability plays major role in obtaining the desired performance of any machine. A continuous condition monitoring of machine is required in certain applications where failure of machine leads to loss of production, human safety and precision. Machine faults are often linked to the bearing faults. Condition monitoring of machine involves continuous watch on the performance of bearings and predicting the faults of bearing before it cause any adversity. This paper investigates an experimental study to diagnose the fault while bearing is in operation. An acoustic emission technique is used in the experimentation. An algorithm is developed to process various types of signals generated from different bearing defects. The algorithm uses time domain analysis along with combination low frequency analysis technique such as fast Fourier transform and high frequency envelope detection. Two methods have adopted for envelope detection which are Hilbert transform and order analysis. Experimental study is carried out for deep groove ball bearing cage defect. Results show the potential effectiveness of the proposed algorithm to determine presence of fault, exact location and severity of fault.

  14. Rolling Bearing Fault Detection Based on the Teager Energy Operator and Elman Neural Network

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    Hongmei Liu

    2013-01-01

    Full Text Available This paper presents an approach to bearing fault diagnosis based on the Teager energy operator (TEO and Elman neural network. The TEO can estimate the total mechanical energy required to generate signals, thereby resulting in good time resolution and self-adaptability to transient signals. These attributes reflect the advantage of detecting signal impact characteristics. To detect the impact characteristics of the vibration signals of bearing faults, we used the TEO to extract the cyclical impact caused by bearing failure and applied the wavelet packet to reduce the noise of the Teager energy signal. This approach also enabled the extraction of bearing fault feature frequencies, which were identified using the fast Fourier transform of Teager energy. The feature frequencies of the inner and outer faults, as well as the ratio of resonance frequency band energy to total energy in the Teager spectrum, were extracted as feature vectors. In order to avoid a frequency leak error, the weighted Teager spectrum around the fault frequency was extracted as feature vector. These vectors were then used to train the Elman neural network and improve the robustness of the diagnostic algorithm. Experimental results indicate that the proposed approach effectively detects bearing faults under variable conditions.

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

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

    2016-02-01

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

  16. Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals.

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    Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

    2015-10-09

    The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments.

  17. A study on real-time fault monitoring detection method of bearing using the infrared thermography

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    Kim, Won Tae [School of Mechanical and Automotive Engineering, Kongju National University, Kongju (Korea, Republic of); Kim, Ho Jong; Hong, Dong Pyo [School of Mechanical System Engineering, Chonbuk Nationa University, Jeonju (Korea, Republic of)

    2013-08-15

    Since real-time monitoring system like a fault early detection has been very important, infrared thermography technique as a new diagnosis method was proposed. This study is focused on the damage detection and temperature characteristic analysis of ball bearing using the non-destructive infrared thermography method. In this paper, for the reliability assessment, infrared experimental data were compared with the frequency data of the existing. As results, the temperature characteristics of ball bearing were analyzed under various loading conditions. Finally it was confirmed that the infrared technique was useful for real-time detection of the bearing damages.

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

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    Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

    2016-05-01

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

  19. Bearing fault detection using motor current signal analysis based on wavelet packet decomposition and Hilbert envelope

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    Imaouchen Yacine

    2015-01-01

    Full Text Available To detect rolling element bearing defects, many researches have been focused on Motor Current Signal Analysis (MCSA using spectral analysis and wavelet transform. This paper presents a new approach for rolling element bearings diagnosis without slip estimation, based on the wavelet packet decomposition (WPD and the Hilbert transform. Specifically, the Hilbert transform first extracts the envelope of the motor current signal, which contains bearings fault-related frequency information. Subsequently, the envelope signal is adaptively decomposed into a number of frequency bands by the WPD algorithm. Two criteria based on the energy and correlation analyses have been investigated to automate the frequency band selection. Experimental studies have confirmed that the proposed approach is effective in diagnosing rolling element bearing faults for improved induction motor condition monitoring and damage assessment.

  20. Rolling bearing fault detection using an adaptive lifting multiwavelet packet with a {1\\frac{1}{2}} dimension spectrum

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    Jiang, Hongkai; Xia, Yong; Wang, Xiaodong

    2013-12-01

    Defect faults on the surface of rolling bearing elements are the most frequent cause of malfunctions and breakages of electrical machines. Due to increasing demands for quality and reliability, extracting fault features in vibration signals is an important topic for fault detection in rolling bearings. In this paper, a novel adaptive lifting multiwavelet packet with {1\\frac{1}{2}} dimension spectrum to detect defects in rolling bearing elements is developed. The adaptive lifting multiwavelet packet is constructed to match vibration signal properties based on the minimum singular value decomposition (SVD) entropy using a genetic algorithm. A {1\\frac{1}{2}} dimension spectrum is further employed to extract rolling bearing fault characteristic frequencies from background noise. The proposed method is applied to analyze the vibration signal collected from electric locomotive rolling bearings with outer raceway and inner raceway defects. The experimental investigation shows that the method is accurate and robust in rolling bearing fault detection.

  1. Fault Detection Enhancement in Rolling Element Bearings via Peak-Based Multiscale Decomposition and Envelope Demodulation

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    Hua-Qing Wang

    2014-01-01

    Full Text Available Vibration signals of rolling element bearings faults are usually immersed in background noise, which makes it difficult to detect the faults. Wavelet-based methods being used commonly can reduce some types of noise, but there is still plenty of room for improvement due to the insufficient sparseness of vibration signals in wavelet domain. In this work, in order to eliminate noise and enhance the weak fault detection, a new kind of peak-based approach combined with multiscale decomposition and envelope demodulation is developed. First, to preserve effective middle-low frequency signals while making high frequency noise more significant, a peak-based piecewise recombination is utilized to convert middle frequency components into low frequency ones. The newly generated signal becomes so smoother that it will have a sparser representation in wavelet domain. Then a noise threshold is applied after wavelet multiscale decomposition, followed by inverse wavelet transform and backward peak-based piecewise transform. Finally, the amplitude of fault characteristic frequency is enhanced by means of envelope demodulation. The effectiveness of the proposed method is validated by rolling bearings faults experiments. Compared with traditional wavelet-based analysis, experimental results show that fault features can be enhanced significantly and detected easily by the proposed method.

  2. Fault Detection of a Wheelset Bearing Based on Appropriately Sparse Impulse Extraction

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    Jianming Ding

    2017-01-01

    Full Text Available Convolution sparse representation (CSR is a novel compressive sensing technique proposed in 2016 and provides an excellent framework for extracting the impulses induced by bearing faults and the unevenness of wheel tread. However, its sparsity performance on extracting impulses is sensitive to the improper penalty parameter. So, a novel fault detection method, appropriately sparse impulse extraction, is proposed based on the combination of CSR, estimating the number of atom types (ENA, and crest factor. The type of atoms embedded in vibration signals is estimated by ENA. Aiming at the different types of atoms, the impulses with different sparse characteristic are spanned by CSR with different penalty parameters. The appropriately sparse impulses are selected for fault detection based on the maximal crest factor. The simulation validation, experiment verification, and practical application are conducted to validate the effectiveness of the proposed appropriately sparse impulses extraction. These results show that the proposed appropriately sparse impulse extraction not only can obtain fault-characteristic frequency and its harmonics for fault judgment but also describes the dynamic behaviour between elementary defects and their matching surfaces. In addition, the proposed appropriately sparse impulse extraction can isolate the impulses with different types of atoms and is very suitable for detecting the wheelset bearing faults.

  3. Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

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    Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

    2016-07-01

    Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes.

  4. Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

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    Dae-Ho Kwak

    2013-12-01

    Full Text Available This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED, and the Teager-Kaiser Energy Operator (TKEO. MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs, through empirical mode decomposition (EMD. The weight vectors of IMFs become design variables for a genetic algorithm (GA. The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system.

  5. A Two-Stage Compression Method for the Fault Detection of Roller Bearings

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    Huaqing Wang

    2016-01-01

    Full Text Available Data measurement of roller bearings condition monitoring is carried out based on the Shannon sampling theorem, resulting in massive amounts of redundant information, which will lead to a big-data problem increasing the difficulty of roller bearing fault diagnosis. To overcome the aforementioned shortcoming, a two-stage compressed fault detection strategy is proposed in this study. First, a sliding window is utilized to divide the original signals into several segments and a selected symptom parameter is employed to represent each segment, through which a symptom parameter wave can be obtained and the raw vibration signals are compressed to a certain level with the faulty information remaining. Second, a fault detection scheme based on the compressed sensing is applied to extract the fault features, which can compress the symptom parameter wave thoroughly with a random matrix called the measurement matrix. The experimental results validate the effectiveness of the proposed method and the comparison of the three selected symptom parameters is also presented in this paper.

  6. Automatic characteristic frequency association and all-sideband demodulation for the detection of a bearing fault

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    Firla, Marcin; Li, Zhong-Yang; Martin, Nadine; Pachaud, Christian; Barszcz, Tomasz

    2016-12-01

    This paper proposes advanced signal-processing techniques to improve condition monitoring of operating machines. The proposed methods use the results of a blind spectrum interpretation that includes harmonic and sideband series detection. The first contribution of this study is an algorithm for automatic association of harmonic and sideband series to characteristic fault frequencies according to a kinematic configuration. The approach proposed has the advantage of taking into account a possible slip of the rolling-element bearings. In the second part, we propose a full-band demodulation process from all sidebands that are relevant to the spectral estimation. To do so, a multi-rate filtering process in an iterative schema provides satisfying precision and stability over the targeted demodulation band, even for unsymmetrical and extremely narrow bands. After synchronous averaging, the filtered signal is demodulated for calculation of the amplitude and frequency modulation functions, and then any features that indicate faults. Finally, the proposed algorithms are validated on vibration signals measured on a test rig that was designed as part of the European Innovation Project 'KAStrion'. This rig simulates a wind turbine drive train at a smaller scale. The data show the robustness of the method for localizing and extracting a fault on the main bearing. The evolution of the proposed features is a good indicator of the fault severity.

  7. A Frequency-Weighted Energy Operator and complementary ensemble empirical mode decomposition for bearing fault detection

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    Imaouchen, Yacine; Kedadouche, Mourad; Alkama, Rezak; Thomas, Marc

    2017-01-01

    Signal processing techniques for non-stationary and noisy signals have recently attracted considerable attentions. Among them, the empirical mode decomposition (EMD) which is an adaptive and efficient method for decomposing signals from high to low frequencies into intrinsic mode functions (IMFs). Ensemble EMD (EEMD) is proposed to overcome the mode mixing problem of the EMD. In the present paper, the Complementary EEMD (CEEMD) is used for bearing fault detection. As a noise-improved method, the CEEMD not only overcomes the mode mixing, but also eliminates the residual of added white noise persisting into the IMFs and enhance the calculation efficiency of the EEMD method. Afterward, a selection method is developed to choose relevant IMFs containing information about defects. Subsequently, a signal is reconstructed from the sum of relevant IMFs and a Frequency-Weighted Energy Operator is tailored to extract both the amplitude and frequency modulations from the selected IMFs. This operator outperforms the conventional energy operator and the enveloping methods, especially in the presence of strong noise and multiple vibration interferences. Furthermore, simulation and experimental results showed that the proposed method improves performances for detecting the bearing faults. The method has also high computational efficiency and is able to detect the fault at an early stage of degradation.

  8. Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings

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    Miao, Yonghao; Zhao, Ming; Lin, Jing; Xu, Xiaoqiang

    2016-10-01

    De-noising and enhancement of the weak fault signature from the noisy signal are crucial for fault diagnosis, as features are often very weak and masked by the background noise. Deconvolution methods have a significant advantage in counteracting the influence of the transmission path and enhancing the fault impulses. However, the performance of traditional deconvolution methods is greatly affected by some limitations, which restrict the application range. Therefore, this paper proposes a new deconvolution method, named sparse maximum harmonics-noise-ratio deconvolution (SMHD), that employs a novel index, the harmonics-to-noise ratio (HNR), to be the objective function for iteratively choosing the optimum filter coefficients to maximize HNR. SMHD is designed to enhance latent periodic impulse faults from heavy noise signals by calculating the HNR to estimate the period. A sparse factor is utilized to further suppress the noise and improve the signal-to-noise ratio of the filtered signal in every iteration step. In addition, the updating process of the sparse threshold value and the period guarantees the robustness of SMHD. On this basis, the new method not only overcomes the limitations associated with traditional deconvolution methods, minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD), but visual inspection is also better, even if the fault period is not provided in advance. Moreover, the efficiency of the proposed method is verified by simulations and bearing data from different test rigs. The results show that the proposed method is effective in the detection of various bearing faults compared with the original MED and MCKD.

  9. Weighted low-rank sparse model via nuclear norm minimization for bearing fault detection

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    Du, Zhaohui; Chen, Xuefeng; Zhang, Han; Yang, Boyuan; Zhai, Zhi; Yan, Ruqiang

    2017-07-01

    It is a fundamental task in the machine fault diagnosis community to detect impulsive signatures generated by the localized faults of bearings. The main goal of this paper is to exploit the low-rank physical structure of periodic impulsive features and further establish a weighted low-rank sparse model for bearing fault detection. The proposed model mainly consists of three basic components: an adaptive partition window, a nuclear norm regularization and a weighted sequence. Firstly, due to the periodic repetition mechanism of impulsive feature, an adaptive partition window could be designed to transform the impulsive feature into a data matrix. The highlight of partition window is to accumulate all local feature information and align them. Then, all columns of the data matrix share similar waveforms and a core physical phenomenon arises, i.e., these singular values of the data matrix demonstrates a sparse distribution pattern. Therefore, a nuclear norm regularization is enforced to capture that sparse prior. However, the nuclear norm regularization treats all singular values equally and thus ignores one basic fact that larger singular values have more information volume of impulsive features and should be preserved as much as possible. Therefore, a weighted sequence with adaptively tuning weights inversely proportional to singular amplitude is adopted to guarantee the distribution consistence of large singular values. On the other hand, the proposed model is difficult to solve due to its non-convexity and thus a new algorithm is developed to search one satisfying stationary solution through alternatively implementing one proximal operator operation and least-square fitting. Moreover, the sensitivity analysis and selection principles of algorithmic parameters are comprehensively investigated through a set of numerical experiments, which shows that the proposed method is robust and only has a few adjustable parameters. Lastly, the proposed model is applied to the

  10. Detection of Bearing Faults of Induction Motor Using Park’s Vector Approach

    OpenAIRE

    Ratna Dahiya; Neelam Mehala

    2010-01-01

    The reliability of an induction motor is of paramount importance in industrial, commercial, aerospace and military applications. Bearing play an important role in the reliability and performance of all motor systems. Due to close relationship between motor system development and bearing assembly performance, it is difficult to imagine the progress of modern rotating machinery without consideration of the wide application of bearing. Most faults arising in motors are often linked to bearing fa...

  11. One-class classification based on the convex hull for bearing fault detection

    Science.gov (United States)

    Zeng, Ming; Yang, Yu; Luo, Songrong; Cheng, Junsheng

    2016-12-01

    Originating from a nearest point problem, a novel method called one-class classification based on the convex hull (OCCCH) is proposed for one-class classification problems. The basic goal of OCCCH is to find the nearest point to the origin from the reduced convex hull of training samples. A generalized Gilbert algorithm is proposed to solve the nearest point problem. It is a geometric algorithm with high computational efficiency. OCCCH has two different forms, i.e., OCCCH-1 and OCCCH-2. The relationships among OCCCH-1, OCCCH-2 and one-class support vector machine (OCSVM) are investigated theoretically. The classification accuracy and the computational efficiency of the three methods are compared through the experiments conducted on several benchmark datasets. Experimental results show that OCCCH (including OCCCH-1 and OCCCH-2) using the generalized Gilbert algorithm performs more efficiently than OCSVM using the well-known sequential minimal optimization (SMO) algorithm; at the same time, OCCCH-2 can always obtain comparable classification accuracies to OCSVM. Finally, these methods are applied to the monitoring model constructions for bearing fault detection. Compared with OCCCH-2 and OCSVM, OCCCH-1 can significantly decrease the false alarm ratio while detecting the bearing fault successfully.

  12. Detection of Bearing Faults of Induction Motor Using Park’s Vector Approach

    Directory of Open Access Journals (Sweden)

    Ratna Dahiya

    2010-08-01

    Full Text Available The reliability of an induction motor is of paramount importance in industrial, commercial, aerospace and military applications. Bearing play an important role in the reliability and performance of all motor systems. Due to close relationship between motor system development and bearing assembly performance, it is difficult to imagine the progress of modern rotating machinery without consideration of the wide application of bearing. Most faults arising in motors are often linked to bearing faults. This paper presents an experimental study to diagnose the bearing fault with help of Park’s vector approach. The experiment is conducted on 0.5 hp three phaseinduction motor. The bearing faults are replicated in the laboratory by drilling the outer and inner race of ball bearing with help of electric discharge machining. The LabVIEW software is used in the experiment to acquire the signal. The acquired signal is analyzed with Park vector approach. The current Park’s vector presentation is generated by programming in LabVIEW. The practical results show that Park’s Vector approach is an effective technique to diagnose the bearing fault at early stage.

  13. Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed

    Science.gov (United States)

    Zhao, Dezun; Li, Jianyong; Cheng, Weidong; Wen, Weigang

    2016-09-01

    Multi-fault detection of the rolling element bearing under time-varying rotational speed presents a challenging issue due to its complexity, disproportion and interaction. Computed order analysis (COA) is one of the most effective approaches to remove the influences of speed fluctuation, and detect all the features of multi-fault. However, many interference components in the envelope order spectrum may lead to false diagnosis results, in addition, the deficiencies of computational accuracy and efficiency also cannot be neglected. To address these issues, a novel method for compound faults detection of rolling element bearing based on the generalized demodulation (GD) algorithm is proposed in this paper. The main idea of the proposed method is to exploit the unique property of the generalized demodulation algorithm in transforming an interested instantaneous frequency trajectory of compound faults bearing signal into a line paralleling to the time axis, and then the FFT algorithm can be directly applied to the transformed signal. This novel method does not need angular resampling algorithm which is the key step of the computed order analysis, and is hence free from the deficiencies of computational error and efficiency. On the other hand, it only acts on the instantaneous fault characteristic frequency trends in envelope signal of multi-fault bearing which include rich fault information, and is hence free from irrelevant items interferences. Both simulated and experimental faulty bearing signal analysis validate that the proposed method is effective and reliable on the compound faults detection of rolling element bearing under variable rotational speed conditions. The comprehensive comparison with the computed order analysis further shows that the proposed method produces higher accurate results in less computation time.

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

    Directory of Open Access Journals (Sweden)

    Ming Zhao

    2013-08-01

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

  15. Statistic-based Spectral Indicator for Bearing Fault Detection in Permanent-Magnet Synchronous Machines using the Stator Current

    OpenAIRE

    Picot, Antoine; Obeid, Ziad; Régnier, Jérémi; Poignant, Sylvain; Darnis, Olivier; Maussion, Pascal

    2014-01-01

    International audience; In this paper, an original method for bearing fault detection in high speed synchronous machines is presented. This method is based on the statistical process of Welch's periodogram of the stator currents in order to obtain stable and normalized fault indicators. The principle of the method is to statistically compare the current spectrum to a healthy reference so as to quantify the changes over the time. A statistic-based indicator is then constructed by monitoring sp...

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

    Science.gov (United States)

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

    2016-05-01

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

  17. Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

    Science.gov (United States)

    Zheng, Jinde; Pan, Haiyang; Cheng, Junsheng

    2017-02-01

    To timely detect the incipient failure of rolling bearing and find out the accurate fault location, a novel rolling bearing fault diagnosis method is proposed based on the composite multiscale fuzzy entropy (CMFE) and ensemble support vector machines (ESVMs). Fuzzy entropy (FuzzyEn), as an improvement of sample entropy (SampEn), is a new nonlinear method for measuring the complexity of time series. Since FuzzyEn (or SampEn) in single scale can not reflect the complexity effectively, multiscale fuzzy entropy (MFE) is developed by defining the FuzzyEns of coarse-grained time series, which represents the system dynamics in different scales. However, the MFE values will be affected by the data length, especially when the data are not long enough. By combining information of multiple coarse-grained time series in the same scale, the CMFE algorithm is proposed in this paper to enhance MFE, as well as FuzzyEn. Compared with MFE, with the increasing of scale factor, CMFE obtains much more stable and consistent values for a short-term time series. In this paper CMFE is employed to measure the complexity of vibration signals of rolling bearings and is applied to extract the nonlinear features hidden in the vibration signals. Also the physically meanings of CMFE being suitable for rolling bearing fault diagnosis are explored. Based on these, to fulfill an automatic fault diagnosis, the ensemble SVMs based multi-classifier is constructed for the intelligent classification of fault features. Finally, the proposed fault diagnosis method of rolling bearing is applied to experimental data analysis and the results indicate that the proposed method could effectively distinguish different fault categories and severities of rolling bearings.

  18. Application of cyclic coherence function to bearing fault detection in a wind turbine generator under electromagnetic vibration

    Science.gov (United States)

    Teng, Wei; Ding, Xian; Zhang, Yangyang; Liu, Yibing; Ma, Zhiyong; Kusiak, Andrew

    2017-03-01

    In a wind turbine generator, there is an intrinsic electromagnetic vibration originated from an alternating magnetic field acting on a low stiffness stator, which modulates vibration signals of the generator and impedes fault feature extraction of bearings. When defects arise in a bearing, the statistics of the vibration signal are periodic and this phenomenon is described as cyclostationarity. Correspondingly, cyclostationary analysis enables finding the degree of cyclostationarity representing potential fault modulation information. In this paper, the electromagnetic vibration acting as a disturbance source for fault feature extraction is deduced. Additionally, the spectral correlation density and cyclic coherence function used for vibration analysis are estimated. A real 2 MW wind turbine generator with a faulty bearing was tested and the vibration signals were analyzed separately using conventional demodulation analysis, cyclic coherence function, complex wavelet transform and spectral kurtosis. The analysis results have demonstrated that the cyclic coherence function can detect the fault feature of inner race successfully, while the feature is concealed by intensive electromagnetic vibration in the other three methods. The disassembled bearing of the wind turbine generator illustrates the effectiveness of the analysis result, and precautionary measures for protecting bearings in generators are suggested.

  19. Multi-fault detection of rolling element bearings under harsh working condition using IMF-based adaptive envelope order analysis.

    Science.gov (United States)

    Zhao, Ming; Lin, Jing; Xu, Xiaoqiang; Li, Xuejun

    2014-10-28

    When operating under harsh condition (e.g., time-varying speed and load, large shocks), the vibration signals of rolling element bearings are always manifested as low signal noise ratio, non-stationary statistical parameters, which cause difficulties for current diagnostic methods. As such, an IMF-based adaptive envelope order analysis (IMF-AEOA) is proposed for bearing fault detection under such conditions. This approach is established through combining the ensemble empirical mode decomposition (EEMD), envelope order tracking and fault sensitive analysis. In this scheme, EEMD provides an effective way to adaptively decompose the raw vibration signal into IMFs with different frequency bands. The envelope order tracking is further employed to transform the envelope of each IMF to angular domain to eliminate the spectral smearing induced by speed variation, which makes the bearing characteristic frequencies more clear and discernible in the envelope order spectrum. Finally, a fault sensitive matrix is established to select the optimal IMF containing the richest diagnostic information for final decision making. The effectiveness of IMF-AEOA is validated by simulated signal and experimental data from locomotive bearings. The result shows that IMF-AEOA could accurately identify both single and multiple faults of bearing even under time-varying rotating speed and large extraneous shocks.

  20. Fault Tolerant Homopolar Magnetic Bearings

    Science.gov (United States)

    Li, Ming-Hsiu; Palazzolo, Alan; Kenny, Andrew; Provenza, Andrew; Beach, Raymond; Kascak, Albert

    2003-01-01

    Magnetic suspensions (MS) satisfy the long life and low loss conditions demanded by satellite and ISS based flywheels used for Energy Storage and Attitude Control (ACESE) service. This paper summarizes the development of a novel MS that improves reliability via fault tolerant operation. Specifically, flux coupling between poles of a homopolar magnetic bearing is shown to deliver desired forces even after termination of coil currents to a subset of failed poles . Linear, coordinate decoupled force-voltage relations are also maintained before and after failure by bias linearization. Current distribution matrices (CDM) which adjust the currents and fluxes following a pole set failure are determined for many faulted pole combinations. The CDM s and the system responses are obtained utilizing 1D magnetic circuit models with fringe and leakage factors derived from detailed, 3D, finite element field models. Reliability results are presented vs. detection/correction delay time and individual power amplifier reliability for 4, 6, and 7 pole configurations. Reliability is shown for two success criteria, i.e. (a) no catcher bearing contact following pole failures and (b) re-levitation off of the catcher bearings following pole failures. An advantage of the method presented over other redundant operation approaches is a significantly reduced requirement for backup hardware such as additional actuators or power amplifiers.

  1. A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals

    Science.gov (United States)

    Bozchalooi, I. Soltani; Liang, Ming

    2008-05-01

    The vibration signal measured from a bearing contains vital information for the prognostic and health assessment purposes. However, when bearings are installed as part of a complex mechanical system, the measured signal is often heavily clouded by various noises due to the compounded effect of interferences of other machine elements and background noises present in the measuring device. As such, reliable condition monitoring would not be possible without proper de-noising. This is particularly true for incipient bearing faults with very weak signature signals. A new de-noising scheme is proposed in this paper to enhance the vibration signals acquired from faulty bearings. This de-noising scheme features a spectral subtraction to trim down the in-band noise prior to wavelet filtering. The Gabor wavelet is used in the wavelet transform and its parameters, i.e., scale and shape factor are selected in separate steps. The proper scale is found based on a novel resonance estimation algorithm. This algorithm makes use of the information derived from the variable shaft rotational speed though such variation is highly undesirable in fault detection since it complicates the process substantially. The shape factor value is then selected by minimizing a smoothness index. This index is defined as the ratio of the geometric mean to the arithmetic mean of the wavelet coefficient moduli. De-noising results are presented for simulated signals and experimental data acquired from both normal and faulty bearings with defective outer race, inner race, and rolling element.

  2. Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System.

    Science.gov (United States)

    Ouyang, Kesai; Lu, Siliang; Zhang, Shangbin; Zhang, Haibin; He, Qingbo; Kong, Fanrang

    2015-08-27

    The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy.

  3. Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System

    Directory of Open Access Journals (Sweden)

    Kesai Ouyang

    2015-08-01

    Full Text Available The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS. Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy.

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

    Science.gov (United States)

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

    2015-03-01

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

  5. Artificial immunity-based induction motor bearing fault diagnosis

    OpenAIRE

    Hakan ÇALIŞ; ÇAKIR, Abdülkadir; Emre DANDIL

    2013-01-01

    In this study, the artificial immunity of the negative selection algorithm is used for bearing fault detection. It is implemented in MATLAB-based graphical user interface software. The developed software uses amplitudes of the vibration signal in the time and frequency domains. Outer, inner, and ball defects in the bearings of the induction motor are detected by anomaly monitoring. The time instants of the fault occurrence and fault level are determined according to the number of a...

  6. A comparison of cepstral editing methods as signal pre-processing techniques for vibration-based bearing fault detection

    Science.gov (United States)

    Peeters, Cédric; Guillaume, Patrick; Helsen, Jan

    2017-07-01

    The detection and diagnosis of incipient rolling element bearing faults is not an undemanding task and signal analysis of vibration measurements therefore often incorporates the use of various complex processing techniques. One of the key steps in the processing procedure is the proper separation of the bearing signal from other influencing sources like shafts or gears. The latter sources produce deterministic signal components showing up as discrete frequencies in the amplitude spectrum, while bearing signals are assumed to be (quasi-) cyclostationary resulting in a smearing of the bearing frequencies in the spectrum. This property gave rise to the idea of using the cepstrum for the purpose of separating the deterministic signal content from the second-order cyclostationary bearing signal. The cepstrum essentially groups the deterministic multi-harmonic signal content in a cepstral peak at the corresponding quefrency, making it more suitable for easy removal of the discrete frequency peaks. Even though initially there was a tendency to only remove or 'lifter' the selected cepstral peaks, nowadays the full real cepstrum is set to zero instead of only certain quefrency bands. This technique, called cepstrum pre-whitening, is easy to implement, can be performed quickly without the need for additional input parameters or fine-tuning and would be well-suited for practical applications. However, these advantages do come at the cost of some control and insight over the editing procedure of the signal. In order to assess the performance of this cepstrum pre-whitening technique, it is compared to an automated cepstrum editing procedure. It automatically selects certain peaks in the real cepstrum and only sets them to zero instead of the full real cepstrum. Both methods perform quite well in separating deterministic signal content from more random content, but there are some differences to observe when using them for diagnosis purposes. An analysis of the methods is made

  7. Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain

    Directory of Open Access Journals (Sweden)

    Juan Jose Saucedo-Dorantes

    2016-01-01

    Full Text Available Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.

  8. Characterization of Fault Size in Bearings

    Science.gov (United States)

    2014-12-23

    0.3 and 1.2 mm into the outer-race of the bearing, which simulates realistic faults that often can be found in damaged bearings. A 3D dynamic model ...3 4. MODEL DESCRIPTION A 3D dynamic ball bearing model was developed to study the effect of faults on the bearing dynamic behavior. The aim of the...understanding of the effects of fault size on the bearing dynamics. The research methodology combines dynamic modeling of the faulty bearing with

  9. Cyclostationary Analysis for Gearbox and Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Zhipeng Feng

    2015-01-01

    Full Text Available Gearbox and rolling element bearing vibration signals feature modulation, thus being cyclostationary. Therefore, the cyclic correlation and cyclic spectrum are suited to analyze their modulation characteristics and thereby extract gearbox and bearing fault symptoms. In order to thoroughly understand the cyclostationarity of gearbox and bearing vibrations, the explicit expressions of cyclic correlation and cyclic spectrum for amplitude modulation and frequency modulation (AM-FM signals are derived, and their properties are summarized. The theoretical derivations are illustrated and validated by gearbox and bearing experimental signal analyses. The modulation characteristics caused by gearbox and bearing faults are extracted. In faulty gearbox and bearing cases, more peaks appear in cyclic correlation slice of 0 lag and cyclic spectrum, than in healthy cases. The gear and bearing faults are detected by checking the presence or monitoring the magnitude change of peaks in cyclic correlation and cyclic spectrum and are located according to the peak cyclic frequency locations or sideband frequency spacing.

  10. EEMD and Duffing Vibrator Motor Bearing Early Fault Detection%EEMD与Duffing振子的电机轴承早期故障检测

    Institute of Scientific and Technical Information of China (English)

    刘复飞

    2015-01-01

    Early motor bearing fault vibration signals are easy to be overwhelmed with strong noise,the problem of difficult to detect,puts forward the general empirical mode decomposition ( EEMD ) and the combination of Duffing oscillator detection method.First to pass through the EEMD bearing fault vibration signal is decomposed into the IMF component,according to the bearing fault characteristic frequency deter-mine Duffing subsystem built-in policy power frequency,Duffing oscillator detection model is established.For the IMF component plus ce power input detection system,system phase track transition to large scale cy-cle state as the criterion,to detect the motor bearing fault and its type.The bearing test data,to validate the feasibility and validity of the method.%针对电机轴承早期故障的振动信号容易被强噪声所淹没,不易检测的难题,提出了总体经验模态分解( EEMD)与Duffing振子相结合的检测方法.先将轴承故障振动信号经EEMD分解为IMF分量,再根据轴承故障特征频率确定Duffing振子系统中内置策动力的频率,建立Duffing振子检测模型;以IMF分量为外加策动力输入检测系统,以系统相轨迹跃迁至大尺度周期状态为判据,检测出电机轴承故障及其类型.通过对轴承实验数据的检测,验证了该方法的可行性和有效性.

  11. Novelty Detection Methods and Novel Fault Class Detection

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jiafan; HUANG Zhichu; WANG Xiaoming

    2006-01-01

    The ability to detect a new fault class can be a useful feature for an intelligent fault classification and diagnosis system. We adopt two novelty detection methods, the support vector data description (SVDD) and the Parzen density estimation, to represent known fault class samples, and to detect new fault class samples. The experiments on real multi-class bearing fault data show that the SVDD can give both high novelty detection rate and target recognition rate, respectively for the prescribed 'unknown' fault samples and the known fault samples by choosing the appropriate SVDD algorithm parameters; but the Parzen density estimation only give a better novelty detection rate in our experiments.

  12. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Jinde Zheng

    2014-01-01

    Full Text Available A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE, Laplacian score (LS, and support vector machines (SVMs is proposed in this paper. Permutation entropy (PE was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

  13. Fault Tolerant Magnetic Bearing for Turbomachinery

    Science.gov (United States)

    Choi, Benjamin; Provenza, Andrew

    2001-01-01

    NASA Glenn Research Center (GRC) has developed a Fault-Tolerant Magnetic Bearing Suspension rig to enhance the bearing system safety. It successfully demonstrated that using only two active poles out of eight redundant poles from each radial bearing (that is, simply 12 out of 16 poles dead) levitated the rotor and spun it without losing stability and desired position up to the maximum allowable speed of 20,000 rpm. In this paper, it is demonstrated that as far as the summation of force vectors of the attracting poles and rotor weight is zero, a fault-tolerant magnetic bearing system maintained the rotor at the desired position without losing stability even at the maximum rotor speed. A proportional-integral-derivative (PID) controller generated autonomous corrective actions with no operator's input for the fault situations without losing load capacity in terms of rotor position. This paper also deals with a centralized modal controller to better control the dynamic behavior over system modes.

  14. Fault diagnosis of active magnetic bearings based on Gaussian GLRT detector

    DEFF Research Database (Denmark)

    Nagel, Leon; Galeazzi, Roberto; Voigt, Andreas Jauernik

    2016-01-01

    Active magnetic bearings are progressively replacing conventional bearings in many industrial applications, particularly in the energy sector. Magnetic bearings have many advantages such as contactless support and clean operation; however their use also poses some challenges connected...... to their inherent open loop instability. Occurrence of faults in one or more components of an active magnetic bearing may lead to loss of control of the rotor. Timely detection and isolation of faults in an active magnetic bearing could prevent hazardous system behaviour by enabling proper reconfiguration...... of the control system. A structural model of the bearing-rotor system is presented and used to perform a detectability and isolability analysis of faults in the magnetic actuator. Structural detectability and group-wise isolability is concluded for single and multiple faults in the actuators. A Gaussian...

  15. Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis

    Science.gov (United States)

    Li, Yifan; Liang, Xihui; Zuo, Ming J.

    2017-02-01

    This paper presents a novel signal processing scheme, diagonal slice spectrum assisted optimal scale morphological filter (DSS-OSMF), for rolling element fault diagnosis. In this scheme, the concept of quadratic frequency coupling (QFC) is firstly defined and the ability of diagonal slice spectrum (DSS) in detection QFC is derived. The DSS-OSMF possesses the merits of depressing noise and detecting QFC. It can remove fault independent frequency components and give a clear representation of fault symptoms. A simulated vibration signal and experimental vibration signals collected from a bearing test rig are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method has a superior performance in extracting fault features of defective rolling element bearing. In addition, comparisons are performed between a multi-scale morphological filter (MMF) and a DSS-OSMF. DSS-OSMF outperforms MMF in detection of an outer race fault and a rolling element fault of a rolling element bearing.

  16. Fault detection and isolation in systems with parametric faults

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1999-01-01

    The problem of fault detection and isolation of parametric faults is considered in this paper. A fault detection problem based on parametric faults are associated with internal parameter variations in the dynamical system. A fault detection and isolation method for parametric faults is formulated...

  17. Quantitative Diagnosis of Fault Severity Trend of Rolling Element Bearings

    Institute of Scientific and Technical Information of China (English)

    CUI Lingli; MA Chunqing; ZHANG Feibin; WANG Huaqing

    2015-01-01

    The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condition and fault type but also the severity of the fault. This means fault severity quantitative analysis is one of most active and valid ways to realize proper maintenance decision. Aiming at the deficiency of the research in bearing single point pitting fault quantitative diagnosis, a new back-propagation neural network method based on wavelet packet decomposition coefficient entropy is proposed. The three levels of wavelet packet coefficient entropy(WPCE) is introduced as a characteristic input vector to the BPNN. Compared with the wavelet packet decomposition energy ratio input vector, WPCE shows more sensitive in distinguishing from the different fault severity degree of the measured signal. The engineering application results show that the quantitative trend fault diagnosis is realized in the different fault degree of the single point bearing pitting fault. The breakthrough attempt from quantitative to qualitative on the pattern recognition of rolling element bearings fault diagnosis is realized.

  18. Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

    Directory of Open Access Journals (Sweden)

    Yu Yuan

    2015-01-01

    Full Text Available The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid type J and order parameter q in algorithm, the value range of J and the cut-off condition of q were optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.

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

    Science.gov (United States)

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

    2016-06-01

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

  20. Quantitative NDE thermography for fault diagnosis of ball bearings with micro-foreign substances

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Won Tae [Div. of Mechanical and Automotive Engineering, Kongju NationalUniversity, Cheonan (Korea, Republic of); Hong, Dong Pyo [Dept. of Mechanical System Engineering, Chonbuk National Univerity, Jeonju (Korea, Republic of)

    2014-08-15

    In this study, a non-destructive evaluation (NDE) method is proposed for ball bearings contaminated with micro foreign substances, which were inserted into a ball bearing to create a defective specimen. The non-contact quantitative infrared thermographic technique was applied for NDE condition monitoring. Passive thermographic experiments were conducted to perform early fault diagnosis, for bearings operated at optimized torque status under a dynamic load condition. The temperature profiles for normal and defective specimens were quantitatively compared, and the thermographic data analyzed. Based on the NDE results, the temperature characteristics and abnormal fault detection of the ball bearing were quantitatively analyzed according to the rise in temperature.

  1. Planetary Gearbox Fault Detection Using Vibration Separation Techniques

    Science.gov (United States)

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

    2011-01-01

    Studies were performed to demonstrate the capability to detect planetary gear and bearing faults in helicopter main-rotor transmissions. The work supported the Operations Support and Sustainment (OSST) program with the U.S. Army Aviation Applied Technology Directorate (AATD) and Bell Helicopter Textron. Vibration data from the OH-58C planetary system were collected on a healthy transmission as well as with various seeded-fault components. Planetary fault detection algorithms were used with the collected data to evaluate fault detection effectiveness. Planet gear tooth cracks and spalls were detectable using the vibration separation techniques. Sun gear tooth cracks were not discernibly detectable from the vibration separation process. Sun gear tooth spall defects were detectable. Ring gear tooth cracks were only clearly detectable by accelerometers located near the crack location or directly across from the crack. Enveloping provided an effective method for planet bearing inner- and outer-race spalling fault detection.

  2. Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

    Directory of Open Access Journals (Sweden)

    Sheraz Ali Khan

    2016-01-01

    Full Text Available Traditional fault diagnosis methods of bearings detect characteristic defect frequencies in the envelope power spectrum of the vibration signal. These defect frequencies depend upon the inherently nonstationary shaft speed. Time-frequency and subband signal analysis of vibration signals has been used to deal with random variations in speed, whereas design variations require retraining a new instance of the classifier for each operating speed. This paper presents an automated approach for fault diagnosis in bearings based upon the 2D analysis of vibration acceleration signals under variable speed conditions. Images created from the vibration signals exhibit unique textures for each fault, which show minimal variation with shaft speed. Microtexture analysis of these images is used to generate distinctive fault signatures for each fault type, which can be used to detect those faults at different speeds. A k-nearest neighbor classifier trained using fault signatures generated for one operating speed is used to detect faults at all the other operating speeds. The proposed approach is tested on the bearing fault dataset of Case Western Reserve University, and the results are compared with those of a spectrum imaging-based approach.

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

    Science.gov (United States)

    Jia, Feng; Lei, Yaguo; Shan, Hongkai; Lin, Jing

    2015-01-01

    The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings. PMID:26610501

  4. Autoregressive modelling for rolling element bearing fault diagnosis

    Science.gov (United States)

    Al-Bugharbee, H.; Trendafilova, I.

    2015-07-01

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

  5. An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

    Directory of Open Access Journals (Sweden)

    Meijiao Li

    2014-11-01

    Full Text Available In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher computational efficiency and is capable of overcoming modal mixing and aliasing. Therefore, the proposed method is more suitable for rolling bearing diagnosis.

  6. Final Technical Report: PV Fault Detection Tool.

    Energy Technology Data Exchange (ETDEWEB)

    King, Bruce Hardison [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Christian Birk [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-12-01

    The PV Fault Detection Tool project plans to demonstrate that the FDT can (a) detect catastrophic and degradation faults and (b) identify the type of fault. This will be accomplished by collecting fault signatures using different instruments and integrating this information to establish a logical controller for detecting, diagnosing and classifying each fault.

  7. Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram.

    Science.gov (United States)

    Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

    2016-09-13

    Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.

  8. Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

    Directory of Open Access Journals (Sweden)

    Xianglong Chen

    2016-09-01

    Full Text Available Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.

  9. Static Decoupling in fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    1998-01-01

    An algebraic approach is given for a design of a static residual weighting factor in connection with fault detection. A complete parameterization is given of the weighting factor which will minimize a given performance index......An algebraic approach is given for a design of a static residual weighting factor in connection with fault detection. A complete parameterization is given of the weighting factor which will minimize a given performance index...

  10. Row fault detection system

    Science.gov (United States)

    Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D.; Smith, Brian Edward

    2008-10-14

    An apparatus, program product and method checks for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

  11. Fault Early Diagnosis of Rolling Element Bearings Combining Wavelet Filtering and Degree of Cyclostationarity Analysis

    Institute of Scientific and Technical Information of China (English)

    ZHOU Fu-chang; CHEN Jin; HE Jun; BI Guo; LI Fu-cai; ZHANG Gui-cai

    2005-01-01

    The vibration signals of rolling element bearing are produced by a combination of periodic and random processes due to the machine's rotation cycle and interaction with the real world. The combination of such components can give rise to signals, which have periodically time-varying ensemble statistical and are best considered as cyclostationary. When the early fault occurs, the background noise is very heavy, it is difficult to disclose the latent periodic components successfully using cyclostationary analysis alone. In this paper the degree of cyclostationarity is combined with wavelet filtering for detection of rolling element bearing early faults. Using the proposed entropy minimization rule. The parameters of the wavelet filter are optimized. This method is shown to be effective in detecting rolling element bearing early fault when cyclostationary analysis by itself fails.

  12. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    Science.gov (United States)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

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

    Science.gov (United States)

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

    2016-05-01

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

  14. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

    Directory of Open Access Journals (Sweden)

    Xiang Wang

    2015-07-01

    Full Text Available Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD, and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

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

    Science.gov (United States)

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

    2015-01-01

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

  16. Fault Tolerant Magnetic Bearing Testing and Conical Magnetic Bearing Development for Extreme Temperature Environments

    Science.gov (United States)

    Keith, Theo G., Jr.; Clark, Daniel

    2004-01-01

    During the six month tenure of the grant, activities included continued research of hydrostatic bearings as a viable backup-bearing solution for a magnetically levitated shaft system in extreme temperature environments (1000 F), developmental upgrades of the fault-tolerant magnetic bearing rig at the NASA Glenn Research Center, and assisting in the development of a conical magnetic bearing for extreme temperature environments, particularly turbomachinery. It leveraged work from the ongoing Smart Efficient Components (SEC) and the Turbine-Based Combined Cycle (TBCC) program at NASA Glenn Research Center. The effort was useful in providing technology for more efficient and powerful gas turbine engines.

  17. Wind turbine fault detection and fault tolerant control

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Johnson, Kathryn

    2013-01-01

    In this updated edition of a previous wind turbine fault detection and fault tolerant control challenge, we present a more sophisticated wind turbine model and updated fault scenarios to enhance the realism of the challenge and therefore the value of the solutions. This paper describes the challe...

  18. Fault tree analysis of most common rolling bearing tribological failures

    Science.gov (United States)

    Vencl, Aleksandar; Gašić, Vlada; Stojanović, Blaža

    2017-02-01

    Wear as a tribological process has a major influence on the reliability and life of rolling bearings. Field examinations of bearing failures due to wear indicate possible causes and point to the necessary measurements for wear reduction or elimination. Wear itself is a very complex process initiated by the action of different mechanisms, and can be manifested by different wear types which are often related. However, the dominant type of wear can be approximately determined. The paper presents the classification of most common bearing damages according to the dominant wear type, i.e. abrasive wear, adhesive wear, surface fatigue wear, erosive wear, fretting wear and corrosive wear. The wear types are correlated with the terms used in ISO 15243 standard. Each wear type is illustrated with an appropriate photograph, and for each wear type, appropriate description of causes and manifestations is presented. Possible causes of rolling bearing failure are used for the fault tree analysis (FTA). It was performed to determine the root causes for bearing failures. The constructed fault tree diagram for rolling bearing failure can be useful tool for maintenance engineers.

  19. A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Zhaowen Chen

    2014-01-01

    Full Text Available Mathematical morphology (MM is an efficient nonlinear signal processing tool. It can be adopted to extract fault information from bearing signal according to a structuring element (SE. Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal. In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal. A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults. The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively. Results show that all faults can be detected clearly and correctly. Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity.

  20. Fault detection using (PI) observers

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, J.; Shafai, B.

    The fault detection and isolation (FDI) problem in connection with Proportional Integral (PI) Observers is considered in this paper. A compact formulation of the FDI design problem using PI observers is given. An analysis of the FDI design problem is derived with respectt to the time domain...... properties. A method for design of PI observers applied to FDI is given....

  1. Fault Detection for Nonlinear Systems

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, H.H.

    1998-01-01

    The paper describes a general method for designing fault detection and isolation (FDI) systems for nonlinear processes. For a rich class of nonlinear systems, a nonlinear FDI system can be designed using convex optimization procedures. The proposed method is a natural extension of methods based...

  2. Study on NDT Fault Diagnosis of the Ball Bearing under Stage of Abrasion by Infrared Thermography

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Jin Ju; Hong, Dong Pyo [Chonbuk National University, Jeonju (Korea, Republic of); Kim, Won Tae [Kongju National University, Gongju (Korea, Republic of)

    2012-02-15

    For fault detection about the abrasion stage of rotational machineries under the dynamic loading conditions unlike the traditional diagnosis method used in the past decade, the infrared thermographic method with its distinctive advantages in non-contact, non-destructive, and visible aspects is proposed. In this paper, by applying a rotating deep-grooved ball bearing, passive thermographic experiments were conducted as an alternative way to proceeding the traditional fault monitoring on spectrum analyzer. As results, the thermographic experiment was compared with the traditional vibration spectrum analysis to evaluate the efficiency of the proposed method. Based on the results obtained as NDT, the temperature characteristics and abnormal fault detections of the ball bearing according to the abrasion stage were analyzed.

  3. Robust fault detection filter design

    Science.gov (United States)

    Douglas, Randal Kirk

    The detection filter is a specially tuned linear observer that forms the residual generation part of an analytical redundancy system designed for model-based fault detection and identification. The detection filter has an invariant state subspace structure that produces a residual with known and fixed directional characteristics in response to a known design fault direction. In addition to a parameterization of the detection filter gain, three methods are given for improving performance in the presence of system disturbances, sensor noise, model mismatch and sensitivity to small parameter variations. First, it is shown that by solving a modified algebraic Riccati equation, a stabilizing detection filter gain is found that bounds the H-infinity norm of the transfer matrix from system disturbances and sensor noise to the detection filter residual. Second, a specially chosen expanded-order detection filter is formed with fault detection properties identical to a set of independent reduced-order filters that have no structural constraints. This result is important to the practitioner because the difficult problem of finding a detection filter insensitive to disturbances and sensor noise is converted to the easier problem of finding a set of uncoupled noise insensitive filters. Furthermore, the statistical properties of the reduced-order filter residuals are easier to find than the statistical properties of the structurally constrained detection filter residual. Third, an interpretation of the detection filter as a special case of the dual of the restricted decoupling problem leads to a new detection filter eigenstructure assignment algorithm. The new algorithm places detection filter left eigenvectors, which annihilate the detection spaces, rather than right eigenvectors, which span the detection spaces. This allows for a more flexible observer based fault detection system structure that could not be formulated as a detection filter. Furthermore, the link to the dual

  4. Fault Diagnosis in a Centrifugal Pump Using Active Magnetic Bearings

    Directory of Open Access Journals (Sweden)

    Rainer Nordmann

    2004-01-01

    compared to state-of-the-art diagnostic tools which are only based on the measurement of the systems outputs, i.e., displacements. In this article, the different steps of the model-based diagnosis, which are modeling, generation of significant features, respectively symptoms, fault detection, and the diagnosis procedure itself are presented and in particular, it is shown how an exemplary fault is detected and identified.

  5. Expert System Detects Power-Distribution Faults

    Science.gov (United States)

    Walters, Jerry L.; Quinn, Todd M.

    1994-01-01

    Autonomous Power Expert (APEX) computer program is prototype expert-system program detecting faults in electrical-power-distribution system. Assists human operators in diagnosing faults and deciding what adjustments or repairs needed for immediate recovery from faults or for maintenance to correct initially nonthreatening conditions that could develop into faults. Written in Lisp.

  6. Actuator Fault Detection and Diagnosis for Quadrotors

    NARCIS (Netherlands)

    Lu, P.; Van Kampen, E.-J.; Yu, B.

    2014-01-01

    This paper presents a method for fault detection and diagnosis of actuator loss of effectiveness for a quadrotor helicopter. This paper not only considers the detection of the actuator loss of effectiveness faults, but also addresses the diagnosis of the faults. The detection and estimation of the f

  7. Impact Seeded Fault Data of Helicopter Oil Cooler Fan Bearings

    Science.gov (United States)

    2011-11-01

    and a tachometer signal to provide a measure of the shaft speed. Data were acquired using a National Instruments-based PXI system; the vibration data...approximately 40–45 min in duration) of the bearings to measure stable vibration signals—the ―baseline test‖ for the different fault conditions tested...5. Examples of Axial and Radial Vibration Data 13 6. Conclusion 15 7. References 16 Distribution List 17 iv List of Figures Figure 1. Oil

  8. Fault diagnosis for tilting-pad journal bearing based on SVD and LMD

    Directory of Open Access Journals (Sweden)

    Zhang Xiaotao

    2016-01-01

    Full Text Available Aiming at fault diagnosis for tilting-pad journal bearing with fluid support developed recently, a new method based on singular value decomposition (SVD and local mean decomposition (LMD is proposed. First, the phase space reconstruction of Hankel matrix and SVD method are used as pre-filter process unit to reduce the random noises in the original signal. Then the purified signal is decomposed by LMD into a series of production functions (PFs. Based on PFs, time frequency map and marginal spectrum can be obtained for fault diagnosis. Finally, this method is applied to numerical simulation and practical experiment data. The results show that the proposed method can effectively detect fault features of tilting-pad journal bearing.

  9. The extraction of wind turbine rolling bearing fault features based on VMD and bispectrum

    Science.gov (United States)

    Yuan, Jingyi; Song, Peng; Wang, Yongjie

    2017-08-01

    Aiming at extracting wind turbine rolling bearing fault feature against the background noise, the method of based on variational mode decomposition and bispectrum were proposed. Firstly, the rolling bearing fault signal was decomposed using VMD. The two components, which had obvious impact features, were extracted and reconstructed using the kurtosis-correlation coefficient criteria. Secondly, the reconstructed signal was analyzed using the bispectrum. The method has good noise suppression capability. Lastly, according to the bispectrum analysis, the fault feature of rolling bearing could be extracted. The analysis of rolling bearing fault simulation signal verifies the effectiveness of the proposed method. And it was applied to extract the fault features of the bearing fault test signal. The different fault features of rolling bearing could be identified effectively. Thus the fault diagnosis can be achieved accurately.

  10. Bearing fault identification by higher order energy operator fusion: A non-resonance based approach

    Science.gov (United States)

    Faghidi, H.; Liang, M.

    2016-10-01

    We report a non-resonance based approach to bearing fault detection. This is achieved by a higher order energy operator fusion (HOEO_F) method. In this method, multiple higher order energy operators are fused to form a single simple transform to process the bearing signal obscured by noise and vibration interferences. The fusion is guided by entropy minimization. Unlike the popular high frequency resonance technique, this method does not require the information of resonance excited by the bearing fault. The effects of the HOEO_F method on signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR) are illustrated in this paper. The performance of the proposed method in handling noise and interferences has been examined using both simulated and experimental data. The results indicate that the HOEO_F method outperforms both the envelope method and the original energy operator method.

  11. Rolling Element Bearing Fault Diagnosis Using Laplace-Wavelet Envelope Power Spectrum

    Directory of Open Access Journals (Sweden)

    D. K. Harrison

    2007-01-01

    Full Text Available The bearing characteristic frequencies (BCF contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations. They are difficult to find in their frequency spectra when using the common technique of fast fourier transforms (FFT. Therefore, Envelope Detection (ED has always been used with FFT to identify faults occurring at the BCF. However, the computation of the ED is suffering to strictly define the resonance frequency band. In this paper, an alternative approach based on the Laplace-wavelet enveloped power spectrum is proposed. The Laplace-Wavelet shape parameters are optimized based on Kurtosis maximization criteria. The results for simulated as well as real bearing vibration signal show the effectiveness of the proposed method to extract the bearing fault characteristic frequencies from the resonant frequency band.

  12. Self adaptive multi-scale morphology AVG-Hat filter and its application to fault feature extraction for wheel bearing

    Science.gov (United States)

    Deng, Feiyue; Yang, Shaopu; Tang, Guiji; Hao, Rujiang; Zhang, Mingliang

    2017-04-01

    Wheel bearings are essential mechanical components of trains, and fault detection of the wheel bearing is of great significant to avoid economic loss and casualty effectively. However, considering the operating conditions, detection and extraction of the fault features hidden in the heavy noise of the vibration signal have become a challenging task. Therefore, a novel method called adaptive multi-scale AVG-Hat morphology filter (MF) is proposed to solve it. The morphology AVG-Hat operator not only can suppress the interference of the strong background noise greatly, but also enhance the ability of extracting fault features. The improved envelope spectrum sparsity (IESS), as a new evaluation index, is proposed to select the optimal filtering signal processed by the multi-scale AVG-Hat MF. It can present a comprehensive evaluation about the intensity of fault impulse to the background noise. The weighted coefficients of the different scale structural elements (SEs) in the multi-scale MF are adaptively determined by the particle swarm optimization (PSO) algorithm. The effectiveness of the method is validated by analyzing the real wheel bearing fault vibration signal (e.g. outer race fault, inner race fault and rolling element fault). The results show that the proposed method could improve the performance in the extraction of fault features effectively compared with the multi-scale combined morphological filter (CMF) and multi-scale morphology gradient filter (MGF) methods.

  13. Fault detection and isolation for complex system

    Science.gov (United States)

    Jing, Chan Shi; Bayuaji, Luhur; Samad, R.; Mustafa, M.; Abdullah, N. R. H.; Zain, Z. M.; Pebrianti, Dwi

    2017-07-01

    Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

  14. A METHODOLOGY FOR THE DETECTION AND DIAGNOSTIC OF LOCALIZED FAULTS IN GEARS AND ROLLING BEARINGS SYSTEMS METODOLOGÍA PARA LA DETECCIÓN Y DIAGNÓSTICO DE FALLAS LOCALIZADAS EN SISTEMAS DE ENGRANAJES Y RODAMIENTOS

    Directory of Open Access Journals (Sweden)

    César San Martín

    2010-04-01

    Full Text Available In this work, an effective methodology to detect early stage faults in rotating machinery is proposed. The methodology is based on the analysis of cyclostationarity, which is inherent to the vibration signals generated by rotating machines. Of a particularly interest are the second and higher orders cyclostationary components since they contain valuable information, which can be used for the early detection of faults in rolling bearings and gear systems. The first step of the methodology consists in the separation of the first-order periodicity components from the raw signal, in order to focus the analysis in the residual part of the signal, which contains the second and higher order periodicities. Then, the residual signal is filtered and demodulated, using the frequency range of highest importance. Finally, the demodulated residual signal is auto-correlated, obtaining an enhanced signal that may contain clear spectral components related to the presence of a prospective localized fault. The methodology is validated analyzing experimental vibration data for two different cases. The first case is related to the detection of a crack in one of the teeth of a gearbox system and the second case is related to the detection of a pitfall in the inner race of a rolling bearing. The results show that the proposed method for the condition monitoring of rotating machines is a useful tool for the tasks of fault diagnosis, which can complement the analysis made using traditional diagnostic techniques.En este trabajo se presenta una metodología para detectar fallas incipientes en máquinas rotatorias. La metodología está basada en el análisis de cicloestacionariedad, la cual está presente en las señales de vibración generadas por máquinas rotatorias. De particular interés son las componentes cicloestacionarias de segundo orden y de órdenes superiores, puesto que contienen información relevante, que puede ser usada para detección temprana de fallas

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

    Science.gov (United States)

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

    2016-01-01

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

  16. Bearing Fault Classification Based on Conditional Random Field

    Directory of Open Access Journals (Sweden)

    Guofeng Wang

    2013-01-01

    Full Text Available Condition monitoring of rolling element bearing is paramount for predicting the lifetime and performing effective maintenance of the mechanical equipment. To overcome the drawbacks of the hidden Markov model (HMM and improve the diagnosis accuracy, conditional random field (CRF model based classifier is proposed. In this model, the feature vectors sequences and the fault categories are linked by an undirected graphical model in which their relationship is represented by a global conditional probability distribution. In comparison with the HMM, the main advantage of the CRF model is that it can depict the temporal dynamic information between the observation sequences and state sequences without assuming the independence of the input feature vectors. Therefore, the interrelationship between the adjacent observation vectors can also be depicted and integrated into the model, which makes the classifier more robust and accurate than the HMM. To evaluate the effectiveness of the proposed method, four kinds of bearing vibration signals which correspond to normal, inner race pit, outer race pit and roller pit respectively are collected from the test rig. And the CRF and HMM models are built respectively to perform fault classification by taking the sub band energy features of wavelet packet decomposition (WPD as the observation sequences. Moreover, K-fold cross validation method is adopted to improve the evaluation accuracy of the classifier. The analysis and comparison under different fold times show that the accuracy rate of classification using the CRF model is higher than the HMM. This method brings some new lights on the accurate classification of the bearing faults.

  17. Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis

    Science.gov (United States)

    Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yan, Ruqiang

    2016-12-01

    The bearing failure, generating harmful vibrations, is one of the most frequent reasons for machine breakdowns. Thus, performing bearing fault diagnosis is an essential procedure to improve the reliability of the mechanical system and reduce its operating expenses. Most of the previous studies focused on rolling bearing fault diagnosis could be categorized into two main families, kurtosis-based filter method and wavelet-based shrinkage method. Although tremendous progresses have been made, their effectiveness suffers from three potential drawbacks: firstly, fault information is often decomposed into proximal frequency bands and results in impulsive feature frequency band splitting (IFFBS) phenomenon, which significantly degrades the performance of capturing the optimal information band; secondly, noise energy spreads throughout all frequency bins and contaminates fault information in the information band, especially under the heavy noisy circumstance; thirdly, wavelet coefficients are shrunk equally to satisfy the sparsity constraints and most of the feature information energy are thus eliminated unreasonably. Therefore, exploiting two pieces of prior information (i.e., one is that the coefficient sequences of fault information in the wavelet basis is sparse, and the other is that the kurtosis of the envelope spectrum could evaluate accurately the information capacity of rolling bearing faults), a novel weighted sparse model and its corresponding framework for bearing fault diagnosis is proposed in this paper, coined KurWSD. KurWSD formulates the prior information into weighted sparse regularization terms and then obtains a nonsmooth convex optimization problem. The alternating direction method of multipliers (ADMM) is sequentially employed to solve this problem and the fault information is extracted through the estimated wavelet coefficients. Compared with state-of-the-art methods, KurWSD overcomes the three drawbacks and utilizes the advantages of both family

  18. A Prognostic Method for Fault Detection in Wind Turbine Drivetrains

    DEFF Research Database (Denmark)

    Nejada, Amir R.; Odgaard, Peter Fogh; Gao, Zhen

    2014-01-01

    In this paper, a prognostic method is presented for fault detection in gears and bearings in wind turbine drivetrains. This method is based on angular velocity measurements from the gearbox input shaft and the output to the generator, using two additional angular velocity sensors on the intermedi......In this paper, a prognostic method is presented for fault detection in gears and bearings in wind turbine drivetrains. This method is based on angular velocity measurements from the gearbox input shaft and the output to the generator, using two additional angular velocity sensors...... on the intermediate shafts inside the gearbox. An angular velocity error function is defined and compared in the faulty and fault-free conditions in frequency domain. Faults can be detected from the change in the energy level of the frequency spectrum of an error function. The method is demonstrated by detecting...... a dynamometer test bench and applied to the numerical gearbox model. The method is exemplified using a 750 kW wind turbine gearbox. The case study results show that defects in the high- and intermediate-speed bearings can be detected using this method. It is shown that this procedure is relatively simple, yet...

  19. A new time-frequency method for identification and classification of ball bearing faults

    Science.gov (United States)

    Attoui, Issam; Fergani, Nadir; Boutasseta, Nadir; Oudjani, Brahim; Deliou, Adel

    2017-06-01

    In order to fault diagnosis of ball bearing that is one of the most critical components of rotating machinery, this paper presents a time-frequency procedure incorporating a new feature extraction step that combines the classical wavelet packet decomposition energy distribution technique and a new feature extraction technique based on the selection of the most impulsive frequency bands. In the proposed procedure, firstly, as a pre-processing step, the most impulsive frequency bands are selected at different bearing conditions using a combination between Fast-Fourier-Transform FFT and Short-Frequency Energy SFE algorithms. Secondly, once the most impulsive frequency bands are selected, the measured machinery vibration signals are decomposed into different frequency sub-bands by using discrete Wavelet Packet Decomposition WPD technique to maximize the detection of their frequency contents and subsequently the most useful sub-bands are represented in the time-frequency domain by using Short Time Fourier transform STFT algorithm for knowing exactly what the frequency components presented in those frequency sub-bands are. Once the proposed feature vector is obtained, three feature dimensionality reduction techniques are employed using Linear Discriminant Analysis LDA, a feedback wrapper method and Locality Sensitive Discriminant Analysis LSDA. Lastly, the Adaptive Neuro-Fuzzy Inference System ANFIS algorithm is used for instantaneous identification and classification of bearing faults. In order to evaluate the performances of the proposed method, different testing data set to the trained ANFIS model by using different conditions of healthy and faulty bearings under various load levels, fault severities and rotating speed. The conclusion resulting from this paper is highlighted by experimental results which prove that the proposed method can serve as an intelligent bearing fault diagnosis system.

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

    Directory of Open Access Journals (Sweden)

    Fang Liu

    2014-05-01

    Full Text Available A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects.

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

    Science.gov (United States)

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

    2014-05-05

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects.

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

    Directory of Open Access Journals (Sweden)

    Changqing Shen

    2013-11-01

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

  3. FAULT DIAGNOSIS APPROACH FOR ROLLER BEARINGS BASED ON EMPIRICAL MODE DECOMPOSITION METHOD AND HILBERT TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    Yu Dejie; Cheng Junsheng; Yang Yu

    2005-01-01

    Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.

  4. Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

    Science.gov (United States)

    Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin

    2016-12-01

    Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.

  5. Arc burst pattern analysis fault detection system

    Science.gov (United States)

    Russell, B. Don (Inventor); Aucoin, B. Michael (Inventor); Benner, Carl L. (Inventor)

    1997-01-01

    A method and apparatus are provided for detecting an arcing fault on a power line carrying a load current. Parameters indicative of power flow and possible fault events on the line, such as voltage and load current, are monitored and analyzed for an arc burst pattern exhibited by arcing faults in a power system. These arcing faults are detected by identifying bursts of each half-cycle of the fundamental current. Bursts occurring at or near a voltage peak indicate arcing on that phase. Once a faulted phase line is identified, a comparison of the current and voltage reveals whether the fault is located in a downstream direction of power flow toward customers, or upstream toward a generation station. If the fault is located downstream, the line is de-energized, and if located upstream, the line may remain energized to prevent unnecessary power outages.

  6. Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform

    Science.gov (United States)

    He, Wangpeng; Zi, Yanyang; Chen, Binqiang; Wu, Feng; He, Zhengjia

    2015-03-01

    Mechanical anomaly is a major failure type of induction motor. It is of great value to detect the resulting fault feature automatically. In this paper, an ensemble super-wavelet transform (ESW) is proposed for investigating vibration features of motor bearing faults. The ESW is put forward based on the combination of tunable Q-factor wavelet transform (TQWT) and Hilbert transform such that fault feature adaptability is enabled. Within ESW, a parametric optimization is performed on the measured signal to obtain a quality TQWT basis that best demonstrate the hidden fault feature. TQWT is introduced as it provides a vast wavelet dictionary with time-frequency localization ability. The parametric optimization is guided according to the maximization of fault feature ratio, which is a new quantitative measure of periodic fault signatures. The fault feature ratio is derived from the digital Hilbert demodulation analysis with an insightful quantitative interpretation. The output of ESW on the measured signal is a selected wavelet scale with indicated fault features. It is verified via numerical simulations that ESW can match the oscillatory behavior of signals without artificially specified. The proposed method is applied to two engineering cases, signals of which were collected from wind turbine and steel temper mill, to verify its effectiveness. The processed results demonstrate that the proposed method is more effective in extracting weak fault features of induction motor bearings compared with Fourier transform, direct Hilbert envelope spectrum, different wavelet transforms and spectral kurtosis.

  7. Fault Detection and Isolation for Spacecraft

    DEFF Research Database (Denmark)

    Jensen, Hans-Christian Becker; Wisniewski, Rafal

    2002-01-01

    This article realizes nonlinear Fault Detection and Isolation for actuators, given there is no measurement of the states in the actuators. The Fault Detection and Isolation of the actuators is instead based on angular velocity measurement of the spacecraft and knowledge about the dynamics...

  8. Exact, almost and delayed fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Saberi, Ali; Stoorvogel, Anton A.;

    1999-01-01

    Considers the problem of fault detection and isolation while using zero or almost zero threshold. A number of different fault detection and isolation problems using exact or almost exact disturbance decoupling are formulated. Solvability conditions are given for the formulated design problems. Th...

  9. Fault Detection for a Diesel Engine Actuator

    DEFF Research Database (Denmark)

    Blanke, M.; Bøgh, S.A.; Jørgensen, R.B.

    1995-01-01

    An electro-mechanical position servo is introduced as a benchmark for mode-based Fault Detection and Identification (FDI).......An electro-mechanical position servo is introduced as a benchmark for mode-based Fault Detection and Identification (FDI)....

  10. Integration of control and fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, J.

    The integrated design of control and fault detection is studied. The result of the analysis is that it is possible to separate the design of the controller and the filter for fault detection in the case where the nominal model can be assumed to be fairly accurate. In the uncertain case, however...

  11. A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings

    Science.gov (United States)

    Wang, Huaqing; Ke, Yanliang; Song, Liuyang; Tang, Gang; Chen, Peng

    2016-01-01

    The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which impedes the application of CS theory. Therefore, it is of great significance to promote the sparsity when applying the CS theory to fault diagnosis of roller bearings. To increase the sparsity of vibration signals, a sparsity-promoted method called the tunable Q-factor wavelet transform based on decomposing the analyzed signals into transient impact components and high oscillation components is utilized in this work. The former become sparser than the raw signals with noise eliminated, whereas the latter include noise. Thus, the decomposed transient impact components replace the original signals for analysis. The CS theory is applied to extract the fault features without complete reconstruction, which means that the reconstruction can be completed when the components with interested frequencies are detected and the fault diagnosis can be achieved during the reconstruction procedure. The application cases prove that the CS theory assisted by the tunable Q-factor wavelet transform can successfully extract the fault features from the compressed samples. PMID:27657063

  12. A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings

    Directory of Open Access Journals (Sweden)

    Huaqing Wang

    2016-09-01

    Full Text Available The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which impedes the application of CS theory. Therefore, it is of great significance to promote the sparsity when applying the CS theory to fault diagnosis of roller bearings. To increase the sparsity of vibration signals, a sparsity-promoted method called the tunable Q-factor wavelet transform based on decomposing the analyzed signals into transient impact components and high oscillation components is utilized in this work. The former become sparser than the raw signals with noise eliminated, whereas the latter include noise. Thus, the decomposed transient impact components replace the original signals for analysis. The CS theory is applied to extract the fault features without complete reconstruction, which means that the reconstruction can be completed when the components with interested frequencies are detected and the fault diagnosis can be achieved during the reconstruction procedure. The application cases prove that the CS theory assisted by the tunable Q-factor wavelet transform can successfully extract the fault features from the compressed samples.

  13. Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy

    Directory of Open Access Journals (Sweden)

    Lei Zhang

    2016-01-01

    Full Text Available In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT, sample entropy (SampEn, and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier.

  14. Active fault detection in MIMO systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    The focus in this paper is on active fault detection (AFD) for MIMO systems with parametric faults. The problem of design of auxiliary inputs with respect to detection of parametric faults is investigated. An analysis of the design of auxiliary inputs is given based on analytic transfer functions...... from auxiliary input to residual outputs. The analysis is based on a singular value decomposition of these transfer functions Based on this analysis, it is possible to design auxiliary input as well as design of the associated residual vector with respect to every single parametric fault in the system...

  15. Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Yunxiao Fu

    2016-06-01

    Full Text Available Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM based correntropy extracted from the Intrinsic Mode Functions (IMFs, which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD and employ Least Square Support Vector Machine (LSSVM to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.

  16. Detection of outer raceway bearing defects in small induction motors using stator current analysis

    Indian Academy of Sciences (India)

    İzzet Y Önel; K Burak Dalci; İbrahim Senol

    2005-12-01

    We investigate the application of induction motor stator current spectral analysis (MCSA) for detection of rolling element bearing damage from the outer raceway. In this work, MCSA and vibration analysis are applied to induction motor to detect outer raceway defects in faulty bearings. Data acquisition, recording,and fast fourier transform (FFT) algorithms are done by using the LabVIEW programming language. Experimental results verify the relationship between vibration analysis and MCSA, and identify the presence of outer raceway bearing defects in induction machines. This work also indicates that detecting fault frequencies by motor currents is more difficult than detecting them by vibration analysis. The use of intensive resolution FFT is recommended in MCSA for detecting faults easily. Reinstalling a faulty bearing can alter the characteristic frequencies and it is difficult to compare results from different bearings or even from the same bearing in different installations.

  17. Unitary Approximations in Fault Detection Filter Design

    Directory of Open Access Journals (Sweden)

    Dušan Krokavec

    2016-01-01

    Full Text Available The paper is concerned with the fault detection filter design requirements that relax the existing conditions reported in the previous literature by adapting the unitary system principle in approximation of fault detection filter transfer function matrix for continuous-time linear MIMO systems. Conditions for the existence of a unitary construction are presented under which the fault detection filter with a unitary transfer function can be designed to provide high residual signals sensitivity with respect to faults. Otherwise, reflecting the emplacement of singular values in unitary construction principle, an associated structure of linear matrix inequalities with built-in constraints is outlined to design the fault detection filter only with a Hurwitz transfer function. All proposed design conditions are verified by the numerical illustrative examples.

  18. Traction Motor Bearing Fault Detection Via Wavelet Packet Analysis of Stator Currents%基于定子电流小波包分析的牵引电机轴承故障诊断

    Institute of Scientific and Technical Information of China (English)

    杨江天; 赵明元; 张志强; 李平康

    2013-01-01

    Rolling element bearing failures account for a large majority of mechanical faults in a locomotive traction system. Bearing failures induce motor vibration, resulting in modulation of stator currents. In this paper, according to the effect of bearing failures on operating parameters of motor(e. g. , vibration and current) , bearing failures of traction motor were classified into single-point defects and generalized roughness. The influence of both types of rolling element bearing faults on the vibration and induction motor stator current spectrum was analyzed. Motor current signature analysis was introduced to provide a nonintrusive way to assess the health of a motor, being integrated with wavelet packet transform which offered an efficient decomposition of signals containing both transient and non-stationary components. Thus, fine frequency resolution was achieved and fault features were extracted via wavelet packet analysis of motor currents. The online locomotive traction motor diagnostic method using motor current signature analysis was presented. The proposed method was effectively applied to the operation tests of Locomotive Type HXD2 , and the incipient bearing faults of traction motor were diagnosed successfully.%滚动轴承失效是机车牵引传动系统的主要故障源之一.轴承失效引起电机振动增加,导致定子电流发生调制.本文根据轴承失效对电机运行参数(振动、电流)的影响,将牵引电机轴承故障分为单点局部损伤和整体磨损.分析两种滚动轴承故障对振动和电机定子电流频谱的影响.定子电流分析可在不影响电机运行的情况下,检测电机的工作状况.小波包变换适应于处理瞬变、非平稳信号,用于电机定子分析能获得较高的频率分辨率,有效提取故障征兆.本文提出一种基于定子电流小波包分析的机车牵引电机轴承在线故障检测方法.HXD2型机车线路运行试验证明,该方法能有效诊断电机轴承早期故障.

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

    Science.gov (United States)

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

    2013-05-01

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

  20. Quantitative evaluation on the performance and feature enhancement of stochastic resonance for bearing fault diagnosis

    Science.gov (United States)

    Li, Guoying; Li, Jimeng; Wang, Shibin; Chen, Xuefeng

    2016-12-01

    Stochastic resonance (SR) has been widely applied in the field of weak signal detection by virtue of its characteristic of utilizing noise to amplify useful signal instead of eliminating noise in nonlinear dynamical systems. How to quantitatively evaluate the performance of SR, including the enhancement effect and the degree of waveform distortion, and how to accurately extract signal amplitude have become two important issues in the research on SR. In this paper, the signal-to-noise ratio (SNR) of the main component to the residual in the SR output is constructed to quantitatively measure the enhancement effect of the SR method. And two indices are constructed to quantitatively measure the degree of waveform distortion of the SR output, including the correlation coefficient between the main component in the SR output and the original signal, and the zero-crossing ratio. These quantitative indices are combined to provide a comprehensive quantitative index for adaptive parameter selection of the SR method, and eventually the adaptive SR method can be effective in enhancing the weak component hidden in the original signal. Fast Fourier Transform and Fourier Transform (FFT+FT) spectrum correction technology can extract the signal amplitude from the original signal and effectively reduce the difficulty of extracting signal amplitude from the distorted resonance output. The application in vibration analysis for bearing fault diagnosis verifies that the proposed quantitative evaluation method for adaptive SR can effectively detect weak fault feature of the vibration signal during the incipient stage of bearing fault.

  1. Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition

    Directory of Open Access Journals (Sweden)

    Bo Zhou

    2016-01-01

    Full Text Available Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT, is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings.

  2. Detecting Fan Faults in refrigerated Cabinets

    DEFF Research Database (Denmark)

    Thybo, C.; Rasmussen, B.D.; Izadi-Zamanabadi, Roozbeh

    2002-01-01

    Fault detection in supermarket refrigeration systems is an important topic due to both economic and food safety reasons. If faults can be detected and diagnosed before the system drifts outside the specified operational envelope, service costs can be reduced and in extreme cases the costly...... discarding of food products can be avoided. In the situations where the operational requirements can be met with a fault present, the system will operate with a higher energy consumption increasing the cost of operation. The objective of this study is to develop a robust method for detecting air circulation...

  3. Cell boundary fault detection system

    Science.gov (United States)

    Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D.; Smith, Brian Edward

    2009-05-05

    A method determines a nodal fault along the boundary, or face, of a computing cell. Nodes on adjacent cell boundaries communicate with each other, and the communications are analyzed to determine if a node or connection is faulty.

  4. 基于虚拟仪器的滚动轴承故障检测仿真实验平台%Study on Simulation Experiment Platform for Fault Detection of Rolling Bearing Based on Virtual Instrument

    Institute of Scientific and Technical Information of China (English)

    张长泉

    2015-01-01

    According to the principle of virtual instrument, develops a simulation experiment platform and educes the time-domain waveform of the bearing vibration by using LabVIEWs and the data collection system CompactDAQ by American Virtual Instrument Company as well as the photoelectric sensor.By the mixed programming of LabVIEW and Matlab,five detecting parameters—the virtual values of low frequen-cy,medium frequency,high frequency,number of shock pulses and demodulation effective factors—are cal-culated on the platform and the bearing faults are judged and compared with the standard values.The experi-mental results show that the test data is highly similar to that of the professional vibration instrument and the simulation system is proven practical and reliable.%采用虚拟仪器技术原理,提出以LabVIEW为软件,以美国虚拟仪器公司的CompactDAQ数据采集系统和光电传感器为硬件,开发滚动轴承故障检测仿真实验平台,得出轴承振动时域波形图. 通过LabVIEW和Matlab混合编程的方法,在仿真平台上计算出轴承低频有效值、中频有效值、高频有效值、冲击脉冲数、解调有效值因数5个检测参数. 将数据与标准值进行对比,以此判断轴承故障是否存在. 仿真实验结果表明,该系统所测数据与专业振动仪器测试数据高度相似,仿真系统具有一定的实用性和可靠性.

  5. Fault Identification Method of Ball Bearing Based on IAs and SVMs

    Directory of Open Access Journals (Sweden)

    Chen Chao

    2016-01-01

    Full Text Available In order to effectively identify the bearing running condition, this paper proposed a new method which combines local mean decomposition (LMD and support vector machine (SVM together for ball bearing fault identification. Firstly, the gathered vibration signals were decomposed into a number of product functions (PFs by LMD, with each PF corresponding to an instantaneous amplitude (IA signal and instantaneous frequency (IF signal. Then, introduce the concept of fault characteristic amplitude ratios which can be used to construct fault feature vectors; the extracted characteristic features were input into SVM to train and construct the fault identification model; the bearing running state identification was thereby realized. Cases of normal and fault were analyzed. Experimental results show that the proposed algorithm can diagnose the bearing failures reasonable and efficient.

  6. Vibration Analysis of Industrial Drive for Broken Bearing Detection Using Probabilistic Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    K. Jayakumar

    2015-02-01

    Full Text Available A reliable monitoring of industrial drives plays a vital role to prevent from the performance degradation of machinery. Today’s fault detection system mechanism uses wavelet transform for proper detection of faults, however it required more attention on detecting higher fault rates with lower execution time. Existence of faults on industrial drives leads to higher current flow rate and the broken bearing detected system determined the number of unhealthy bearings but need to develop a faster system with constant frequency domain. Vibration data acquisition was used in our proposed work to detect broken bearing faults in induction machine. To generate an effective fault detection of industrial drives, Biorthogonal Posterior Vibration Signal-Data Probabilistic Wavelet Neural Network (BPPVS-WNN system was proposed in this paper. This system was focused to reducing the current flow and to identify faults with lesser execution time with harmonic values obtained through fifth derivative. Initially, the construction of Biorthogonal vibration signal-data based wavelet transform in BPPVS-WNN system localizes the time and frequency domain. The Biorthogonal wavelet approximates the broken bearing using double scaling and factor, identifies the transient disturbance due to fault on induction motor through approximate coefficients and detailed coefficient. Posterior Probabilistic Neural Network detects the final level of faults using the detailed coefficient till fifth derivative and the results obtained through it at a faster rate at constant frequency signal on the industrial drive. Experiment through the Simulink tool detects the healthy and unhealthy motor on measuring parametric factors such as fault detection rate based on time, current flow rate and execution time.

  7. Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

    Directory of Open Access Journals (Sweden)

    Weigang Wen

    2015-01-01

    Full Text Available Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis.

  8. Fault Detection and Isolation using Eigenstructure Assignment

    DEFF Research Database (Denmark)

    Jørgensen, R.B.; Patton, R.J.; Chen, J.

    1994-01-01

    The purpose of this article is to investigate the robustness to model uncertainties of observer based fault detection and isolation. The approach is designed with a straight forward dynamic nad the observer....

  9. The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value

    Directory of Open Access Journals (Sweden)

    Te Han

    2016-01-01

    Full Text Available Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears. Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise.

  10. Complex Demodulation for Bearing Fault Detection

    Science.gov (United States)

    1989-10-01

    components are filtered, using a digital, zero phase filter . A frequency domain representation of the filtered signal is shown in Figure 3. Digital...filtering can be seen as the convolution of the heterodyned signal with the impulse response of the zero phase filter . The resulting function {W) is then...given by, W = V H (4) where * denotes convolution and (H) is the impulse response of the zero phase filter . This can be rewritten using the convolution

  11. Fault Detection and Isolation using Eigenstructure Assignment

    DEFF Research Database (Denmark)

    Jørgensen, R.B.; Patton, R.J.; Chen, J.

    1994-01-01

    The purpose of this article is to investigate the robustness to model uncertainties of observer based fault detection and isolation. The approach is designed with a straight forward dynamic nad the observer.......The purpose of this article is to investigate the robustness to model uncertainties of observer based fault detection and isolation. The approach is designed with a straight forward dynamic nad the observer....

  12. Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jian-Jiun Ding

    2012-07-01

    Full Text Available Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE and multiscale entropy (MSE.

  13. Statistical fault detection in photovoltaic systems

    KAUST Repository

    Garoudja, Elyes

    2017-05-08

    Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array\\'s maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.

  14. DATA-MINING BASED FAULT DETECTION

    Institute of Scientific and Technical Information of China (English)

    Ma Hongguang; Han Chongzhao; Wang Guohua; Xu Jianfeng; Zhu Xiaofei

    2005-01-01

    This paper presents a fault-detection method based on the phase space reconstruction and data mining approaches for the complex electronic system. The approach for the phase space reconstruction of chaotic time series is a combination algorithm of multiple autocorrelation and Γ-test, by which the quasi-optimal embedding dimension and time delay can be obtained.The data mining algorithm, which calculates the radius of gyration of unit-mass point around the centre of mass in the phase space, can distinguish the fault parameter from the chaotic time series output by the tested system. The experimental results depict that this fault detection method can correctly detect the fault phenomena of electronic system.

  15. Controller modification applied for active fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob; Poulsen, Niels Kjølstad

    2014-01-01

    This paper is focusing on active fault detection (AFD) for parametric faults in closed-loop systems. This auxiliary input applied for the fault detection will also disturb the external output and consequently reduce the performance of the controller. Therefore, only small auxiliary inputs are used...... with the result that the detection and isolation time can be long. In this paper it will be shown, that this problem can be handled by using a modification of the feedback controller. By applying the YJBK-parameterization (after Youla, Jabr, Bongiorno and Kucera) for the controller, it is possible to modify...... the feedback controller with a minor effect on the external output in the fault free case. Further, in the faulty case, the signature of the auxiliary input can be optimized. This is obtained by using a band-pass filter for the YJBK parameter that is only effective in a small frequency range where...

  16. Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine

    Science.gov (United States)

    Mao, Wentao; He, Ling; Yan, Yunju; Wang, Jinwan

    2017-01-01

    Diagnosis of bearings generally plays an important role in fault diagnosis of mechanical system, and machine learning has been a promising tool in this field. In many real applications of bearings fault diagnosis, the data tend to be online imbalanced, which means, the number of fault data is much less than the normal data while they are all collected in online sequential way. Suffering from this problem, many traditional diagnosis methods will get low accuracy of fault data which acts as the minority class in the collected bearing data. To address this problem, an online sequential prediction method for imbalanced fault diagnosis problem is proposed based on extreme learning machine. This method introduces the principal curve and granulation division to simulate the flow distribution and overall distribution characteristics of fault data, respectively. Then a confident over-sampling and under-sampling process is proposed to establish the initial offline diagnosis model. In online stage, the obtained granules and principal curves are rebuilt on the bearing data which are arrived in sequence, and after the over-sampling and under-sampling process, the balanced sample set is formed to update the diagnosis model dynamically. A theoretical analysis is provided and proves that, even existing information loss, the proposed method has lower bound of the model reliability. Simulation experiments are conducted on IMS bearing data and CWRU bearing data. The comparative results demonstrate that the proposed method can improve the fault diagnosis accuracy with better effectiveness and robustness than other algorithms.

  17. Fault Feature Extraction of Rolling Bearing Based on an Improved Cyclical Spectrum Density Method

    Institute of Scientific and Technical Information of China (English)

    LI Min; YANG Jianhong; WANG Xiaojing

    2015-01-01

    The traditional cyclical spectrum density(CSD) method is widely used to analyze the fault signals of rolling bearing. All modulation frequencies are demodulated in the cyclic frequency spectrum. Consequently, recognizing bearing fault type is difficult. Therefore, a new CSD method based on kurtosis(CSDK) is proposed. The kurtosis value of each cyclic frequency is used to measure the modulation capability of cyclic frequency. When the kurtosis value is large, the modulation capability is strong. Thus, the kurtosis value is regarded as the weight coefficient to accumulate all cyclic frequencies to extract fault features. Compared with the traditional method, CSDK can reduce the interference of harmonic frequency in fault frequency, which makes fault characteristics distinct from background noise. To validate the effectiveness of the method,experiments are performed on the simulation signal, the fault signal of the bearing outer race in the test bed, and the signal gathered from the bearing of the blast furnace belt cylinder. Experimental results show that the CSDK is better than the resonance demodulation method and the CSD in extracting fault features and recognizing degradation trends. The proposed method provides a new solution to fault diagnosis in bearings.

  18. Fault diagnosis of the rolling bearing with optical fiber Bragg grating vibration sensor

    Science.gov (United States)

    Wei, Peng; Dai, Zejing; Zheng, Leilei; Li, Ming

    2016-10-01

    Fault diagnosis of the rolling bearing means a lot for property and life safety. In this paper the Fiber Bragg Grating (FBG) vibration sensor and resonance demodulation technology are used in the fault diagnosis of the rolling bearing. Traditionally, the vibration signals are measured by the resistance strain gauge, accelerometer, etc. But those traditional electronic sensors are usually influenced by the industry electromagnetic noise. But the FBG vibration sensor is totally different. It has a lot of advantages such as small volume, light weight, easy connection and so on. And the high industry electromagnetic noise means nothing to the FBG sensors. In this paper, we use the FBG vibration and temperature sensors to measure the fast strain and temperature signal of the rolling bearing. In order to extract the fault signals from strong background noise, the resonant demodulation technology is used to analyze and process the vibration signals collected by the FBG sensors. In order to verify the reliability of the FBG vibration sensor and resonance demodulation technology applied in the fault diagnosis of the rolling bearing, several experiments are done. Five FBG vibration sensors are attached on the different parts of the rolling bearing to verify its function and its influence on the fault diagnosis of the rolling bearing. The results of the experiments show that the FBG vibration sensor method could be used in fault diagnosis of the rolling bearing. The repetitive experiments show the reliability of the FBG vibration sensors method.

  19. Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Lingli Jiang

    2011-01-01

    Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

  20. An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination

    Directory of Open Access Journals (Sweden)

    Anamika Yadav

    2014-01-01

    Full Text Available Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.

  1. Fault Detection and Control of Process Systems

    Directory of Open Access Journals (Sweden)

    Vu Trieu Minh

    2007-01-01

    Full Text Available This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM estimator and generalized predictive control (GPC algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.

  2. Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing

    Science.gov (United States)

    Lv, Yong; Yuan, Rui; Song, Gangbing

    2016-12-01

    Rolling bearings are widely used in rotary machinery systems. The measured vibration signal of any part linked to rolling bearings contains fault information when failure occurs, differing only by energy levels. Bearing failure will cause the vibration of other components, and therefore the collected bearing vibration signals are mixed with vibration signal of other parts and noise. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can avoid the loss of local information. Subsequently using the multivariate empirical mode decomposition (multivariate EMD) to simultaneously analyze the multivariate signal is beneficial to extract fault information, especially for weak fault characteristics during the period of early failure. This paper proposes a novel method for fault feature extraction of rolling bearing based on multivariate EMD. The nonlocal means (NL-means) denoising method is used to preprocess the multivariate signal and the correlation analysis is employed to calculate fault correlation factors to select effective intrinsic mode functions (IMFs). Finally characteristic frequencies are extracted from the selected IMFs by spectrum analysis. The numerical simulations and applications to bearing monitoring verify the effectiveness of the proposed method and indicate that this novel method is promising in the field of signal decomposition and fault diagnosis.

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

    Science.gov (United States)

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

    2016-04-01

    The energy concentration level is an important indicator for time-frequency analysis (TFA). Weak energy concentration would result in time-frequency representation (TFR) diffusion and thus leading to ambiguous results or even misleading signal analysis results, particularly for nonstationary multicomponent signals. To improve the energy concentration level, this paper proposes a generalized stepwise demodulation transform (GSDT). The rationale of the proposed method is that (1) the generalized demodulation (GD) can map the original signal into an analytic signal with constant instantaneous frequency (IF) and improve the energy concentration level on time-frequency plane, and (2) focusing on a short window around the time instant of interest, a backward demodulation operation can recover the original frequency at the time instant without affecting the improved energy concentration level. By repeating the backward demodulation at every time instant of interest, the TFR of the entire signal can be attained with enhanced energy concentration level. With the GSDT, an iterative GSDT (IGSDT) is developed to analyze multicomponent signal that is subjected to different modulating sources for their constituent components. The IGSDT iteratively demodulates each constituent component to attain its TFR and the TFR of the whole signal is derived from superposing all the resulting TFRs of constituent components. The cross-term free and more energy concentrated TFR of the signal is, therefore, obtained, and the diffusion in the TFR can be reduced. The GSDT-based synchrosqueezing transform is also elaborated to further enhance the GSDT(IGSDT) yielded TFR. The effectiveness of the proposed method in TFA is tested using both simulated monocomponent and multicomponent signals. The application of the proposed method to bearing fault detection is explored. Bearing condition and fault pattern can be revealed by the proposed method resulting TFR. The main advantages of the proposed method

  4. Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution

    Science.gov (United States)

    Li, Jimeng; Li, Ming; Zhang, Jinfeng

    2017-08-01

    Rolling bearings are the key components in the modern machinery, and tough operation environments often make them prone to failure. However, due to the influence of the transmission path and background noise, the useful feature information relevant to the bearing fault contained in the vibration signals is weak, which makes it difficult to identify the fault symptom of rolling bearings in time. Therefore, the paper proposes a novel weak signal detection method based on time-delayed feedback monostable stochastic resonance (TFMSR) system and adaptive minimum entropy deconvolution (MED) to realize the fault diagnosis of rolling bearings. The MED method is employed to preprocess the vibration signals, which can deconvolve the effect of transmission path and clarify the defect-induced impulses. And a modified power spectrum kurtosis (MPSK) index is constructed to realize the adaptive selection of filter length in the MED algorithm. By introducing the time-delayed feedback item in to an over-damped monostable system, the TFMSR method can effectively utilize the historical information of input signal to enhance the periodicity of SR output, which is beneficial to the detection of periodic signal. Furthermore, the influence of time delay and feedback intensity on the SR phenomenon is analyzed, and by selecting appropriate time delay, feedback intensity and re-scaling ratio with genetic algorithm, the SR can be produced to realize the resonance detection of weak signal. The combination of the adaptive MED (AMED) method and TFMSR method is conducive to extracting the feature information from strong background noise and realizing the fault diagnosis of rolling bearings. Finally, some experiments and engineering application are performed to evaluate the effectiveness of the proposed AMED-TFMSR method in comparison with a traditional bistable SR method.

  5. Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis

    Science.gov (United States)

    Zhou, Peng; Lu, Siliang; Liu, Fang; Liu, Yongbin; Li, Guihua; Zhao, Jiwen

    2017-03-01

    Stochastic resonance (SR), which is characterized by the fact that proper noise can be utilized to enhance weak periodic signals, has been widely applied in weak signal detection. SR is a nonlinear parameterized filter, and the output signal relies on the system parameters for the deterministic input signal. The most commonly used index for parameter tuning in the SR procedure is the signal-to-noise ratio (SNR). However, using the SNR index to evaluate the denoising effect of SR quantitatively is insufficient when the target signal frequency cannot be estimated accurately. To address this issue, six different indexes, namely, power spectral kurtosis of the SR output signal, correlation coefficient between the SR output and the original signal, peak SNR, structural similarity, root mean square error, and smoothness, are constructed in this study to measure the SR output quantitatively. These six quantitative indexes are fused into a new synthetic quantitative index (SQI) via a back propagation neural network to guide the adaptive parameter selection of the SR procedure. The index fusion procedure reduces the instability of each index and thus improves the robustness of parameter tuning. In addition, genetic algorithm is utilized to quickly select the optimal SR parameters. The efficiency of bearing fault diagnosis is thus further improved. The effectiveness and efficiency of the proposed SQI-based adaptive SR method for bearing fault diagnosis are verified through numerical and experiment analyses.

  6. Stochastic Resonance with a Joint Woods-Saxon and Gaussian Potential for Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Haibin Zhang

    2014-01-01

    Full Text Available This work aims for a new stochastic resonance (SR model which performs well in bearing fault diagnosis. Different from the traditional bistable SR system, we realize the SR based on the joint of Woods-Saxon potential (WSP and Gaussian potential (GP instead of a reflection-symmetric quartic potential. With this potential model, all the parameters in the Woods-Saxon and Gaussian SR (WSGSR system are not coupled when compared to the traditional one, so the output signal-to-noise ratio (SNR can be optimized much more easily by tuning the system parameters. Besides, a smoother potential bottom and steeper potential wall lead to a stable particle motion within each potential well and avoid the unexpected noise. Different from the SR with only WSP which is a monostable system, we improve it into a bistable one as a general form offering a higher SNR and a wider bandwidth. Finally, the proposed model is verified to be outstanding in weak signal detection for bearing fault diagnosis and the strategy offers us a more effective and feasible diagnosis conclusion.

  7. Fault Detection and Isolation in Centrifugal Pumps

    DEFF Research Database (Denmark)

    Kallesøe, Carsten

    Centrifugal pumps are used in a variety of different applications, such as water supply, wastewater, and different industrial applications. Some pump installations are crucial for the applications to work. Failures can lead to substantial economic losses and can influence the life of many people...... when they occur. Therefore, detection of faults, if possible in an early stage, and isolation of their causes are of great interest. Especially fault detection, which can be used for predictive maintenance, can decrease working expenses and increase the reliability of the application in which the pump...... is placed. The topic of this work is Fault Detection and Identification in centrifugal pumps. Different approaches are developed with special focus on robustness. Robustness with respect to disturbances, unknown parts of the system, and parameter variations are considered. All developed algorithms...

  8. Fault Detection under Fuzzy Model Uncertainty

    Institute of Scientific and Technical Information of China (English)

    Marek Kowal; Józef Korbicz

    2007-01-01

    The paper tackles the problem of robust fault detection using Takagi-Sugeno fuzzy models. A model-based strategy is employed to generate residuals in order to make a decision about the state of the process. Unfortunately, such a method is corrupted by model uncertainty due to the fact that in real applications there exists a model-reality mismatch. In order to ensure reliable fault detection the adaptive threshold technique is used to deal with the mentioned problem. The paper focuses also on fuzzy model design procedure. The bounded-error approach is applied to generating the rules for the model using available measurements. The proposed approach is applied to fault detection in the DC laboratory engine.

  9. Online Distributed Fault Detection of Sensor Measurements

    Institute of Scientific and Technical Information of China (English)

    GAO Jianliang; XU Yongjun; LI Xiaowei

    2007-01-01

    In wireless sensor networks (WSNs), a faulty sensor may produce incorrect data and transmit them to the other sensors. This would consume the limited energy and bandwidth of WSNs. Furthermore, the base station may make inappropriate decisions when it receives the incorrect data sent by the faulty sensors. To solve these problems, this paper develops an online distributed algorithm to detect such faults by exploring the weighted majority vote scheme. Considering the spatial correlations in WSNs, a faulty sensor can diagnose itself through utilizing the spatial and time information provided by its neighbor sensors. Simulation results show that even when as many as 30% of the sensors are faulty, over 95% of faults can be correctly detected with our algorithm. These results indicate that the proposed algorithm has excellent performance in detecting fault of sensor measurements in WSNs.

  10. Wavelet neural network and its application in fault diagnosis of rolling bearing

    Science.gov (United States)

    Wang, Guo-Feng; Wang, Tai-Yong

    2005-12-01

    In order to realize diagnosis of rolling bearing of rotating machines, the wavelet neural network was proposed. This kind of artificial neural network takes wavelet function as neuron of hidden layer so as to realize nonlinear mapping between fault and symptoms. A algorithm based on minimum mean square error was given to obtain the weight value of network, dilation and translation parameter of wavelet function. To testify the correctness of wavelet neural network, it was adopted in diagnosing the fault type and location of rolling bearing. The final result shows that it can recognize the fault of outer race, inner race and roller accurately.

  11. Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition

    Directory of Open Access Journals (Sweden)

    Yujie Cheng

    2017-05-01

    Full Text Available Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP, which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS, we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.

  12. Fault Diagnosis of Train Axle Box Bearing Based on Multifeature Parameters

    Directory of Open Access Journals (Sweden)

    Xiaofeng Li

    2015-01-01

    Full Text Available Failure of the train axle box bearing will cause great loss. Now, condition-based maintenance of train axle box bearing has been a research hotspot around the world. Vibration signals generated by train axle box bearing have nonlinear and nonstationary characteristics. The methods used in traditional bearing fault diagnosis do not work well with the train axle box. To solve this problem, an effective method of axle box bearing fault diagnosis based on multifeature parameters is presented in this paper. This method can be divided into three parts, namely, weak fault signal extraction, feature extraction, and fault recognition. In the first part, a db4 wavelet is employed for denoising the original signals from the vibration sensors. In the second part, five time-domain parameters, five IMF energy-torque features, and two amplitude-ratio features are extracted. The latter seven frequency domain features are calculated based on the empirical mode decomposition and envelope spectrum analysis. In the third part, a fault classifier based on BP neural network is designed for automatic fault pattern recognition. A series of tests are carried out to verify the proposed method, which show that the accuracy is above 90%.

  13. Time-varying demodulation analysis for rolling bearing fault diagnosis under variable speed conditions

    Science.gov (United States)

    Feng, Zhipeng; Chen, Xiaowang; Wang, Tianyang

    2017-07-01

    Rolling bearings often work under variable speed conditions, resulting in nonstationary vibrations. How to effectively extract the time-varying fault frequency from nonstationary vibration signals is a key issue in rolling bearing fault diagnosis. To address this issue, a quality time-frequency analysis of excellent time-frequency readability and robust to noise is necessary. To this end, the concentration of frequency and time (ConceFT) method is exploited. Based on this time-frequency analysis method, and considering the modulation feature of rolling bearing vibrations, we propose joint time-varying amplitude and frequency demodulated spectra to reveal the time-varying fault characteristic frequency. Firstly, the optimal frequency band sensitive to rolling bearing fault is selected by spectral kurtosis. Then, both the amplitude envelope and instantaneous frequency of the sensitive signal component within the selected optimal frequency band are calculated. Next, the ConceFT method is applied to the amplitude envelope and instantaneous frequency to generate the time-varying amplitude and frequency demodulated spectra. Finally, rolling bearing fault can be diagnosed by analysis of the time-varying frequency revealed by the time-varying demodulated spectra. This method is free from complex time-varying sidebands, and is robust to noise interference. It is illustrated by numerical simulated signal analysis, and is further validated via lab experimental rolling bearing vibration signal analyses. The localized defects on both inner and outer race are successfully diagnosed.

  14. Prognosticating fault development rate in wind turbine generator bearings using local trend models

    DEFF Research Database (Denmark)

    Skrimpas, Georgios Alexandros; Palou, Jonel; Sweeney, Christian Walsted;

    2016-01-01

    Generator bearing defects, e.g. ball, inner and outer race defects, are ranked among the most frequent mechanical failures encountered in wind turbines. Diagnosis and prognosis of bearing faults can be successfully implemented using vibration based condition monitoring systems, where tracking...... the signal energy between 10Hz to 1000Hz is utilized as feature to characterize the severity of developing bearing faults. Furthermore, local trend models are employed to predict the progression of bearing defects from a vibration standpoint in accordance with the limits suggested in ISO 10816. Predictions...... of vibration trends from multi-megawatt wind turbine generators are presented, showing the effectiveness of the suggested approach on the calculation of the RUL and fault progression rate....

  15. An adaptive deep convolutional neural network for rolling bearing fault diagnosis

    Science.gov (United States)

    Fuan, Wang; Hongkai, Jiang; Haidong, Shao; Wenjing, Duan; Shuaipeng, Wu

    2017-09-01

    The working conditions of rolling bearings usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called the adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of manual feature extraction, the deep CNN model is initialized for automatic feature learning. Secondly, to adapt to different signal characteristics, the main parameters of the deep CNN model are determined with a particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to diagnose rolling bearing faults, and the results confirm that the proposed method is more effective and robust than other intelligent methods.

  16. The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation

    Directory of Open Access Journals (Sweden)

    Jun Ma

    2015-01-01

    Full Text Available Since the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault feature extraction method for the rolling bearing based on the local mean decomposition (LMD and envelope demodulation is proposed. Firstly, decompose the original vibration signal by LMD to get a series of production functions (PFs. Then dispose the envelope demodulation analysis on PF component. Finally, perform Fourier Transform on the demodulation signals and judge failure condition according to the dominant frequency of the spectrum. The results show that the proposed method can correctly extract the fault characteristics to diagnose faults.

  17. Compressive sensing-based feature extraction for bearing fault diagnosis using a heuristic neural network

    Science.gov (United States)

    Yuan, Haiying; Wang, Xiuyu; Sun, Xun; Ju, Zijian

    2017-06-01

    Bearing fault diagnosis collects massive amounts of vibration data about a rotating machinery system, whose fault classification largely depends on feature extraction. Features reflecting bearing work states are directly extracted using time-frequency analysis of vibration signals, which leads to high dimensional feature data. To address the problem of feature dimension reduction, a compressive sensing-based feature extraction algorithm is developed to construct a concise fault feature set. Next, a heuristic PSO-BP neural network, whose learning process perfectly combines particle swarm optimization and the Levenberg-Marquardt algorithm, is constructed for fault classification. Numerical simulation experiments are conducted on four datasets sampled under different severity levels and load conditions, which verify that the proposed fault diagnosis method achieves efficient feature extraction and high classification accuracy.

  18. PV Systems Reliability Final Technical Report: Ground Fault Detection

    Energy Technology Data Exchange (ETDEWEB)

    Lavrova, Olga [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Flicker, Jack David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Johnson, Jay [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2016-01-01

    We have examined ground faults in PhotoVoltaic (PV) arrays and the efficacy of fuse, current detection (RCD), current sense monitoring/relays (CSM), isolation/insulation (Riso) monitoring, and Ground Fault Detection and Isolation (GFID) using simulations based on a Simulation Program with Integrated Circuit Emphasis SPICE ground fault circuit model, experimental ground faults installed on real arrays, and theoretical equations.

  19. Nonlinear Model-Based Fault Detection for a Hydraulic Actuator

    NARCIS (Netherlands)

    Van Eykeren, L.; Chu, Q.P.

    2011-01-01

    This paper presents a model-based fault detection algorithm for a specific fault scenario of the ADDSAFE project. The fault considered is the disconnection of a control surface from its hydraulic actuator. Detecting this type of fault as fast as possible helps to operate an aircraft more cost effect

  20. Fault Detection for Shipboard Monitoring and Decision Support Systems

    DEFF Research Database (Denmark)

    Lajic, Zoran; Nielsen, Ulrik Dam

    2009-01-01

    In this paper a basic idea of a fault-tolerant monitoring and decision support system will be explained. Fault detection is an important part of the fault-tolerant design for in-service monitoring and decision support systems for ships. In the paper, a virtual example of fault detection will be p...

  1. Wear fault diagnosis of an emulsion pump crank bearing

    Institute of Scientific and Technical Information of China (English)

    HAN Xiao-ming; DU Chang-long; ZHANG Yong-zhong; TIE Zhan-xu

    2008-01-01

    The total load on a crank bearing was calculated by performing a load analysis of the crank connecting rod mechanism.The Reynolds equation for hydrodynamic lubrication of the crank bearing was established at the Reynolds boundary condition and was then solved using the Holland method. From this the regular track of the bearing axis was obtained. As the crank bearing gradually wears the eccentricity ratio corresponding to the minimum oil film thickness increases gradually. The oil-bound film eventually breaks down, which allows friction and collision between the metal surfaces of the crank pin and the bearing. The rigid impact leads to excitation of high frequency vibrations at the natural frequencies of the connecting rod. The experiments show that the wear condition of the crank bearing can be identified correctly through the vibration signature at the natural frequencies of the connecting rod. The degree of wear can be predicted accurately through the energy content of the high frequency bands.

  2. All row, planar fault detection system

    Science.gov (United States)

    Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D; Smith, Brian Edward

    2013-07-23

    An apparatus, program product and method for detecting nodal faults may simultaneously cause designated nodes of a cell to communicate with all nodes adjacent to each of the designated nodes. Furthermore, all nodes along the axes of the designated nodes are made to communicate with their adjacent nodes, and the communications are analyzed to determine if a node or connection is faulty.

  3. Fundamental problems in fault detection and identification

    DEFF Research Database (Denmark)

    Saberi, Ali; Stoorvogel, Anton A.; Sannuti, Peddapullaiah;

    1999-01-01

    For certain fundamental problems in fault detection and identification, the necessary and sufficient conditions for their solvability are derived. These conditions are weaker than the ones found in the literature, since we do not assume any particular structure for the residual generator...

  4. A Fault Feature Extraction Method for Rolling Bearing Based on Pulse Adaptive Time-Frequency Transform

    Directory of Open Access Journals (Sweden)

    Jinbao Yao

    2016-01-01

    Full Text Available Shock pulse method is a widely used technique for condition monitoring of rolling bearing. However, it may cause erroneous diagnosis in the presence of strong background noise or other shock sources. Aiming at overcoming the shortcoming, a pulse adaptive time-frequency transform method is proposed to extract the fault features of the damaged rolling bearing. The method arranges the rolling bearing shock pulses extracted by shock pulse method in the order of time and takes the reciprocal of the time interval between the pulse at any moment and the other pulse as all instantaneous frequency components in the moment. And then it visually displays the changing rule of each instantaneous frequency after plane transformation of the instantaneous frequency components, realizes the time-frequency transform of shock pulse sequence through time-frequency domain amplitude relevancy processing, and highlights the fault feature frequencies by effective instantaneous frequency extraction, so as to extract the fault features of the damaged rolling bearing. The results of simulation and application show that the proposed method can suppress the noises well, highlight the fault feature frequencies, and avoid erroneous diagnosis, so it is an effective fault feature extraction method for the rolling bearing with high time-frequency resolution.

  5. Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings

    Science.gov (United States)

    Miao, Yonghao; Zhao, Ming; Lin, Jing; Lei, Yaguo

    2017-08-01

    The extraction of periodic impulses, which are the important indicators of rolling bearing faults, from vibration signals is considerably significance for fault diagnosis. Maximum correlated kurtosis deconvolution (MCKD) developed from minimum entropy deconvolution (MED) has been proven as an efficient tool for enhancing the periodic impulses in the diagnosis of rolling element bearings and gearboxes. However, challenges still exist when MCKD is applied to the bearings operating under harsh working conditions. The difficulties mainly come from the rigorous requires for the multi-input parameters and the complicated resampling process. To overcome these limitations, an improved MCKD (IMCKD) is presented in this paper. The new method estimates the iterative period by calculating the autocorrelation of the envelope signal rather than relies on the provided prior period. Moreover, the iterative period will gradually approach to the true fault period through updating the iterative period after every iterative step. Since IMCKD is unaffected by the impulse signals with the high kurtosis value, the new method selects the maximum kurtosis filtered signal as the final choice from all candidates in the assigned iterative counts. Compared with MCKD, IMCKD has three advantages. First, without considering prior period and the choice of the order of shift, IMCKD is more efficient and has higher robustness. Second, the resampling process is not necessary for IMCKD, which is greatly convenient for the subsequent frequency spectrum analysis and envelope spectrum analysis without resetting the sampling rate. Third, IMCKD has a significant performance advantage in diagnosing the bearing compound-fault which expands the application range. Finally, the effectiveness and superiority of IMCKD are validated by a number of simulated bearing fault signals and applying to compound faults and single fault diagnosis of a locomotive bearing.

  6. Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive

    Science.gov (United States)

    Li, Zipeng; Chen, Jinglong; Zi, Yanyang; Pan, Jun

    2017-02-01

    As one of most critical component of high-speed locomotive, wheel set bearing fault identification has attracted an increasing attention in recent years. However, non-stationary vibration signal with modulation phenomenon and heavy background noise make it difficult to excavate the hidden weak fault feature. Variational Mode Decomposition (VMD), which can decompose the non-stationary signal into couple Intrinsic Mode Functions adaptively and non-recursively, brings a feasible tool. However, heavy background noise seriously affects setting of mode number, which may lead to information loss or over decomposition problem. In this paper, an independence-oriented VMD method via correlation analysis is proposed to adaptively extract weak and compound fault feature of wheel set bearing. To overcome the information loss problem, the appropriate mode number is determined by the criterion of approximate complete reconstruction. Then the similar modes are combined according to the similarity of their envelopes to solve the over decomposition problem. Finally, three applications to wheel set bearing fault of high speed locomotive verify the effectiveness of the proposed method compared with original VMD, EMD and EEMD methods.

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

    Science.gov (United States)

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

    2016-09-01

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

  8. A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction

    Directory of Open Access Journals (Sweden)

    Yongjian Li

    2016-01-01

    Full Text Available Axle box bearings are the most critical mechanical components of railway vehicles. Condition monitoring is of great benefit to ensure the healthy status of bearings in the railway train. In this paper, a novel fault diagnosis model for axle box bearing based on symmetric alpha-stable distribution feature extraction and least squares support vector machines (LS-SVM using vibration signals is proposed which is conducted in three main steps. Firstly, fast nonlocal means is used for denoising and ensemble empirical mode decomposition is applied to extract fault feature information. Then a new statistical method of feature extraction, symmetric alpha-stable distribution, is employed to obtain representative features from intrinsic mode functions. Additionally, the hybrid fault feature sets are input into LS-SVM to identify the fault type. To enhance the performance of LS-SVM in the case of small-scale samples, Morlet wavelet kernel function is combined with LS-SVM for the classification of fault type and fault severity and the particle swarm optimization is used for the optimization of LS-WSVM parameters. Finally, the experimental results demonstrate that the proposed approach performs more effectively and robustly than the other methods in small-scale samples for fault detection and classification of railway vehicle bearings.

  9. PCA algorithm for detection, localisation and evolution of damages in gearbox bearings

    Energy Technology Data Exchange (ETDEWEB)

    Pirra, M; Gandino, E; Garibaldi, L; Machorro-Lopez, J M [Dipartimento di Meccanica, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino (Italy); Torri, A, E-mail: luigi.garibaldi@polito.it [Avio S.p.A., Strada del Drosso 145, 10135 Torino (Italy)

    2011-07-19

    A fundamental aspect when dealing with rolling element bearings, which often represent a key component in rotating machineries, consists in correctly identifying a degraded behaviour of a bearing with a reasonable level of confidence. This is one of the main requirements a health and usage monitoring system (HUMS) should have. This paper introduces a monitoring technique for the diagnosis of bearing faults based on Principal Component Analysis (PCA). This method overcomes the problem of acquiring data under different environmental conditions (hardly biasing the data) and allows accurate damage recognition, also assuring a rather low number of False Alarms (FA). In addition, a novel criterion is proposed in order to isolate the area in which the faulty bearing stands. Another useful feature of this PCA-based method concerns the capability to observe an increasing trend in the evolution of bearing degradation. The described technique is tested on an industrial rig (designed by Avio S.p.A.), consisting of a full size aeroengine gearbox. Healthy and variously damaged bearings, such as with an inner or rolling element fault, are set up and vibration signals are collected and processed in order to properly detect a fault. Finally, data collected from a test rig assembled by the Dynamics and Identification Research Group (DIRG) are used to demonstrate that the proposed method is able to correctly detect and to classify different levels of the same type of fault and also to localise it.

  10. Robust Fault Detection and Isolation for Stochastic Systems

    Science.gov (United States)

    George, Jemin; Gregory, Irene M.

    2010-01-01

    This paper outlines the formulation of a robust fault detection and isolation scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves precise reconstruction of sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the robust fault detection and isolation system.

  11. Phase Space Similarity as a Signature for Rolling Bearing Fault Diagnosis and Remaining Useful Life Estimation

    Directory of Open Access Journals (Sweden)

    Fang Liu

    2016-01-01

    Full Text Available Feature extraction from vibration signal is still a challenge in the area of fault diagnosis and remaining useful life (RUL estimation of rotary machine. In this paper, a novel feature called phase space similarity (PSS is introduced for health condition monitoring of bearings. Firstly, the acquired signal is transformed to the phase space through the phase space reconstruction (PSR. The similar vibration always exists in the phase space due to the comparable evolution of the dynamics that are characteristic of the system state. Secondly, the normalized cross-correlation (NCC is employed to calculate the PSS between bearing data with different states. Based on the PSS, a fault pattern recognition algorithm, a bearing fault size prediction algorithm, and a RUL estimation algorithm are introduced to analyze the experimental signal. Results have shown the effectiveness of the PSS as it can better grasp the nature and regularity of the signals.

  12. Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis

    Science.gov (United States)

    Gu, Xiaohui; Yang, Shaopu; Liu, Yongqiang; Hao, Rujiang

    2016-12-01

    Envelope analysis is one of the most useful methods in localized fault diagnosis of rolling element bearings. However, there is a challenge in selecting the optimal resonance band. In this paper, a novel method based on kurtogram and frequency domain correlated kurtosis is proposed. To obtain the correct relationship between the node and frequency band in wavelet packet transform, a vital process named frequency ordering is conducted to solve the frequency folding problem due to down sampling. Correlated kurtosis of envelope spectrum instead of correlated kurtosis of envelope signal or kurtosis of envelope spectrum is utilized to generate the kurtogram, in which the maximum value can indicate the optimal band for envelope analysis. Several cases of experimental bearing fault signals are used to evaluate the immunity of the proposed method to strong noise interference. The improved performance has also been compared with two previous developed methods. The results demonstrate the effectiveness and robustness of the method in fault diagnosis of rolling element bearings.

  13. A new rolling bearing fault diagnosis method based on GFT impulse component extraction

    Science.gov (United States)

    Ou, Lu; Yu, Dejie; Yang, Hanjian

    2016-12-01

    Periodic impulses are vital indicators of rolling bearing faults. The extraction of impulse components from rolling bearing vibration signals is of great importance for fault diagnosis. In this paper, vibration signals are taken as the path graph signals in a manifold perspective, and the Graph Fourier Transform (GFT) of vibration signals are investigated from the graph spectrum domain, which are both introduced into the vibration signal analysis. To extract the impulse components efficiently, a new adjacency weight matrix is defined, and then the GFT of the impulse component and harmonic component in the rolling bearing vibration signals are analyzed. Furthermore, as the GFT graph spectrum of the impulse component is mainly concentrated in the high-order region, a new rolling bearing fault diagnosis method based on GFT impulse component extraction is proposed. In the proposed method, the GFT of a vibration signal is firstly performed, and its graph spectrum coefficients in the high-order region are extracted to reconstruct different impulse components. Next, the Hilbert envelope spectra of these impulse components are calculated, and the envelope spectrum values at the fault characteristic frequency are arranged in order. Furthermore, the envelope spectrum with the maximum value at the fault characteristic frequency is selected as the final result, from which the rolling bearing fault can be diagnosed. Finally, an index KR, which is the product of the kurtosis and Hilbert envelope spectrum fault feature ratio of the extracted impulse component, is put forward to measure the performance of the proposed method. Simulations and experiments are utilized to demonstrate the feasibility and effectiveness of the proposed method.

  14. Research and Development of a Chaotic Signal Synchronization Error Dynamics-Based Ball Bearing Fault Diagnostor

    Directory of Open Access Journals (Sweden)

    Ying-Che Kuo

    2014-10-01

    Full Text Available This paper describes the fault diagnosis in the operation of industrial ball bearings. In order to cluster the very small differential signals of the four classic fault types of the ball bearing system, the chaos synchronization (CS concept is used in this study as the chaos system is very sensitive to a system’s variation such as initial conditions or system parameters. In this study, the Chen-Lee chaotic system was used to load the normal and fault signals of the bearings into the chaos synchronization error dynamics system. The fractal theory was applied to determine the fractal dimension and lacunarity from the CS error dynamics. Extenics theory was then applied to distinguish the state of the bearing faults. This study also compared the proposed method with discrete Fourier transform and wavelet packet analysis. According to the results, it is shown that the proposed chaos synchronization method combined with extenics theory can separate the characteristics (fractal dimension vs. lacunarity completely. Therefore, it has a better fault diagnosis rate than the two traditional signal processing methods, i.e., Fourier transform and wavelet packet analysis combined with extenics theory.

  15. Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

    Directory of Open Access Journals (Sweden)

    Xinghui Zhang

    2015-01-01

    Full Text Available Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.

  16. Experimental Determination of AH-64 Apache Tailshaft Hanger Bearing Vibration Characteristics with Seeded Faults

    Science.gov (United States)

    2009-06-01

    position, and it was attached by drilling and tapping the bearing hanger assembly to accept a mounting stud. This allows for collection of VMEP data as...discussed in great detail here. One of the first faults simulated in this study involved using a 1/32” end mill to drill into the ball track of...these fault modes, it is expected that spallation of the races is very unlikely due to the light loads seen by the bearings on the aircraft. Reduced

  17. Optimum IMFs Selection Based Envelope Analysis of Bearing Fault Diagnosis in Plunger Pump

    Directory of Open Access Journals (Sweden)

    Wenliao Du

    2016-01-01

    Full Text Available As the plunger pump always works in a complicated environment and the hydraulic cycle has an intrinsic fluid-structure interaction character, the fault information is submerged in the noise and the disturbance impact signals. For the fault diagnosis of the bearings in plunger pump, an optimum intrinsic mode functions (IMFs selection based envelope analysis was proposed. Firstly, the Wigner-Ville distribution was calculated for the acquired vibration signals, and the resonance frequency brought on by fault was obtained. Secondly, the empirical mode decomposition (EMD was employed for the vibration signal, and the optimum IMFs and the filter bandwidth were selected according to the Wigner-Ville distribution. Finally, the envelope analysis was utilized for the selected IMFs filtered by the band pass filter, and the fault type was recognized by compared with the bearing character frequencies. For the two modes, inner race fault and compound fault in the inner race and roller of rolling element bearing in plunger pump, the experiments show that a promising result is achieved.

  18. Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

    Directory of Open Access Journals (Sweden)

    Jie Tao

    2016-01-01

    Full Text Available In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN. By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods.

  19. A computer-vision-based rotating speed estimation method for motor bearing fault diagnosis

    Science.gov (United States)

    Wang, Xiaoxian; Guo, Jie; Lu, Siliang; Shen, Changqing; He, Qingbo

    2017-06-01

    Diagnosis of motor bearing faults under variable speed is a problem. In this study, a new computer-vision-based order tracking method is proposed to address this problem. First, a video recorded by a high-speed camera is analyzed with the speeded-up robust feature extraction and matching algorithm to obtain the instantaneous rotating speed (IRS) of the motor. Subsequently, an audio signal recorded by a microphone is equi-angle resampled for order tracking in accordance with the IRS curve, through which the frequency-domain signal is transferred to an angular-domain one. The envelope order spectrum is then calculated to determine the fault characteristic order, and finally the bearing fault pattern is determined. The effectiveness and robustness of the proposed method are verified with two brushless direct-current motor test rigs, in which two defective bearings and a healthy bearing are tested separately. This study provides a new noninvasive measurement approach that simultaneously avoids the installation of a tachometer and overcomes the disadvantages of tacholess order tracking methods for motor bearing fault diagnosis under variable speed.

  20. A Hybrid Approach for Fault Diagnosis of Railway Rolling Bearings Using STWD-EMD-GA-LSSVM

    Directory of Open Access Journals (Sweden)

    Dechen Yao

    2016-01-01

    Full Text Available Vibration signals resulting from railway rolling bearings are nonstationary by nature; this paper proposes a hybrid approach for the fault diagnosis of railway rolling bearings using segment threshold wavelet denoising (STWD, empirical mode decomposition (EMD, genetic algorithm (GA, and least squares support vector machine (LSSVM. The original signal is first denoised using STWD as a prefilter, which improves the subsequent decomposition into a number of intrinsic mode functions (IMFs using EMD. Secondly, the IMF energy-torques are extracted as feature parameters. Concurrently, a GA is employed to optimize the LSSVM to improve the classification accuracy. Finally, the extracted features are used as inputs for classification by the GA-LSSVM. Actual railway rolling bearing vibration signals are used to experimentally verify the effectiveness of the proposed method. The results show that the novel method is effective and accurate for fault diagnosis of railway rolling bearings.

  1. Frequency Based Fault Detection in Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2014-01-01

    In order to obtain lower cost of energy for wind turbines fault detection and accommodation is important. Expensive condition monitoring systems are often used to monitor the condition of rotating and vibrating system parts. One example is the gearbox in a wind turbine. This system is operated...... in parallel to the control system, using different computers and additional often expensive sensors. In this paper a simple filter based algorithm is proposed to detect changes in a resonance frequency in a system, exemplified with faults resulting in changes in the resonance frequency in the wind turbine...... gearbox. Only the generator speed measurement which is available in even simple wind turbine control systems is used as input. Consequently this proposed scheme does not need additional sensors and computers for monitoring the condition of the wind gearbox. The scheme is evaluated on a wide-spread wind...

  2. A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Xinghui Zhang

    2015-01-01

    Full Text Available A new improved Kurtogram was proposed in this paper. Instead of Kurtosis, correlated Kurtosis of envelope signal extracted from the wavelet packet node was used as an indicator to determine the optimal frequency band. Correlated Kurtosis helps to determine the fault related impulse signals not affected by other unrelated signal components. Finally, two simulated and three experimental bearing fault cases are used to validate the effectiveness of proposed method and to compare with other similar methods. The results demonstrate it can locate resonant frequency band with a high reliability than two previous developed methods by Lei et al. and Wang et al. especially for the incipient faults under low load.

  3. Microstructural evidence for seismic and aseismic slips along clay-bearing, carbonate faults

    Science.gov (United States)

    Smeraglia, Luca; Bettucci, Andrea; Billi, Andrea; Carminati, Eugenio; Cavallo, Andrea; Di Toro, Giulio; Natali, Marco; Passeri, Daniele; Rossi, Marco; Spagnuolo, Elena

    2017-05-01

    In this multimethodological study, microstructural observations of fault rocks are combined with micromechanical property analyses (contact resonance atomic force microscopy (CR-AFM)) and with rotary friction experiments (Slow- to High-Velocity rotary-shear friction Apparatus apparatus) to find evidence of seismic to aseismic slip and understand the nanoscale rheology of clay-bearing, carbonate-hosted faults. Fluidized structures, truncated clasts, pores and vesicles, and phyllosilicate nanosized spherules and tubes suggest fast deformation events occurred during seismic slip, whereas clay-assisted pressure-solution processes, clumped clasts, foliation surfaces, and mantled clasts indicate slow deformation events occurred during postseismic/interseismic periods. CR-AFM measurements show that the occurrence of 5 wt % of clay within the carbonate-hosted gouges can significantly reduce the fault core stiffness at nanoscale. In addition, during high-velocity friction experiments simulating seismic slip conditions, the presence of ultrathin phyllosilicate-bearing (≤3 wt %) layers within calcite gouges, as those observed in the natural fault, show faster dynamic weakening than that of pure calcite gouges. The weak behavior of such layers could facilitate the upward propagation of seismic slip during earthquakes, thus possibly enhancing surface faulting. Microstructural observations and experimental evidence fit some well-known geophysical and geodetic observations on the short- to long-term mechanical behavior of faults such as postseismic/interseismic aseismic creep, interseismic fault locking, and seismic slip propagation up to the Earth's surface.

  4. A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

    Science.gov (United States)

    Grasso, M.; Chatterton, S.; Pennacchi, P.; Colosimo, B. M.

    2016-12-01

    Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time-frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time-frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.

  5. Model Based Fault Detection in a Centrifugal Pump Application

    DEFF Research Database (Denmark)

    Kallesøe, Carsten; Cocquempot, Vincent; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    A model based approach for fault detection in a centrifugal pump, driven by an induction motor, is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, observer design and Analytical Redundancy Relation (ARR) design. Structural consideration...... is capable of detecting four different faults in the mechanical and hydraulic parts of the pump.......A model based approach for fault detection in a centrifugal pump, driven by an induction motor, is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, observer design and Analytical Redundancy Relation (ARR) design. Structural considerations...

  6. An Immunology-inspired Fault Detection and Identification System

    Directory of Open Access Journals (Sweden)

    Liguo Weng

    2012-09-01

    Full Text Available This paper presents a fault detection and identification (FDI approach inspired by the immune system. The salient features of the immune system, such as adaptability, robustness, flexibility, archival memory and distributed cognition abilities, have been the valuable source of inspiration for fundamentally new methods for fault detection and identification. This research makes use of immunological concepts to develop a robust fault detection and identification mechanism, capable of detecting and classifying diverse system faults dynamically. Such an FDI mechanism also has the ability to learn and classify overlapping faults using distributed sensing. Moreover, its detection accuracy can be continuously improved during system operation. As tested by numerical simulations in which faults are represented by overlapping banana functions, the proposed algorithms are adaptive to new types of faults and overlapping faults.

  7. Health Assessment and Fault Classification of Roller Element Bearings

    Science.gov (United States)

    2012-07-01

    and is outfitted with mounting holes for accelerometers in positions of interest. As can be seen in figure 1, the rig is a complete drivetrain ...this study, the belts, gearbox, and magnetic load were removed from the drivetrain to reduce the noise from vibrations not related to the bearings

  8. An effort allocation model considering different budgetary constraint on fault detection process and fault correction process

    Directory of Open Access Journals (Sweden)

    Vijay Kumar

    2016-01-01

    Full Text Available Fault detection process (FDP and Fault correction process (FCP are important phases of software development life cycle (SDLC. It is essential for software to undergo a testing phase, during which faults are detected and corrected. The main goal of this article is to allocate the testing resources in an optimal manner to minimize the cost during testing phase using FDP and FCP under dynamic environment. In this paper, we first assume there is a time lag between fault detection and fault correction. Thus, removal of a fault is performed after a fault is detected. In addition, detection process and correction process are taken to be independent simultaneous activities with different budgetary constraints. A structured optimal policy based on optimal control theory is proposed for software managers to optimize the allocation of the limited resources with the reliability criteria. Furthermore, release policy for the proposed model is also discussed. Numerical example is given in support of the theoretical results.

  9. Weak fault detection and health degradation monitoring using customized standard multiwavelets

    Science.gov (United States)

    Yuan, Jing; Wang, Yu; Peng, Yizhen; Wei, Chenjun

    2017-09-01

    the inner race of a locomotive bearing, effectively detect and locate the potential failure from a complicated epicyclic gear train and successfully reveal the fault development and performance degradation of a test bearing in the lifetime.

  10. A Method for Incipient Fault Diagnosis of Roller Bearings Based on the Wavelet Transform Correlation Filter and Hilbert Transform

    Institute of Scientific and Technical Information of China (English)

    ZENG Qing-hu; QIU Jing; LIU Guan-jun

    2007-01-01

    Noise is the biggest obstacle that makes the incipient fault diagnosis results of roller bearings uncorrected; a new method for diagnosing incipient fault of roller bearings based on the Wavelet Transform Correlation Filter and Hilbert Transform was proposed. First, the weak fault information features are picked up from the roller bearings fault vibration signals by use of a de-noising characteristic of the Wavelet Transform Correlation Filter as the preprocessing of the Hilbert Envelope Analysis. Then, in order to get fault features frequency, de-noised wavelet coefficients of high scales which represent high frequency signal were analyzed by Hilbert Envelope Spectrum Analysis. The simulation signals and diagnosing examples analysis results reveal that the proposed method is more effective than the method of direct wavelet coefficients-Hilbert Transform in de-noising and clarifying roller bearing incipient fault.

  11. A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine

    Directory of Open Access Journals (Sweden)

    Xiao-hui He

    2016-01-01

    Full Text Available To realize the fault diagnosis of bearing effectively, this paper presents a novel bearing fault diagnosis method based on Gaussian restricted Boltzmann machine (Gaussian RBM. Vibration signals are firstly resampled to the same equivalent speed. Subsequently, the envelope spectrums of the resampled data are used directly as the feature vectors to represent the fault types of bearing. Finally, in order to deal with the high-dimensional feature vectors based on envelope spectrum, a classifier model based on Gaussian RBM is applied. Gaussian RBM has the ability to provide a closed-form representation of the distribution underlying the training data, and it is very convenient for modeling high-dimensional real-valued data. Experiments on 10 different data sets verify the performance of the proposed method. The superiority of Gaussian RBM classifier is also confirmed by comparing with other classifiers, such as extreme learning machine, support vector machine, and deep belief network. The robustness of the proposed method is also studied in this paper. It can be concluded that the proposed method can realize the bearing fault diagnosis accurately and effectively.

  12. Fast-moving dislocations trigger flash weakening in carbonate-bearing faults during earthquakes

    NARCIS (Netherlands)

    Spagnuolo, Elena; Plümper, Oliver; Violay, Marie; Cavallo, Andrea; Di Toro, Giulio

    2015-01-01

    Rupture fronts can cause fault displacement, reaching speeds up to several ms-1 within a few milliseconds, at any distance away from the earthquake nucleation area. In the case of silicate-bearing rocks the abrupt slip acceleration results in melting at asperity contacts causing a large reduction in

  13. A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

    Directory of Open Access Journals (Sweden)

    HungLinh Ao

    2014-01-01

    Full Text Available This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs. Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

  14. A novel identification method of Volterra series in rotor-bearing system for fault diagnosis

    Science.gov (United States)

    Xia, Xin; Zhou, Jianzhong; Xiao, Jian; Xiao, Han

    2016-01-01

    Volterra series is widely employed in the fault diagnosis of rotor-bearing system to prevent dangerous accidents and improve economic efficiency. The identification of the Volterra series involves the infinite-solution problems which is caused by the periodic characteristic of the excitation signal of rotor-bearing system. But this problem has not been considered in the current identification methods of the Volterra series. In this paper, a key kernels-PSO (KK-PSO) method is proposed for Volterra series identification. Instead of identifying the Volterra series directly, the key kernels of Volterra are found out to simply the Volterra model firstly. Then, the Volterra series with the simplest formation is identified by the PSO method. Next, simulation verification is utilized to verify the feasibility and effectiveness of the KK-PSO method by comparison to the least square (LS) method and traditional PSO method. Finally, experimental tests have been done to get the Volterra series of a rotor-bearing test rig in different states, and a fault diagnosis system is built with a neural network to classify different fault conditions by the kernels of the Volterra series. The analysis results indicate that the KK-PSO method performs good capability on the identification of Volterra series of rotor-bearing system, and the proposed method can further improve the accuracy of fault diagnosis.

  15. Bearing Damage Detection of BLDC Motors Based on Current Envelope Analysis

    Science.gov (United States)

    Lee, Chun-Yao; Hsieh, Yu-Hua

    2012-12-01

    This paper proposes current envelope analysis (CEA) to analyze bearing fault signals in brushless direct current (BLDC) motors, and back propagation neural networks (BPNN) to automatically identify bearing faults. We made sample motors which contained different types of fault, recorded the current signals, and extracted the current features using CEA and Hilbert Huang transform (HHT) for BPNN fault identification. The results indicate that this approach can efficiently identify bearing faults in BLDC motors.

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

    Directory of Open Access Journals (Sweden)

    Gang Tang

    2016-06-01

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

  17. Rolling Bearing Fault Detection Based on Complex Wavelet Transform and Singlar Value Ratio Spectrum%基于复小波和奇异值比谱的轴承故障检测方法

    Institute of Scientific and Technical Information of China (English)

    侯者非; 杨杰; 张雪

    2011-01-01

    A novel signal processing algorithm was proposed here for vibration signal analysis in condition monitoring and health diagnosis of rolling bearings.Such technique required an envelope being extracted from the vibration signal with complex Morlet wavelet transform (MWT).The principal periodic component in the envelope was subsequently detected, enhanced and reconstructed automatically with sweep frequency method based on finding the peak value of singular value ratio (SVR) spectrum.Such signal processing approach was experimentally evaluated by using vibration signals measured on rolling element bearings that contained localized structural defects with proved validity and efficiency.%提出一种基于包络分析和奇异值比谱的滚动轴承振动故障监测和诊断方法.首先利用具有解析带通特性的复Morlet小波来获得信号的包络,然后采用扫频方式检测奇异值比谱最大峰值自动提取、增强、重构包络信号中的主周期分量,提取到轴承故障特征.该方法已成功地应用到了对滚动轴承故障检测实验,验证了该方法的有效性、可行性.

  18. Ball bearing defect models: A study of simulated and experimental fault signatures

    Science.gov (United States)

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

    2017-07-01

    Numerical model based virtual prototype of a system can serve as a tool to generate huge amount of data which replace the dependence on expensive and often difficult to conduct experiments. However, the model must be accurate enough to substitute the experiments. The abstraction level and details considered during model development depend on the purpose for which simulated data should be generated. This article concerns development of simulation models for deep groove ball bearings which are used in a variety of rotating machinery. The purpose of the model is to generate vibration signatures which usually contain features of bearing defects. Three different models with increasing level-of-complexity are considered: a bearing kinematics based planar motion block diagram model developed in MATLAB Simulink which does not explicitly consider cage and traction dynamics, a planar motion model with cage, traction and contact dynamics developed using multi-energy domain bond graph formalism in SYMBOLS software, and a detailed spatial multi-body dynamics model with complex contact and traction mechanics developed using ADAMS software. Experiments are conducted using Spectra Quest machine fault simulator with different prefabricated faulted bearings. The frequency domain characteristics of simulated and experimental vibration signals for different bearing faults are compared and conclusions are drawn regarding usefulness of the developed models.

  19. Research on the fault diagnosis of bearing based on wavelet and demodulation

    Science.gov (United States)

    Li, Jiapeng; Yuan, Yu

    2017-05-01

    As a most commonly-used machine part, antifriction bearing is extensively used in mechanical equipment. Vibration signal analysis is one of the methods to monitor and diagnose the running status of antifriction bearings. Therefore, using wavelet analysis for demising is of great importance in the engineering practice. This paper firstly presented the basic theory of wavelet analysis to study the transformation, decomposition and reconstruction of wavelet. In addition, edition software LabVIEW was adopted to conduct wavelet and demodulation upon the vibration signal of antifriction bearing collected. With the combination of Hilbert envelop demodulation analysis, the fault character frequencies of the demised signal were extracted to conduct fault diagnosis analysis, which serves as a reference for the wavelet and demodulation of the vibration signal in engineering practice.

  20. FUZZY FAULT DETECTION FOR PERMANENT MAGNET SYNCHRONOUS GENERATOR

    Directory of Open Access Journals (Sweden)

    N. Selvaganesan

    2011-07-01

    Full Text Available Faults in engineering systems are difficult to avoid and may result in serious consequences. Effective fault detection and diagnosis can improve system reliability and avoid expensive maintenance. In this paper fuzzy system based fault detection scheme for permanent magnet synchronous generator is proposed. The sequence current components like positive and negative sequence currents are used as fault indicators and given as inputs to fuzzy fault detector. Also, the fuzzy inference system is created and rule base is evaluated, relating the sequence current component to the type of faults. These rules are fired for specific changes in sequence current component and the faults are detected. The feasibility of the proposed scheme for permanent magnet synchronous generator is demonstrated for different types of fault under various operating conditions using MATLAB/Simulink.

  1. Fault Detection and Load Distribution for the Wind Farm Challenge

    DEFF Research Database (Denmark)

    Borchersen, Anders Bech; Larsen, Jesper Abildgaard; Stoustrup, Jakob

    2014-01-01

    is to detect and handle different faults occurring in the individual turbines on farm level. The fault detection system is designed such that it takes advantage of the fact that within a wind farm several of the turbines will be operating under similar conditions. To enable this the turbines are grouped......In this paper a fault detection system and a fault tolerant controller for a wind farm model is designed and tested. The wind farm model is taken from the wind farm challenge which is a public available challenge where a wind farm consisting of nine turbines is proposed. The goal of the challenge...... in the model. All the detections are not within the requirement of the challenge thus room for improvement. To take advantage of the fault detection system a fault tolerant controller for the wind farm has been designed. The fault tolerant controller is a dispatch controller which is estimating the possible...

  2. On the frictional (in) stability of clay-bearing faults

    Science.gov (United States)

    Violay, M.; Orellana, F.; Scuderi, M. M.; Collettini, C.

    2016-12-01

    Opalinus clay (OPA) is shale rock studied under the context of deep geological disposal by The Mont Terri Laboratory research program in Switzerland. Despite its favorable hydro-mechanical properties, the presence of a large tectonic fault system intersecting the rock formation arises questions over the long-term safety performance of a nuclear waste repository, in terms of possible leakages and the possibility of earthquakes triggered by fault instability. To study the frictional stability of OPA, we have performed velocity steps (1-300 μm/s) and slide-hold-slide tests (1-10000 s) on simulated gouge and intact samples - sheared parallel and perpendicular to foliation - at different normal stresses (4 - 30 MPa). To understand the deformation mechanisms, we have analyzed the microstructures of the sheared samples trough optical and SE microscopy. Results reported peak and steady state friction values ranging from 0.21 to 0.52 and from 0.14 to 0.39 respectively. Consistently, samples with well-developed layering showed lower friction values than gouge samples even though they have the same mineralogical composition. At all normal stresses, velocity dependence tests on gouge showed a velocity strengthening regime, whereas, intact samples developed both velocity-strengthening and velocity-weakening regimes. Finally, we have recorded near zero healing values for both intact and powdered samples at different normal stress. However, a complex evolution from negative to positive frictional healing rate, with an inflexion holding time of 300 s, has been observed. In conclusion, our data suggests that both the velocity strengthening regime and the near zero healing for the simulated gouge, are consistent with aseismic creep. We have also reported the possibility of unstable sliding outside the fault core accompanied by low capacity of contact regeneration, and low capacity to sustain future stress drops compared to evidence showed by experiments on simulated gouge. Moreover

  3. Fault Analysis and Detection in Microgrids with High PV Penetration

    Energy Technology Data Exchange (ETDEWEB)

    El Khatib, Mohamed [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hernandez Alvidrez, Javier [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ellis, Abraham [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-05-01

    In this report we focus on analyzing current-controlled PV inverters behaviour under faults in order to develop fault detection schemes for microgrids with high PV penetration. Inverter model suitable for steady state fault studies is presented and the impact of PV inverters on two protection elements is analyzed. The studied protection elements are superimposed quantities based directional element and negative sequence directional element. Additionally, several non-overcurrent fault detection schemes are discussed in this report for microgrids with high PV penetration. A detailed time-domain simulation study is presented to assess the performance of the presented fault detection schemes under different microgrid modes of operation.

  4. IMU Fault Detection Based on 2-CUSUM

    Directory of Open Access Journals (Sweden)

    Élcio Jeronimo de Oliveira

    2012-01-01

    IMU strapdown platforms using fiber optic gyros (FOG or micro electro mechanical systems (MEMSs. A way to solve this problem makes use of sensor redundancy and parity vector (PV analysis. However, the actual sensor outputs can include some anomalies, as impulsive noise which can be associated with the sensors itself or data acquisition process, committing the elementary threshold criteria as commonly used. Therefore, to overcome this problem, in this work, it is proposed an algorithm based on median filter (MF for prefiltering and chi-square cumulative sum (2-CUSUM only for fault detection (FD applied to an IMU composed by four FOGs.

  5. Fault Detection for Diesel Engine Actuator

    DEFF Research Database (Denmark)

    Blanke, M.; Bøgh, S.A.; Jørgensen, R.B.

    1994-01-01

    Feedback control systems are vulnerable to faults in control loop sensors and actuators, because feedback actions may cause abrupt responses and process damage when faults occur.......Feedback control systems are vulnerable to faults in control loop sensors and actuators, because feedback actions may cause abrupt responses and process damage when faults occur....

  6. High Speed Operation and Testing of a Fault Tolerant Magnetic Bearing

    Science.gov (United States)

    DeWitt, Kenneth; Clark, Daniel

    2004-01-01

    Research activities undertaken to upgrade the fault-tolerant facility, continue testing high-speed fault-tolerant operation, and assist in the commission of the high temperature (1000 degrees F) thrust magnetic bearing as described. The fault-tolerant magnetic bearing test facility was upgraded to operate to 40,000 RPM. The necessary upgrades included new state-of-the art position sensors with high frequency modulation and new power edge filtering of amplifier outputs. A comparison study of the new sensors and the previous system was done as well as a noise assessment of the sensor-to-controller signals. Also a comparison study of power edge filtering for amplifier-to-actuator signals was done; this information is valuable for all position sensing and motor actuation applications. After these facility upgrades were completed, the rig is believed to have capabilities for 40,000 RPM operation, though this has yet to be demonstrated. Other upgrades included verification and upgrading of safety shielding, and upgrading control algorithms. The rig will now also be used to demonstrate motoring capabilities and control algorithms are in the process of being created. Recently an extreme temperature thrust magnetic bearing was designed from the ground up. The thrust bearing was designed to fit within the existing high temperature facility. The retrofit began near the end of the summer, 04, and continues currently. Contract staff authored a NASA-TM entitled "An Overview of Magnetic Bearing Technology for Gas Turbine Engines", containing a compilation of bearing data as it pertains to operation in the regime of the gas turbine engine and a presentation of how magnetic bearings can become a viable candidate for use in future engine technology.

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

    Science.gov (United States)

    Liu, Hongmei; Wang, Xuan; Lu, Chen

    2015-08-01

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

  8. Optimal Robust Fault Detection for Linear Discrete Time Systems

    Directory of Open Access Journals (Sweden)

    Nike Liu

    2008-01-01

    Full Text Available This paper considers robust fault-detection problems for linear discrete time systems. It is shown that the optimal robust detection filters for several well-recognized robust fault-detection problems, such as ℋ−/ℋ∞, ℋ2/ℋ∞, and ℋ∞/ℋ∞ problems, are the same and can be obtained by solving a standard algebraic Riccati equation. Optimal filters are also derived for many other optimization criteria and it is shown that some well-studied and seeming-sensible optimization criteria for fault-detection filter design could lead to (optimal but useless fault-detection filters.

  9. Experimental Fault Detection and Accomodation for an Agricultural Mobile Robot

    DEFF Research Database (Denmark)

    Østergaard, Kasper Zinck; Vinther, D.; Bisgaard, Morten;

    2005-01-01

    This paper presents a systematic procedure to achieve fault tolerant capability for a four-wheel driven, four-wheel steered mobile robot moving in outdoor terrain. The procedure is exemplified through the paper by applying on a compass module. Detailed methods for fault detection and fault...

  10. Fault Detection of Wind Turbines with Uncertain Parameters

    DEFF Research Database (Denmark)

    Tabatabaeipour, Seyed Mojtaba; Odgaard, Peter Fogh; Bak, Thomas;

    2012-01-01

    In this paper a set-membership approach for fault detection of a benchmark wind turbine is proposed. The benchmark represents relevant fault scenarios in the control system, including sensor, actuator and system faults. In addition we also consider parameter uncertainties and uncertainties on the...

  11. Double-dictionary matching pursuit for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity

    Science.gov (United States)

    Cui, Lingli; Gong, Xiangyang; Zhang, Jianyu; Wang, Huaqing

    2016-12-01

    The quantitative diagnosis of rolling bearing fault severity is particularly crucial to realize a proper maintenance decision. Aiming at the fault feature of rolling bearing, a novel double-dictionary matching pursuit (DDMP) for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity (LZC) index is proposed in this paper. In order to match the features of rolling bearing fault, the impulse time-frequency dictionary and modulation dictionary are constructed to form the double-dictionary by using the method of parameterized function model. Then a novel matching pursuit method is proposed based on the new double-dictionary. For rolling bearing vibration signals with different fault sizes, the signals are decomposed and reconstructed by the DDMP. After the noise reduced and signals reconstructed, the LZC index is introduced to realize the fault extent evaluation. The applications of this method to the fault experimental signals of bearing outer race and inner race with different degree of injury have shown that the proposed method can effectively realize the fault extent evaluation.

  12. Pseudotachylite Bearing Cretaceous Fault in the Saddlebag Lake Pendant, Central Sierra Nevada, CA

    Science.gov (United States)

    Whitesides, A. S.; Cao, W.; Paterson, S. R.

    2010-12-01

    Over the past several years the undergraduate researchers and mentors in the University of Southern California’s Undergraduate Team Research program has mapped the northern continuation of the Gem Lake shear zone from Gem Lake to Virginia Canyon near the north end of the Saddlebag pendant. In the center of this dominantly dextral, ductile shear zone we now recognize a pseudotachylite bearing brittle fault that often juxtaposes Triassic metavolcanics to the east of the fault with a Jurassic metasedimentary package to the west of the fault. Kinematic indicators such as slickenlines, steps, and offset dikes found within the brittle fault zone also suggest dextral oblique motion, similar to the motion of the ductile shear zone. The brittle fault dips steeply and strikes N-NW with the fault zone width varying from narrow (sub m scale) to a 100-200 m wide fracture zone as seen in the Sawmill area. Jurrasic metasediments (> 177Ma) and Cretaceous metavolcanics (110-95Ma) lie to the West of the fault and Triassic metavolcanics (219Ma) lie to the East of the fault in the Virginia Canyon, Saddlebag Lake, and Sawmill areas. The absence of ~45 million years of Jurassic metavolcanics along the contact of the fault in each area, suggests tectonic removal of the sequence. Pseudotachylite, quartz vein rich breccias, gouge, fault scarps, and truncated Cathedral Peak dikes (~88 Ma) originating from the Tuolumne Batholith (TB), are common features associated with the brittle fault. The truncated, 88 Ma Cathedral Peak dikes plus nearby biotite cooling ages of 82 Ma indicate that displacement on the brittle fault continued well after TB emplacement and cooling and likely continued after ~80 Ma. The pseudotachylite suggests earthquakes occurred on the brittle fault during the Cretaceous. Movement also occurred along the fault at fairly shallow depths as indicated by the presence of vugs, or cavities with free euhedral crystal growth, within the quartz vein breccias. In the Sawmill

  13. Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

    Directory of Open Access Journals (Sweden)

    Songrong Luo

    2015-01-01

    Full Text Available Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs by using local characteristic-scale decomposition (LCD method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM classifier based on Chemical Reaction Optimization (CRO algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.

  14. GNAR-GARCH model and its application in feature extraction for rolling bearing fault diagnosis

    Science.gov (United States)

    Ma, Jiaxin; Xu, Feiyun; Huang, Kai; Huang, Ren

    2017-09-01

    Given its simplicity of modeling and sensitivity to condition variations, time series model is widely used in feature extraction to realize fault classification and diagnosis. However, nonlinear and nonstationary characteristics common in fault signals of rolling bearing bring challenges to the diagnosis. In this paper, a hybrid model, the combination of a general expression for linear and nonlinear autoregressive (GNAR) model and a generalized autoregressive conditional heteroscedasticity (GARCH) model, (i.e., GNAR-GARCH), is proposed and applied to rolling bearing fault diagnosis. An exact expression of GNAR-GARCH model is given. Maximum likelihood method is used for parameter estimation and modified Akaike Information Criterion is adopted for structure identification of GNAR-GARCH model. The main advantage of this novel model over other models is that the combination makes the model suitable for nonlinear and nonstationary signals. It is verified with statistical tests that contain comparisons among the different time series models. Finally, GNAR-GARCH model is applied to fault diagnosis by modeling mechanical vibration signals including simulation and real data. With the parameters estimated and taken as feature vectors, k-nearest neighbor algorithm is utilized to realize the classification of fault status. The results show that GNAR-GARCH model exhibits higher accuracy and better performance than do other models.

  15. Fault Detection based on MCSA for a 400Hz Asynchronous Motor for Airborne Applications

    Directory of Open Access Journals (Sweden)

    Steffen Haus

    2013-01-01

    Full Text Available Future health monitoring concepts in different fields of engineering require reliable fault detection to avoid unscheduled machine downtime. Diagnosis of electrical induction machines for industrial applications is widely discussed in literature. In aviation industry, this topic is still only rarely discussed.A common approach to health monitoring for electrical induction machines is to use Motor Current Signature Analysis (MCSA based on a Fast Fourier Transform (FFT. Research results on this topic are available for comparatively large motors, where the power supply is typically based on 50Hz alternating current, which is the general power supply frequency for industrial applications.In this paper, transferability to airborne applications, where the power supply is 400Hz, is assessed. Three phase asynchronous motors are used to analyse detectability of different motor faults. The possibility to transfer fault detection results from 50Hz to 400Hz induction machines is the main question answered in this research work. 400Hz power supply frequency requires adjusted motor design, causing increased motor speed compared to 50Hz supply frequency. The motor used for experiments in this work is a 800W motor with 200V phase to phase power supply, powering an avionic fan. The fault cases to be examined are a bearing fault, a rotor unbalance, a stator winding fault, a broken rotor bar and a static air gap eccentricity. These are the most common faults in electrical induction machines which can cause machine downtime. The focus of the research work is the feasibility of the application of MCSA for small scale, high speed motor design, using the Fourier spectra of the current signal.Detectability is given for all but the bearing fault, although rotor unbalance can only be detected in case of severe damage level. Results obtained in the experiments are interpreted with respect to the motor design. Physical interpretation are given in case the results differ

  16. High Temperature, Permanent Magnet Biased, Fault Tolerant, Homopolar Magnetic Bearing Development

    Science.gov (United States)

    Palazzolo, Alan; Tucker, Randall; Kenny, Andrew; Kang, Kyung-Dae; Ghandi, Varun; Liu, Jinfang; Choi, Heeju; Provenza, Andrew

    2008-01-01

    This paper summarizes the development of a magnetic bearing designed to operate at 1,000 F. A novel feature of this high temperature magnetic bearing is its homopolar construction which incorporates state of the art high temperature, 1,000 F, permanent magnets. A second feature is its fault tolerance capability which provides the desired control forces with over one-half of the coils failed. The construction and design methodology of the bearing is outlined and test results are shown. The agreement between a 3D finite element, magnetic field based prediction for force is shown to be in good agreement with predictions at room and high temperature. A 5 axis test rig will be complete soon to provide a means to test the magnetic bearings at high temperature and speed.

  17. On Identifiability of Bias-Type Actuator-Sensor Faults in Multiple-Model-Based Fault Detection and Identification

    Science.gov (United States)

    Joshi, Suresh M.

    2012-01-01

    This paper explores a class of multiple-model-based fault detection and identification (FDI) methods for bias-type faults in actuators and sensors. These methods employ banks of Kalman-Bucy filters to detect the faults, determine the fault pattern, and estimate the fault values, wherein each Kalman-Bucy filter is tuned to a different failure pattern. Necessary and sufficient conditions are presented for identifiability of actuator faults, sensor faults, and simultaneous actuator and sensor faults. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have biases.

  18. Fault detection for discrete-time switched systems with sensor stuck faults and servo inputs.

    Science.gov (United States)

    Zhong, Guang-Xin; Yang, Guang-Hong

    2015-09-01

    This paper addresses the fault detection problem of switched systems with servo inputs and sensor stuck faults. The attention is focused on designing a switching law and its associated fault detection filters (FDFs). The proposed switching law uses only the current states of FDFs, which guarantees the residuals are sensitive to the servo inputs with known frequency ranges in faulty cases and robust against them in fault-free case. Thus, the arbitrarily small sensor stuck faults, including outage faults can be detected in finite-frequency domain. The levels of sensitivity and robustness are measured in terms of the finite-frequency H- index and l2-gain. Finally, the switching law and FDFs are obtained by the solution of a convex optimization problem.

  19. Time-frequency vibration analysis for the detection of motor damages caused by bearing currents

    Science.gov (United States)

    Prudhom, Aurelien; Antonino-Daviu, Jose; Razik, Hubert; Climente-Alarcon, Vicente

    2017-02-01

    Motor failure due to bearing currents is an issue that has drawn an increasing industrial interest over recent years. Bearing currents usually appear in motors operated by variable frequency drives (VFD); these drives may lead to common voltage modes which cause currents induced in the motor shaft that are discharged through the bearings. The presence of these currents may lead to the motor bearing failure only few months after system startup. Vibration monitoring is one of the most common ways for detecting bearing damages caused by circulating currents; the evaluation of the amplitudes of well-known characteristic components in the vibration Fourier spectrum that are associated with race, ball or cage defects enables to evaluate the bearing condition and, hence, to identify an eventual damage due to bearing currents. However, the inherent constraints of the Fourier transform may complicate the detection of the progressive bearing degradation; for instance, in some cases, other frequency components may mask or be confused with bearing defect-related while, in other cases, the analysis may not be suitable due to the eventual non-stationary nature of the captured vibration signals. Moreover, the fact that this analysis implies to lose the time-dimension limits the amount of information obtained from this technique. This work proposes the use of time-frequency (T-F) transforms to analyse vibration data in motors affected by bearing currents. The experimental results obtained in real machines show that the vibration analysis via T-F tools may provide significant advantages for the detection of bearing current damages; among other, these techniques enable to visualise the progressive degradation of the bearing while providing an effective discrimination versus other components that are not related with the fault. Moreover, their application is valid regardless of the operation regime of the machine. Both factors confirm the robustness and reliability of these tools

  20. Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy

    Directory of Open Access Journals (Sweden)

    Long Han

    2015-09-01

    Full Text Available The randomness and fuzziness that exist in rolling bearings when faults occur result in uncertainty in acquisition signals and reduce the accuracy of signal feature extraction. To solve this problem, this study proposes a new method in which cloud model characteristic entropy (CMCE is set as the signal characteristic eigenvalue. This approach can overcome the disadvantages of traditional entropy complexity in parameter selection when solving uncertainty problems. First, the acoustic emission signals under normal and damage rolling bearing states collected from the experiments are decomposed via ensemble empirical mode decomposition. The mutual information method is then used to select the sensitive intrinsic mode functions that can reflect signal characteristics to reconstruct the signal and eliminate noise interference. Subsequently, CMCE is set as the eigenvalue of the reconstructed signal. Finally, through the comparison of experiments between sample entropy, root mean square and CMCE, the results show that CMCE can better represent the characteristic information of the fault signal.

  1. Fuzzy associative memories for instrument fault detection

    Energy Technology Data Exchange (ETDEWEB)

    Heger, A.S. [New Mexico Univ., Albuquerque, NM (United States). Dept. of Chemical and Nuclear Engineering; Holbert, K.E.; Ishaque, A.M. [Arizona State Univ., Tempe, AZ (United States)

    1996-06-01

    A fuzzy logic instrument fault detection scheme is developed for systems having two or three redundant sensors. In the fuzzy logic approach the deviation between each signal pairing is computed and classified into three fuzzy sets. A rule base is created allowing the human perception of the situation to be represented mathematically. Fuzzy associative memories are then applied. Finally, a defuzzification scheme is used to find the centroid location, and hence the signal status. Real-time analyses are carried out to evaluate the instantaneous signal status as well as the long-term results for the sensor set. Instantaneous signal validation results are used to compute a best estimate for the measured state variable. The long-term sensor validation method uses a frequency fuzzy variable to determine the signal condition over a specific period. To corroborate the methodology synthetic data representing various anomalies are analyzed with both the fuzzy logic technique and the parity space approach. (Author).

  2. Fault Detection Observer Design for LSFDJ: A Factorization Approach

    Institute of Scientific and Technical Information of China (English)

    YANG Xiao-jun; WENG Zheng-xin; TIAN Zuo-hua

    2005-01-01

    Based on a new special co-inner-outer factorization, a factorization approach for design fault detection observer for LSFDJ was proposed. It is a simple state-space method and can deal with time-varying LSFDJ with sensor noise and sensor faults. The performance of the fault detection observer is optimized in an H∞ setting,where the ratio between the gains from disturbance and fault to residual respectively is minimized. The design parameters of the detection observer were given in terms of the solution to the Riccati differential equation with jumps.

  3. Model Based Incipient Fault Detection for Gear Drives

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper presents the method of model based incipient fault detection for gear drives,this method is based on parity space method. It can generate the robust residual that is maximally sensitive to the fault caused by the change of the parameters. The example of simulation shows the application of the method, and the residual waves have different characteristics due to different parameter changes; one can detect and isolate the fault based on the different characteristics.

  4. Detection of generator bearing inner race creep by means of vibration and temperature analysis

    DEFF Research Database (Denmark)

    Skrimpas, Georgios Alexandros; Dragiev, Ivaylo G.; Hilmisson, Reynir

    2015-01-01

    damages and development. Detection of bearing creep can be achieved reliably based on continuous trending of the amplitude of vibration running speed harmonic and temperature absolute values. In order to decrease the number of condition indicators which need to be assessed, it is proposed to exploit......Vibration and temperature analysis are the two dominating condition monitoring techniques applied to fault detection of bearing failures in wind turbine generators. Relative movement between the bearing inner ring and generator axle is one of the most severe failure modes in terms of secondary...... a weighted average descriptor calculated based on the 3rd up to 6th harmonic orders. Two cases of different bearing creep severity are presented, showing the consistency of the combined vibration and temperature data utilization. In general, vibration monitoring reveals early signs of abnormality several...

  5. Tracy-Widom distribution based fault detection approach: application to aircraft sensor/actuator fault detection.

    Science.gov (United States)

    Hajiyev, Ch

    2012-01-01

    The fault detection approach based on the Tracy-Widom distribution is presented and applied to the aircraft flight control system. An operative method of testing the innovation covariance of the Kalman filter is proposed. The maximal eigenvalue of the random Wishart matrix is used as the monitoring statistic, and the testing problem is reduced to determine the asymptotics for the largest eigenvalue of the Wishart matrix. As a result, an algorithm for testing the innovation covariance based on the Tracy-Widom distribution is proposed. In the simulations, the longitudinal and lateral dynamics of the F-16 aircraft model is considered, and detection of sensor and control surface faults in the flight control system which affect the innovation covariance, are examined.

  6. Active Fault Detection Based on a Statistical Test

    DEFF Research Database (Denmark)

    Sekunda, André Krabdrup; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2016-01-01

    In this paper active fault detection of closed loop systems using dual Youla-Jabr-Bongiorno-Kucera(YJBK) parameters is presented. Until now all detector design for active fault detection using the dual YJBK parameters has been based on CUSUM detectors. Here a method for design of a matched filter...

  7. Dynamic Model and Fault Feature Research of Dual-Rotor System with Bearing Pedestal Looseness

    Directory of Open Access Journals (Sweden)

    Nanfei Wang

    2016-01-01

    Full Text Available The paper presents a finite element model of dual-rotor system with pedestal looseness stemming from loosened bolts. Dynamic model including bearing pedestal looseness is established based on the dual-rotor test rig. Three-degree-of-freedom (DOF planar rigid motion of loose bearing pedestal is fully considered and collision recovery coefficient is also introduced in the model. Based on the Timoshenko beam elements, using the finite element method, rigid body kinematics, and the Newmark-β algorithm for numerical simulation, dynamic characteristics of the inner and outer rotors and the bearing pedestal plane rigid body motion under bearing pedestal looseness condition are studied. Meanwhile, the looseness experiments under two different speed combinations are carried out, and the experimental results are basically the same. The simulation results are compared with the experimental results, indicating that vibration displacement waveforms of loosened rotor have “clipping” phenomenon. When the bearing pedestal looseness fault occurs, the inner and outer rotors vibration spectrum not only contains the difference and sum frequency of the two rotors’ fundamental frequency but also contains 2X and 3X component of rotor with loosened support, and so forth; low frequency spectrum is more, containing dividing component, and so forth; the rotor displacement spectrums also contain fewer combination frequency components, and so forth; when one side of the inner rotor bearing pedestal is loosened, the inner rotor axis trajectory is drawn into similar-ellipse shape.

  8. Methodology for fault detection in induction motors via sound and vibration signals

    Science.gov (United States)

    Delgado-Arredondo, Paulo Antonio; Morinigo-Sotelo, Daniel; Osornio-Rios, Roque Alfredo; Avina-Cervantes, Juan Gabriel; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene de Jesus

    2017-01-01

    Nowadays, timely maintenance of electric motors is vital to keep up the complex processes of industrial production. There are currently a variety of methodologies for fault diagnosis. Usually, the diagnosis is performed by analyzing current signals at a steady-state motor operation or during a start-up transient. This method is known as motor current signature analysis, which identifies frequencies associated with faults in the frequency domain or by the time-frequency decomposition of the current signals. Fault identification may also be possible by analyzing acoustic sound and vibration signals, which is useful because sometimes this information is the only available. The contribution of this work is a methodology for detecting faults in induction motors in steady-state operation based on the analysis of acoustic sound and vibration signals. This proposed approach uses the Complete Ensemble Empirical Mode Decomposition for decomposing the signal into several intrinsic mode functions. Subsequently, the frequency marginal of the Gabor representation is calculated to obtain the spectral content of the IMF in the frequency domain. This proposal provides good fault detectability results compared to other published works in addition to the identification of more frequencies associated with the faults. The faults diagnosed in this work are two broken rotor bars, mechanical unbalance and bearing defects.

  9. An Improved Wavelet‐Based Multivariable Fault Detection Scheme

    KAUST Repository

    Harrou, Fouzi

    2017-07-06

    Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)‐based Q and EWMA methods and MSPLS‐based Q method.

  10. Fault detection filter design for stochastic time-delay systems with sensor faults

    Science.gov (United States)

    Li, Xiao-Jian; Yang, Guang-Hong

    2012-08-01

    This article considers the fault detection (FD) problem for a class of Itô-type stochastic time-delay systems subject to external disturbances and sensor faults. The main objective is to design a fault detection filter (FDF) such that it has prescribed levels of disturbance attenuation and fault sensitivity. Sufficient conditions for guaranteeing these levels are formulated in terms of linear matrix inequalities (LMIs), and the corresponding fault detection filter design is cast into a convex optimisation problem which can be efficiently handled by using standard numerical algorithms. In order to reduce the conservatism of filter design with mixed objectives, multi-Lyapunov functions approach is used via Projection Lemma. In addition, it is shown that our results not only include some previous conditions characterising H ∞ performance and H - performance defined for linear time-invariant (LTI) systems as special cases but also improve these conditions. Finally, two examples are employed to illustrate the effectiveness of the proposed design scheme.

  11. Fault detection and fault-tolerant control for nonlinear systems

    CERN Document Server

    Li, Linlin

    2016-01-01

    Linlin Li addresses the analysis and design issues of observer-based FD and FTC for nonlinear systems. The author analyses the existence conditions for the nonlinear observer-based FD systems to gain a deeper insight into the construction of FD systems. Aided by the T-S fuzzy technique, she recommends different design schemes, among them the L_inf/L_2 type of FD systems. The derived FD and FTC approaches are verified by two benchmark processes. Contents Overview of FD and FTC Technology Configuration of Nonlinear Observer-Based FD Systems Design of L2 nonlinear Observer-Based FD Systems Design of Weighted Fuzzy Observer-Based FD Systems FTC Configurations for Nonlinear Systems< Application to Benchmark Processes Target Groups Researchers and students in the field of engineering with a focus on fault diagnosis and fault-tolerant control fields The Author Dr. Linlin Li completed her dissertation under the supervision of Prof. Steven X. Ding at the Faculty of Engineering, University of Duisburg-Essen, Germany...

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

    Science.gov (United States)

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

    2014-06-16

    Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.

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

    Directory of Open Access Journals (Sweden)

    Chang Liu

    2014-06-01

    Full Text Available Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM and a differential evolution (DE algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.

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

    Science.gov (United States)

    Al-Bugharbee, Hussein; Trendafilova, Irina

    2016-05-01

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

  15. Comparison of FEA with condition monitoring for real-time damage detection of bearing using infrared thermography techniques

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Yoon Jae; Ranjit, Shrestha; Kim, Won Tae [Dept. of Mechanical and Automotive Engineering, Kongju National University, Cheonan(Korea, Republic of)

    2015-06-15

    Since real-time monitoring systems, such as early fault detection, have been very important, an infrared thermography technique was proposed as a new diagnosis method. This study focused on damage detection and temperature characteristic analysis of ball bearings using the non-destructive, infrared thermography method. In this paper, for the reliability assessment, infrared experimental data were compared with finite element analysis (FEA) results from ANSYS. In this investigation, the temperature characteristics of ball bearing were analyzed under various loading conditions. Finally, it was confirmed that the infrared thermography technique was useful for the real-time detection of damage to bearings.

  16. An optimal selection method for morphological filter's parameters and its application in bearing fault diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Aijun; Xiang, Ling [North China Electric Power University, Hebei (China)

    2016-03-15

    The Mathematical morphological filter (MMF) is widely applied in vibration signal processing for fault diagnosis. The Structure element (SE) and the cutoff frequency of filter have important impacts on the filtering effect, but there is no selection principle of these parameters for vibration signal processing in fault diagnosis. In this paper, the working mechanism of the MMF is studied, and a novel technique with filter characteristics and selection criterion of the MMF is proposed. The filter characteristics of morphological filter are described through frequency response analysis. The relationship between the SE length and the cutoff frequency of MMF is put forward, and the quantitative selection method of SE in engineering is proposed to effectively remove the noise and detect the impulses. The method is evaluated using both simulated signal and experimental bearing vibration signal. The results show that quantized selection method can make MMF have the better filtering effect, and can reliably extract impulsive features for bearing defect diagnosis. The study provides a theoretical basis for the application of MMF in vibration signal processing.

  17. Fault detection and diagnosis of diesel engine valve trains

    Science.gov (United States)

    Flett, Justin; Bone, Gary M.

    2016-05-01

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

  18. Export Methods in Fault Detection and Localization Mechanisms

    Directory of Open Access Journals (Sweden)

    Aymen Belghith

    2012-07-01

    Full Text Available Monitoring the quality of service in a multi-domain network allows providers to ensure the control of multi-domain service performance. A multi-domain service is a service that crosses multiple domains. In this paper, we propose several mechanisms for fault detection and fault localization. A fault is detected when an end-to-end contract is not respected. Faulty domains are domains that do not fulfill their Quality of Service (QoS requirements. Our three proposed fault detection and localization mechanisms (FDLM depend on the export method used. These export methods define how the measurement results are exported for analysis. We consider the periodic export, the triggered export, and a combined method. For each FDLM, we propose two sub-schemes that use different fault detection strategies. In this paper, we describe these mechanisms and evaluate their performance using Network Simulator (NS-2.

  19. Robust fault detection for switched linear systems with state delays.

    Science.gov (United States)

    Wang, Dong; Wang, Wei; Shi, Peng

    2009-06-01

    This correspondence deals with the problem of robust fault detection for discrete-time switched systems with state delays under an arbitrary switching signal. The fault detection filter is used as the residual generator, in which the filter parameters are dependent on the system mode. Attention is focused on designing the robust fault detection filter such that, for unknown inputs, control inputs, and model uncertainties, the estimation error between the residuals and faults is minimized. The problem of robust fault detection is converted into an H(infinity)-filtering problem. By a switched Lyapunov functional approach, a sufficient condition for the solvability of this problem is established in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the proposed method.

  20. Using Order Tracking Analysis Method to Detect the Angle Faults of Blades on Wind Turbine

    DEFF Research Database (Denmark)

    Li, Pengfei; Hu, Weihao; Liu, Juncheng;

    2016-01-01

    The angle faults of blades on wind turbines are usually included in the set angle fault and the pitch angle fault. They are occupied with a high proportion in all wind turbine faults. Compare with the traditional fault detection methods, using order tracking analysis method to detect angle faults...

  1. Rolling Element Bearing Fault Diagnosis Using Integrated Nonlocal Means Denoising with Modified Morphology Filter Operators

    Directory of Open Access Journals (Sweden)

    Mien Van

    2016-01-01

    Full Text Available The impulses in vibration signals are used to identify faults in the bearings of rotating machinery. However, vibration signals are usually contaminated by noise that makes the process of extracting impulse characteristic of localized defect very challenging. In order to effectively diagnose bearing with noise masking vibration signal, a new methodology is proposed using integrated (i nonlocal means- (NLM- based denoising and (ii improved morphological filter operators. NLM based denoising is first employed to eliminate or reduce the background noise with minimal signal distortion. This denoised signal is then analysed by a proposed modified morphological analysis (MMA. The MMA analysis introduces a new morphological operator which is based on Modified-Different (DIF filter to include only fault relevant impulsive characteristics of the vibration signal. To improve further performance of the methodology the length of the structure element (SE used in MMA is optimized using a particle swarm optimization- (PSO- based kurtosis criterion. The results of simulated and real vibration signal show that the integrated NLM with MMA method as well as the MMA method alone yields superior performance in extracting impulsive characteristics of vibrations signals, especially for signal with high level of noise or presence of other sources masking the fault.

  2. The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification

    Directory of Open Access Journals (Sweden)

    Jiyong Li

    2015-01-01

    Full Text Available Rolling element bearings are widely used in high-speed rotating machinery; thus proper monitoring and fault diagnosis procedure to avoid major machine failures is necessary. As feature extraction and classification based on vibration signals are important in condition monitoring technique, and superfluous features may degrade the classification performance, it is needed to extract independent features, so LSSVM (least square support vector machine based on hybrid KICA-GDA (kernel independent component analysis-generalized discriminate analysis is presented in this study. A new method named sensitive subband feature set design (SSFD based on wavelet packet is also presented; using proposed variance differential spectrum method, the sensitive subbands are selected. Firstly, independent features are obtained by KICA; the feature redundancy is reduced. Secondly, feature dimension is reduced by GDA. Finally, the projected feature is classified by LSSVM. The whole paper aims to classify the feature vectors extracted from the time series and magnitude of spectral analysis and to discriminate the state of the rolling element bearings by virtue of multiclass LSSVM. Experimental results from two different fault-seeded bearing tests show good performance of the proposed method.

  3. VIBRATION ANALYSIS FOR DETECTION AND LOCALIZATION THE FAULTS OF ROTATING MACHINERY USING WAVELET TECHINIQUES

    Directory of Open Access Journals (Sweden)

    MIHAIL PRICOP

    2016-06-01

    Full Text Available Vulnerable and critical mechanical systems are bearings and drive belts. Signal analysis of vibration highlights the changes in root mean square, the frequency spectrum (frequencies and amplitudes in the time- frequency (Short Time Fourier Transform and Wavelet Transform, are the most used method for faults diagnosis and location of rotating machinery. This article presents the results of an experimental study applied on a di agnostic platform of rotating machinery through three Wavelet methods: (Discrete Wavelet Transform -DWT, Continuous Wavelet Transform -CWT, Wavelet Packet Transform -WPT with different mother wavelet. Wavelet Transform is used to decompose the original sig nal into sub -frequency band signals in order to obtain multiple data series at different resolutions and to identify faults appearing in the complex rotation systems. This paper investigates the use of different mother wavelet functions for drive belts and bearing fault diagnosis. The results demonstrate the possibility of using different mother wavelets in rotary systems diagnosis detecting and locating in this way the faults in bearings and drive belts.

  4. Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM

    Directory of Open Access Journals (Sweden)

    Jiang Xingmeng

    2016-01-01

    Full Text Available Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD and relevance vector machine (RVM is proposed. First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs were obtained. Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector. Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD method.

  5. All-to-all sequenced fault detection system

    Science.gov (United States)

    Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D.; Smith, Brian Edward

    2010-11-02

    An apparatus, program product and method enable nodal fault detection by sequencing communications between all system nodes. A master node may coordinate communications between two slave nodes before sequencing to and initiating communications between a new pair of slave nodes. The communications may be analyzed to determine the nodal fault.

  6. A Robust Fault Detection Approach for Nonlinear Systems

    Institute of Scientific and Technical Information of China (English)

    Min-Ze Chen; Qi Zhao; Dong-Hua Zhou

    2006-01-01

    In this paper, we study the robust fault detection problem of nonlinear systems. Based on the Lyapunov method,a robust fault detection approach for a general class of nonlinear systems is proposed. A nonlinear observer is first provided,and a sufficient condition is given to make the observer locally stable. Then, a practical algorithm is presented to facilitate the realization of the proposed observer for robust fault detection. Finally, a numerical example is provided to show the effectiveness of the proposed approach.

  7. Distance Based Fault detection in wireless sensor network

    Directory of Open Access Journals (Sweden)

    Ayasha Siddiqua

    2013-05-01

    Full Text Available Wireless Sensor Network (WSNs have become a new information collection and monitoring solution for a variety of application. In WSN, sensor nodes have strong hardware and software restrictionin terms of processing power, memory capability, power supply and communication throughput. Due to these restrictions, fault may occur in sensor. This paper presents a distance based fault detection (DBFDmethod for wireless sensor network using the average of confidence level and sensed data of sensor node. Simulation results show that sensor nodes with permanent faults and without fault which was judged as faulty are identified with high accuracy for a wide range of fault rate, and keep false alarm rate for different levels of sensor fault model and also correct nodes are identified by accuracy.

  8. Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings

    Science.gov (United States)

    Zhou, Hongdi; Shi, Tielin; Liao, Guanglan; Xuan, Jianping; Duan, Jie; Su, Lei; He, Zhenzhi; Lai, Wuxing

    2017-01-01

    This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after dimension reduction. It makes full use of the labeled information and introduces a weight strategy in the feature extraction. The class-related weights are introduced to denote differences among the samples from different patterns, and genetic algorithm (GA) is implemented to seek out appropriate weights for optimizing the classification results. The features based on wavelet packet decomposition are derived from the original signals. Then the intrinsic geometric features extracted by WKECA are fed into the support vector machine (SVM) classifier to recognize different operating conditions of bearings, and we obtain the overall accuracy (97%) for the experimental samples. The experimental results demonstrated the feasibility and effectiveness of the proposed method. PMID:28335480

  9. A New Method for Rolling Element Bearing Fault Diagnosis Based on Cyclostationary Theory

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The theory of cyclostationary and its application are very important for the analysis and processing of a non-stationary signal. The paper introduces second-order cyclostationary statistics, with emphass on discussion of cyclic periodogram arithmetic. Com-paring the time smoothed cyclic periodogram with the frequency smoothed cyclic perio- dogram, we found that the former is more useful to extract the feature of cyclostationary signals. The method has been applied to analyze the vibration signal of a rolling element bearing measured on a test bench, and proved to be effective. Meanwhile, we have com pared it with traditional power spectral density analysis, and the results prove that the time smoothed cyclic periodogram is more available to diagnose the fault of a rolling ele ment bearing.

  10. Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform

    Science.gov (United States)

    Li, Yongbo; Liang, Xihui; Xu, Minqiang; Huang, Wenhu

    2017-03-01

    When a fault occurs on bearings, the measured bearing fault signals contain both high Q-factor oscillation component and low Q-factor periodic impact component. TQWT is the improvement of the traditional single Q-factor wavelet transform, which is very suitable for separating the low Q-factor component from the high Q-factor component. However, the accuracy of its decomposition heavily depended on the selection of Q-factors. There is no reported simple but effective method to select the Q-factors with enough accuracy. This study aims to develop a strategy to diagnostic the early fault of rolling bearings. In this paper, a characteristic frequency ratio (CFR) is used to optimize Q-factors of TQWT (OTQWT). However, directly application of OTQWT is difficult to extract fault signatures at early stage due to the weak fault symptoms and strong noise. A strategy of combination of intrinsic characteristic-scale decomposition (ICD) and TQWT is proposed. ICD owns significant advantages on computation efficiency and alleviation of mode mixing. The effectiveness of the proposed strategy is tested with both simulated and experimental vibration signals. Meanwhile, comparisons are conducted between the proposed method and other methods like: envelope demodulation and EEMD-TQWT. Results show that the proposed method has superior performance in extracting fault features of defective bearings at an early stage.

  11. Fault Detection in Systems-A Fuzzy Approach

    Directory of Open Access Journals (Sweden)

    Ashok Kumar

    2004-04-01

    Full Text Available The task of fault detection is important when dealing with failures of crucial nature. After detection of faults in a system, it is advisable to suggest maintenance action before occurrenceof a failure. Fault detection may be done by observing various symptoms of the system during its operational stage. Sometimes, symptoms cannot be quantified easily but can be expressedin linguistic terms. Since linguistic terms are fuzzy quantifiers, these can be represented by fuzzy numbers. In this paper, two cases have been discussed, where a fault likely to affect a particular systemlsystems, is detected. In the first case, this is done by means of a compositional rule of inference. The second case is based on modified similarity measure. For both these  cases, linguistic terms have been expressed as trapezoidal fuzzy numbers

  12. Fault Management: Degradation Signature Detection, Modeling, and Processing Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Fault to Failure Progression (FFP) signature modeling and processing is a new method for applying condition-based signal data to detect degradation, to identify...

  13. Performance evaluation of fault detection methods for wastewater treatment processes.

    Science.gov (United States)

    Corominas, Lluís; Villez, Kris; Aguado, Daniel; Rieger, Leiv; Rosén, Christian; Vanrolleghem, Peter A

    2011-02-01

    Several methods to detect faults have been developed in various fields, mainly in chemical and process engineering. However, minimal practical guidelines exist for their selection and application. This work presents an index that allows for evaluating monitoring and diagnosis performance of fault detection methods, which takes into account several characteristics, such as false alarms, false acceptance, and undesirable switching from correct detection to non-detection during a fault event. The usefulness of the index to process engineering is demonstrated first by application to a simple example. Then, it is used to compare five univariate fault detection methods (Shewhart, EWMA, and residuals of EWMA) applied to the simulated results of the Benchmark Simulation Model No. 1 long-term (BSM1_LT). The BSM1_LT, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor and actuator faults and process disturbances in a wastewater treatment plant. The results from the method comparison using BSM1_LT show better performance to detect a sensor measurement shift for adaptive methods (residuals of EWMA) and when monitoring the actuator signals in a control loop (e.g., airflow). Overall, the proposed index is able to screen fault detection methods.

  14. Fault detection and isolation in processes involving induction machines

    Energy Technology Data Exchange (ETDEWEB)

    Zell, K.; Medvedev, A. [Control Engineering Group, Luleaa University of Technology, Luleaa (Sweden)

    1997-12-31

    A model-based technique for fault detection and isolation in electro-mechanical systems comprising induction machines is introduced. Two coupled state observers, one for the induction machine and another for the mechanical load, are used to detect and recognize fault-specific behaviors (fault signatures) from the real-time measurements of the rotor angular velocity and terminal voltages and currents. Practical applicability of the method is verified in full-scale experiments with a conveyor belt drive at SSAB, Luleaa Works. (orig.) 3 refs.

  15. Filtering, control and fault detection with randomly occurring incomplete information

    CERN Document Server

    Dong, Hongli; Gao, Huijun

    2013-01-01

    This book investigates the filtering, control and fault detection problems for several classes of nonlinear systems with randomly occurring incomplete information. It proposes new concepts, including RVNs, ROMDs, ROMTCDs, and ROQEs. The incomplete information under consideration primarily includes missing measurements, time-delays, sensor and actuator saturations, quantization effects and time-varying nonlinearities. The first part of this book focuses on the filtering, control and fault detection problems for several classes of nonlinear stochastic discrete-time systems and

  16. Adaptive Redundant Lifting Wavelet Transform Based on Fitting for Fault Feature Extraction of Roller Bearings

    Directory of Open Access Journals (Sweden)

    Huaqing Wang

    2012-03-01

    Full Text Available A least square method based on data fitting is proposed to construct a new lifting wavelet, together with the nonlinear idea and redundant algorithm, the adaptive redundant lifting transform based on fitting is firstly stated in this paper. By variable combination selections of basis function, sample number and dimension of basis function, a total of nine wavelets with different characteristics are constructed, which are respectively adopted to perform redundant lifting wavelet transforms on low-frequency approximate signals at each layer. Then the normalized lP norms of the new node-signal obtained through decomposition are calculated to adaptively determine the optimal wavelet for the decomposed approximate signal. Next, the original signal is taken for subsection power spectrum analysis to choose the node-signal for single branch reconstruction and demodulation. Experiment signals and engineering signals are respectively used to verify the above method and the results show that bearing faults can be diagnosed more effectively by the method presented here than by both spectrum analysis and demodulation analysis. Meanwhile, compared with the symmetrical wavelets constructed with Lagrange interpolation algorithm, the asymmetrical wavelets constructed based on data fitting are more suitable in feature extraction of fault signal of roller bearings.

  17. Time-frequency manifold sparse reconstruction: A novel method for bearing fault feature extraction

    Science.gov (United States)

    Ding, Xiaoxi; He, Qingbo

    2016-12-01

    In this paper, a novel transient signal reconstruction method, called time-frequency manifold (TFM) sparse reconstruction, is proposed for bearing fault feature extraction. This method introduces image sparse reconstruction into the TFM analysis framework. According to the excellent denoising performance of TFM, a more effective time-frequency (TF) dictionary can be learned from the TFM signature by image sparse decomposition based on orthogonal matching pursuit (OMP). Then, the TF distribution (TFD) of the raw signal in a reconstructed phase space would be re-expressed with the sum of learned TF atoms multiplied by corresponding coefficients. Finally, one-dimensional signal can be achieved again by the inverse process of TF analysis (TFA). Meanwhile, the amplitude information of the raw signal would be well reconstructed. The proposed technique combines the merits of the TFM in denoising and the atomic decomposition in image sparse reconstruction. Moreover, the combination makes it possible to express the nonlinear signal processing results explicitly in theory. The effectiveness of the proposed TFM sparse reconstruction method is verified by experimental analysis for bearing fault feature extraction.

  18. Observer Based Detection of Sensor Faults in Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Nielsen, R.

    2009-01-01

    An observer based scheme is proposed to detect sensor faults in wind  turbines. In the example used for the proposed scheme the wind turbine  drive train is considered. A model of the drive train is used to  design the observer, and in this model the wind speed is an important  input, however......, if an unknown input observer the fault detection  scheme can be non dependent on the actual wind speed. The scheme  is validated on data from a more advanced and detailed simulation  model. The proposed scheme detects the sensor faults a few samples  after the beginning of the faults....

  19. A fault detection and isolation filter for discrete linear systems.

    Science.gov (United States)

    Giovanini, L; Dondo, R

    2003-10-01

    The problem of fault and/or abrupt disturbances detection and isolation for discrete linear systems is analyzed in this work. A strategy for detecting and isolating faults and/or abrupt disturbances is presented. The strategy is an extension of an already existing result in the continuous time domain to the discrete domain. The resulting detection algorithm is a Kalman filter with a special structure. The filter generates a residuals vector in such a way that each element of this vector is related with one fault or disturbance. Therefore the effects of the other faults, disturbances, and measurement noises in this element are minimized. The necessary stability and convergence conditions are briefly exposed. A numerical example is also presented.

  20. Fault detection by surface seismic scanning tunneling macroscope: Field test

    KAUST Repository

    Hanafy, Sherif M.

    2014-08-05

    The seismic scanning tunneling macroscope (SSTM) is proposed for detecting the presence of near-surface impedance anomalies and faults. Results with synthetic data are consistent with theory in that scatterers closer to the surface provide brighter SSTM profiles than those that are deeper. The SSTM profiles show superresolution detection if the scatterers are in the near-field region of the recording line. The field data tests near Gulf of Aqaba, Haql, KSA clearly show the presence of the observable fault scarp, and identify the subsurface presence of the hidden faults indicated in the tomograms. Superresolution detection of the fault is achieved, even when the 35 Hz data are lowpass filtered to the 5-10 Hz band.

  1. A fault detection service for wide area distributed computations.

    Energy Technology Data Exchange (ETDEWEB)

    Stelling, P.

    1998-06-09

    The potential for faults in distributed computing systems is a significant complicating factor for application developers. While a variety of techniques exist for detecting and correcting faults, the implementation of these techniques in a particular context can be difficult. Hence, we propose a fault detection service designed to be incorporated, in a modular fashion, into distributed computing systems, tools, or applications. This service uses well-known techniques based on unreliable fault detectors to detect and report component failure, while allowing the user to tradeoff timeliness of reporting against false positive rates. We describe the architecture of this service, report on experimental results that quantify its cost and accuracy, and describe its use in two applications, monitoring the status of system components of the GUSTO computational grid testbed and as part of the NetSolve network-enabled numerical solver.

  2. Auxiliary signal design in fault detection and diagnosis

    Science.gov (United States)

    Zhang, Xue Jun

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

  3. Mine geophysics methods in studying the coal bearing rock mass condition in low magnitude tectonic fault zones

    Science.gov (United States)

    Alexeev, A. D.; Zhitlyonok, D. M.; Pitalenko, E. I.

    2003-04-01

    Disjunctive type tectonic faults are quite serious problem at underground coal winning. In the fault adjacent areas both coal seam and coal bearing rocks are usually essentially fractured that makes them less stable in coalfaces at underground mining. Some researchers have pointed out to enhanced stress state in these areas as well provided that loosening zones are absent. Coal seams are mostly inclined to disjunctive faults in Central region of Donets Coal Basin where tectonic processes were very intense. There are a lot of small faults with magnitudes close to seam thickness about 2 m in this region along with large thrust or fault disjunctives with stratigraphic magnitudes over 10 m (Dyleyev, Northern, Brunvald, Bulavin faults and others). Highest disjunctive dislocation is typical for coalfields near mines "Toretskaya" and "Novodzerzhinskaya", Coal Production Co. "Dzerzhinskugol", where dislocation density reaches about 8.5 faults per 1 km across the field. Small disjunctive faults often coincide with sites of sudden coal and gas outbursts, longwall inrushes, and poor support condition in development workings. It is known that affected zones on either side accommodate each disjunctive fault, these zones being distinctive for increased fissuring, higher stresses, coal and rocks differing strength. Affected zone width dependence on the fault parameters was determined using geological approach. Mine electrical survey and acoustical probing methods were used to study rock mass faulted condition in the vicinity of development workings and stopes intercepting low magnitude (below 5 m) disjunctive faults in coal field of mine "Toretskaya". These findings have allowed to establish a new fault magnitude dependence of rupture tectonic dislocation's affected zone width in the form of B = 3.2 H, where B is dislocation's affected zone width (m); H is the dislocation's stratigraphic magnitude (m). It was established as well that stress level in rock mass near disjunctive

  4. Optimal Sensor Allocation for Fault Detection and Isolation

    Science.gov (United States)

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

    2004-01-01

    Automatic fault diagnostic schemes rely on various types of sensors (e.g., temperature, pressure, vibration, etc) to measure the system parameters. Efficacy of a diagnostic scheme is largely dependent on the amount and quality of information available from these sensors. The reliability of sensors, as well as the weight, volume, power, and cost constraints, often makes it impractical to monitor a large number of system parameters. An optimized sensor allocation that maximizes the fault diagnosibility, subject to specified weight, volume, power, and cost constraints is required. Use of optimal sensor allocation strategies during the design phase can ensure better diagnostics at a reduced cost for a system incorporating a high degree of built-in testing. In this paper, we propose an approach that employs multiple fault diagnosis (MFD) and optimization techniques for optimal sensor placement for fault detection and isolation (FDI) in complex systems. Keywords: sensor allocation, multiple fault diagnosis, Lagrangian relaxation, approximate belief revision, multidimensional knapsack problem.

  5. Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Wen-An Yang

    2016-01-01

    Full Text Available The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems. Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems. This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA for fault diagnosis of rolling element bearings. The binary immune genetic algorithm (BIGA is employed to solve the trace ratio problem in TR-KDA. The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings. Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data. The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings.

  6. Multi-directional fault detection system

    Science.gov (United States)

    Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D.; Smith, Brian Edward

    2009-03-17

    An apparatus, program product and method checks for nodal faults in a group of nodes comprising a center node and all adjacent nodes. The center node concurrently communicates with the immediately adjacent nodes in three dimensions. The communications are analyzed to determine a presence of a faulty node or connection.

  7. Early-stage fault isolation based on frequency response fitted by small-size samples for cryogenic cold compressors with active magnetic bearings

    Science.gov (United States)

    Arpaia, Pasquale; De Vito, Luca; Girone, Mario; Pezzetti, Marco

    2016-01-01

    A model-based method for fault detection and early-stage isolation, applicable when unfaulty conditions can be identified only by a reduced number of trials (even only one), is presented. The basic idea is to model analytically the uncertainty of the unfaulty frequency response and express the fault condition in terms of the noise power variance. A preliminary fault isolation is carried out by sensitivity analysis in order to identify the most influencing model parameters and assess their influence on the estimated noise. Then, during maintenance tests, the noise power is checked to detect the faulty condition. This technique is conceived to check the quality of a critical component in an experimental installation (fault detection and early-stage isolation), as well as to detect its faulty dynamic behaviors over a long horizon maintenance test campaign (condition monitoring). The method was applied to four cold compressors with active magnetic bearings at CERN by proving to be able to detect an actual faulty condition in one of such compressors.

  8. Fault feature extraction and enhancement of rolling element bearing in varying speed condition

    Science.gov (United States)

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

    2016-08-01

    In engineering applications, the variability of load usually varies the shaft speed, which further degrades the efficacy of the diagnostic method based on the hypothesis of constant speed analysis. Therefore, the investigation of the diagnostic method suitable for the varying speed condition is significant for the bearing fault diagnosis. In this instance, a novel fault feature extraction and enhancement procedure was proposed by the combination of the iterative envelope analysis and a low pass filtering operation in this paper. At first, based on the analytical model of the collected vibration signal, the envelope signal was theoretically calculated and the iterative envelope analysis was improved for the varying speed condition. Then, a feature enhancement procedure was performed by applying a low pass filter on the temporal envelope obtained by the iterative envelope analysis. Finally, the temporal envelope signal was transformed to the angular domain by the computed order tracking and the fault feature was extracted on the squared envelope spectrum. Simulations and experiments were used to validate the efficacy of the theoretical analysis and proposed procedure. It is shown that the computed order tracking method is recommended to be applied on the envelope of the signal in order to avoid the energy spreading and amplitude distortion. Compared with the feature enhancement method performed by the fast kurtogram and corresponding optimal band pass filtering, the proposed method can efficiently extract the fault character in the varying speed condition with less amplitude attenuation. Furthermore, do not involve the center frequency estimation, the proposed method is more concise for engineering applications.

  9. Clustering diagnosis of rolling element bearing fault based on integrated Autoregressive/Autoregressive Conditional Heteroscedasticity model

    Science.gov (United States)

    Wang, Guofeng; Liu, Chang; Cui, Yinhu

    2012-09-01

    Feature extraction plays an important role in the clustering analysis. In this paper an integrated Autoregressive (AR)/Autoregressive Conditional Heteroscedasticity (ARCH) model is proposed to characterize the vibration signal and the model coefficients are adopted as feature vectors to realize clustering diagnosis of rolling element bearings. The main characteristic is that the AR item and ARCH item are interrelated with each other so that it can depict the excess kurtosis and volatility clustering information in the vibration signal more accurately in comparison with two-stage AR/ARCH model. To testify the correctness, four kinds of bearing signals are adopted for parametric modeling by using the integrated and two-stage AR/ARCH model. The variance analysis of the model coefficients shows that the integrated AR/ARCH model can get more concentrated distribution. Taking these coefficients as feature vectors, K means based clustering is utilized to realize the automatic classification of bearing fault status. The results show that the proposed method can get more accurate results in comparison with two-stage model and discrete wavelet decomposition.

  10. Incipient failure detection (IFD) of SSME ball bearings

    Science.gov (United States)

    1982-01-01

    Because of the immense noise background during the operation of a large engine such as the SSME, the relatively low level unique ball bearing signatures were often buried by the overall machine signal. As a result, the most commonly used bearing failure detection technique, pattern recognition using power spectral density (PSD) constructed from the extracted bearing signals, is rendered useless. Data enhancement techniques were carried out by using a HP5451C Fourier Analyzer. The signal was preprocessed by a Digital Audio Crop. DAC-1024I noise cancelling filter in order to estimate the desired signal corrupted by the backgound noise. Reference levels of good bearings were established. Any deviation of bearing signals from these reference levels indicate the incipient bearing failures.

  11. Observer Based Fault Detection and Moisture Estimating in Coal Mill

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Mataji, Babak

    2008-01-01

    In this paper an observer-based method for detecting faults and estimating moisture content in the coal in coal mills is presented. Handling of faults and operation under special conditions, such as high moisture content in the coal, are of growing importance due to the increasing requirements to......In this paper an observer-based method for detecting faults and estimating moisture content in the coal in coal mills is presented. Handling of faults and operation under special conditions, such as high moisture content in the coal, are of growing importance due to the increasing...... requirements to the general performance of power plants. Detection  of faults and moisture content estimation are consequently of high interest in the handling of the problems caused by faults and moisture content. The coal flow out of the mill is the obvious variable to monitor, when detecting non-intended drops in the coal...... flow out of the coal mill. However, this variable is not measurable. Another estimated variable is the moisture content, which is only "measurable" during steady-state operations of the coal mill. Instead, this paper suggests a method where these unknown variables are estimated based on a simple energy...

  12. Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, Howard [Purdue Univ., West Lafayette, IN (United States); Braun, James E. [Purdue Univ., West Lafayette, IN (United States)

    2015-12-31

    This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment in the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.

  13. Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, Howard; Braun, James E.

    2015-12-31

    This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment in the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.

  14. Application of fault factor method to fault detection and diagnosis for space shuttle main engine

    Science.gov (United States)

    Cha, Jihyoung; Ha, Chulsu; Ko, Sangho; Koo, Jaye

    2016-09-01

    This paper deals with an application of the multiple linear regression algorithm to fault detection and diagnosis for the space shuttle main engine (SSME) during a steady state. In order to develop the algorithm, the energy balance equations, which balances the relation among pressure, mass flow rate and power at various locations within the SSME, are obtained. Then using the measurement data of some important parameters of the engine, fault factors which reflects the deviation of each equation from the normal state are estimated. The probable location of each fault and the levels of severity can be obtained from the estimated fault factors. This process is numerically demonstrated for the SSME at 104% Rated Propulsion Level (RPL) by using the simulated measurement data from the mathematical models of the engine. The result of the current study is particularly important considering that the recently developed reusable Liquid Rocket Engines (LREs) have staged-combustion cycles similarly to the SSME.

  15. Development and Test of Methods for Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Jørgensen, R.B.

    the thesis. The IPC offers prospects of repeated fault scenarios, and support studies in robustness issues. The thesis contributes with a numerical fault analysis representation, practical applications of existing methods for FDI, and a method for robust FDI for practical applications....... they are especiallu crucial for the entire operaiton of a closed loop system. The purpose of the thesis is to investigate, deveop, and verify methods for fault detection and isolation on control loop systems. An Industrial Position Controller, (IPC), laboratory setup is used as an application example throughout...

  16. Signal processing for solar array monitoring, fault detection, and optimization

    CERN Document Server

    Braun, Henry; Spanias, Andreas

    2012-01-01

    Although the solar energy industry has experienced rapid growth recently, high-level management of photovoltaic (PV) arrays has remained an open problem. As sensing and monitoring technology continues to improve, there is an opportunity to deploy sensors in PV arrays in order to improve their management. In this book, we examine the potential role of sensing and monitoring technology in a PV context, focusing on the areas of fault detection, topology optimization, and performance evaluation/data visualization. First, several types of commonly occurring PV array faults are considered and detection algorithms are described. Next, the potential for dynamic optimization of an array's topology is discussed, with a focus on mitigation of fault conditions and optimization of power output under non-fault conditions. Finally, monitoring system design considerations such as type and accuracy of measurements, sampling rate, and communication protocols are considered. It is our hope that the benefits of monitoring presen...

  17. Stator Fault Detection in Induction Motors by Autoregressive Modeling

    Directory of Open Access Journals (Sweden)

    Francisco M. Garcia-Guevara

    2016-01-01

    Full Text Available This study introduces a novel methodology for early detection of stator short circuit faults in induction motors by using autoregressive (AR model. The proposed algorithm is based on instantaneous space phasor (ISP module of stator currents, which are mapped to α-β stator-fixed reference frame; then, the module is obtained, and the coefficients of the AR model for such module are estimated and evaluated by order selection criterion, which is used as fault signature. For comparative purposes, a spectral analysis of the ISP module by Discrete Fourier Transform (DFT is performed; a comparison of both methodologies is obtained. To demonstrate the suitability of the proposed methodology for detecting and quantifying incipient short circuit stator faults, an induction motor was altered to induce different-degree fault scenarios during experimentation.

  18. Soft-Fault Detection Technologies Developed for Electrical Power Systems

    Science.gov (United States)

    Button, Robert M.

    2004-01-01

    The NASA Glenn Research Center, partner universities, and defense contractors are working to develop intelligent power management and distribution (PMAD) technologies for future spacecraft and launch vehicles. The goals are to provide higher performance (efficiency, transient response, and stability), higher fault tolerance, and higher reliability through the application of digital control and communication technologies. It is also expected that these technologies will eventually reduce the design, development, manufacturing, and integration costs for large, electrical power systems for space vehicles. The main focus of this research has been to incorporate digital control, communications, and intelligent algorithms into power electronic devices such as direct-current to direct-current (dc-dc) converters and protective switchgear. These technologies, in turn, will enable revolutionary changes in the way electrical power systems are designed, developed, configured, and integrated in aerospace vehicles and satellites. Initial successes in integrating modern, digital controllers have proven that transient response performance can be improved using advanced nonlinear control algorithms. One technology being developed includes the detection of "soft faults," those not typically covered by current systems in use today. Soft faults include arcing faults, corona discharge faults, and undetected leakage currents. Using digital control and advanced signal analysis algorithms, we have shown that it is possible to reliably detect arcing faults in high-voltage dc power distribution systems (see the preceding photograph). Another research effort has shown that low-level leakage faults and cable degradation can be detected by analyzing power system parameters over time. This additional fault detection capability will result in higher reliability for long-lived power systems such as reusable launch vehicles and space exploration missions.

  19. Fault-tolerant control for current sensors of doubly fed induction generators based on an improved fault detection method

    DEFF Research Database (Denmark)

    Li, Hui; Yang, Chao; Hu, Yaogang

    2014-01-01

    Fault-tolerant control of current sensors is studied in this paper to improve the reliability of a doubly fed induction generator (DFIG). A fault-tolerant control system of current sensors is presented for the DFIG, which consists of a new current observer and an improved current sensor fault...... detection algorithm, and fault-tolerant control system are investigated by simulation. The results indicate that the outputs of the observer and the sensor are highly coherent. The fault detection algorithm can efficiently detect both soft and hard faults in current sensors, and the fault-tolerant control...... system can effectively tolerate both types of faults. © 2013 Published by Elsevier Ltd. All rights reserved....

  20. FaultBuster: data driven fault detection and diagnosis for industrial systems

    DEFF Research Database (Denmark)

    Bergantino, Nicola; Caponetti, Fabio; Longhi, Sauro

    2009-01-01

    Efficient and reliable monitoring systems are mandatory to assure the required security standards in industrial complexes. This paper describes the recent developments of FaultBuster, a purely data-driven diagnostic system. It is designed so to be easily scalable to different monitor tasks....... Multivariate statistical models based on principal components are used to detect abnormal situations. Tailored to alarms, a probabilistic inference engine process the fault evidences to output the most probable diagnosis. Results from the DX 09 Diagnostic Challenge shown strong detection properties, while...

  1. Fault-weighted quantification method of fault detection coverage through fault mode and effect analysis in digital I&C systems

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Jaehyun; Lee, Seung Jun, E-mail: sjlee420@unist.ac.kr; Jung, Wondea

    2017-05-15

    Highlights: • We developed the fault-weighted quantification method of fault detection coverage. • The method has been applied to specific digital reactor protection system. • The unavailability of the module had 20-times difference with the traditional method. • Several experimental tests will be effectively prioritized using this method. - Abstract: The one of the most outstanding features of a digital I&C system is the use of a fault-tolerant technique. With an awareness regarding the importance of thequantification of fault detection coverage of fault-tolerant techniques, several researches related to the fault injection method were developed and employed to quantify a fault detection coverage. In the fault injection method, each injected fault has a different importance because the frequency of realization of every injected fault is different. However, there have been no previous studies addressing the importance and weighting factor of each injected fault. In this work, a new method for allocating the weighting to each injected fault using the failure mode and effect analysis data was proposed. For application, the fault-weighted quantification method has also been applied to specific digital reactor protection system to quantify the fault detection coverage. One of the major findings in an application was that we may estimate the unavailability of the specific module in digital I&C systems about 20-times smaller than real value when we use a traditional method. The other finding was that we can also classify the importance of the experimental case. Therefore, this method is expected to not only suggest an accurate quantification procedure of fault-detection coverage by weighting the injected faults, but to also contribute to an effective fault injection experiment by sorting the importance of the failure categories.

  2. Generic, scalable and decentralized fault detection for robot swarms

    Science.gov (United States)

    Christensen, Anders Lyhne; Timmis, Jon

    2017-01-01

    Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system’s capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation. PMID:28806756

  3. Fault-tolerant strategies for an implantable centrifugal blood pump using a radially controlled magnetic bearing.

    Science.gov (United States)

    Pai, Chi Nan; Shinshi, Tadahiko

    2011-10-01

    In our laboratory, an implantable centrifugal blood pump (CBP) with a two degrees-of-freedom radially controlled magnetic bearing (MB) to support the impeller without contact has been developed to assist the pumping function of the weakened heart ventricle. In order to maintain the function of the CBP after damage to the electromagnets (EMs) of the MB, fault-tolerant strategies for the CBP are proposed in this study. Using a redundant MB design, magnetic levitation of the impeller was maintained with damage to up to two out of a total of four EMs of the MB; with damage to three EMs, contact-free support of the impeller was achieved using hydrodynamic and electromagnetic forces; and with damage to all four EMs, the pump operating point, of 5 l/min against 100 mmHg, was achieved using the motor for rotation of the impeller, with contact between the impeller and the stator.

  4. The microstructural evolution of clay-bearing carbonate faults during high-velocity friction experiments

    Science.gov (United States)

    Bullock, Rachael; De Paola, Nicola; Holdsworth, Robert

    2014-05-01

    Seismicity in the Northern Apennines, Italy, nucleates within and propagates through a multilayer sequence comprising limestones with marl interbeds. Observations from the Gubbio fault (1984, Ms = 5.2) indicate that the majority of earthquake displacement is localized within principal slip zones (PSZs), thermal decomposition of calcite. Initial microstructure of the wet gouges, on the other hand, is characterized by a distributed and interconnected network of wet clay surrounding calcite grains. The microstructure of the sheared wet gouges is characterized by a diffuse PSS, limited fabric development, and no PSZ; deformation is much more distributed. In addition, grain-size reduction in the wet gouges is ~1 order of magnitude less than in dry gouge equivalents. Thus, we attribute the contrasting frictional behaviour and microstructural evolution in the dry vs. wet gouges to the fact that in the wet gouges, distributed slip preferentially occurs on the pre-existing, weak clay network. This reduces the need for grain-breakage to occur before slip is able to localize, explaining the lack of a slip-hardening phase. Shear induced compaction of the wet clay-bearing gouges is also likely to generate a considerable pore-fluid overpressure within the impermeable clay network, further contributing to their weak behaviour. The lack of resistance to frictional sliding shown by the wet clay-bearing gouges contrasts with the traditional concept that phyllosilicates, due to their velocity-strengthening nature, should have a stabilizing role in upper crustal fault zones, and has significant implications for seismic hazard in the Apennines.

  5. A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy

    Science.gov (United States)

    Li, Yongbo; Xu, Minqiang; Wang, Rixin; Huang, Wenhu

    2016-01-01

    This paper presents a new rolling bearing fault diagnosis method based on local mean decomposition (LMD), improved multiscale fuzzy entropy (IMFE), Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT). When the fault occurs in rolling bearings, the measured vibration signal is a multi-component amplitude-modulated and frequency-modulated (AM-FM) signal. LMD, a new self-adaptive time-frequency analysis method can decompose any complicated signal into a series of product functions (PFs), each of which is exactly a mono-component AM-FM signal. Hence, LMD is introduced to preprocess the vibration signal. Furthermore, IMFE that is designed to avoid the inaccurate estimation of fuzzy entropy can be utilized to quantify the complexity and self-similarity of time series for a range of scales based on fuzzy entropy. Besides, the LS approach is introduced to refine the fault features by sorting the scale factors. Subsequently, the obtained features are fed into the multi-fault classifier ISVM-BT to automatically fulfill the fault pattern identifications. The experimental results validate the effectiveness of the methodology and demonstrate that proposed algorithm can be applied to recognize the different categories and severities of rolling bearings.

  6. Parameter estimation and reliable fault detection of electric motors

    Institute of Scientific and Technical Information of China (English)

    Dusan PROGOVAC; Le Yi WANG; George YIN

    2014-01-01

    Accurate model identification and fault detection are necessary for reliable motor control. Motor-characterizing parameters experience substantial changes due to aging, motor operating conditions, and faults. Consequently, motor parameters must be estimated accurately and reliably during operation. Based on enhanced model structures of electric motors that accommodate both normal and faulty modes, this paper introduces bias-corrected least-squares (LS) estimation algorithms that incorporate functions for correcting estimation bias, forgetting factors for capturing sudden faults, and recursive structures for efficient real-time implementation. Permanent magnet motors are used as a benchmark type for concrete algorithm development and evaluation. Algorithms are presented, their properties are established, and their accuracy and robustness are evaluated by simulation case studies under both normal operations and inter-turn winding faults. Implementation issues from different motor control schemes are also discussed.

  7. Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems.

    Science.gov (United States)

    Mekki, Hemza; Benzineb, Omar; Boukhetala, Djamel; Tadjine, Mohamed; Benbouzid, Mohamed

    2015-07-01

    The fault-tolerant control problem belongs to the domain of complex control systems in which inter-control-disciplinary information and expertise are required. This paper proposes an improved faults detection, reconstruction and fault-tolerant control (FTC) scheme for motor systems (MS) with typical faults. For this purpose, a sliding mode controller (SMC) with an integral sliding surface is adopted. This controller can make the output of system to track the desired position reference signal in finite-time and obtain a better dynamic response and anti-disturbance performance. But this controller cannot deal directly with total system failures. However an appropriate combination of the adopted SMC and sliding mode observer (SMO), later it is designed to on-line detect and reconstruct the faults and also to give a sensorless control strategy which can achieve tolerance to a wide class of total additive failures. The closed-loop stability is proved, using the Lyapunov stability theory. Simulation results in healthy and faulty conditions confirm the reliability of the suggested framework.

  8. A New Acoustic Emission Sensor Based Gear Fault Detection Approach

    Directory of Open Access Journals (Sweden)

    Junda Zhu

    2013-01-01

    Full Text Available In order to reduce wind energy costs, prognostics and health management (PHM of wind turbine is needed to ensure the reliability and availability of wind turbines. A gearbox is an important component of a wind turbine. Therefore, developing effective gearbox fault detection tools is important to the PHM of wind turbine. In this paper, a new acoustic emission (AE sensor based gear fault detection approach is presented. This approach combines a heterodyne based frequency reduction technique with time synchronous average (TSA and spectrum kurtosis (SK to process AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne technique commonly used in communication is first employed to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from several hundred kHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. The presented approach is validated using seeded gear tooth crack fault tests on a notational split torque gearbox. The approach presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.

  9. Data driven fault detection and isolation: a wind turbine scenario

    Directory of Open Access Journals (Sweden)

    Rubén Francisco Manrique Piramanrique

    2015-04-01

    Full Text Available One of the greatest drawbacks in wind energy generation is the high maintenance cost associated to mechanical faults. This problem becomes more evident in utility scale wind turbines, where the increased size and nominal capacity comes with additional problems associated with structural vibrations and aeroelastic effects in the blades. Due to the increased operation capability, it is imperative to detect system degradation and faults in an efficient manner, maintaining system integrity, reliability and reducing operation costs. This paper presents a comprehensive comparison of four different Fault Detection and Isolation (FDI filters based on “Data Driven” (DD techniques. In order to enhance FDI performance, a multi-level strategy is used where:  the first level detects the occurrence of any given fault (detection, while  the second identifies the source of the fault (isolation. Four different DD classification techniques (namely Support Vector Machines, Artificial Neural Networks, K Nearest Neighbors and Gaussian Mixture Models were studied and compared for each of the proposed classification levels. The best strategy at each level could be selected to build the final data driven FDI system. The performance of the proposed scheme is evaluated on a benchmark model of a commercial wind turbine. 

  10. Robust filtering and fault detection of switched delay systems

    CERN Document Server

    Wang, Dong; Wang, Wei

    2013-01-01

    Switched delay systems appear in a wide field of applications including networked control systems, power systems, memristive systems. Though the large amount of ideas with respect to such systems have generated, until now, it still lacks a framework to focus on filter design and fault detection issues which are relevant to life safety and property loss. Beginning with the comprehensive coverage of the new developments in the analysis and control synthesis for switched delay systems, the monograph not only provides a systematic approach to designing the filter and detecting the fault of switched delay systems, but it also covers the model reduction issues. Specific topics covered include: (1) Arbitrary switching signal where delay-independent and delay-dependent conditions are presented by proposing a linearization technique. (2) Average dwell time where a weighted Lyapunov function is come up with dealing with filter design and fault detection issues beside taking model reduction problems. The monograph is in...

  11. Unknown input observer based detection of sensor faults in a wind turbine

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2010-01-01

    In this paper an unknown input observer is designed to detect three different sensor fault scenarios in a specified bench mark model for fault detection and accommodation of wind turbines. In this paper a subset of faults is dealt with, it are faults in the rotor and generator speed sensors as well...... as a converter sensor fault. The proposed scheme detects the speed sensor faults in question within the specified requirements given in the bench mark model, while the converter fault is detected but not within the required time to detect....

  12. Fault Estimation and Control for a Quad-Rotor MAV Using a Polynomial Observer. Part I: Fault Detection

    OpenAIRE

    Flores Colunga, Gerardo Ramon; Aguilar-Sierra, Hipolito; Lozano, Rogelio; Salazar, Sergio

    2014-01-01

    International audience; This work addresses the problem of fault detection and diagnosis (FDD) for a quad-rotor mini aerial vehicle (MAV). Actuator faults are considered on this paper. The basic idea behind the proposed method is to estimate the faults signals using the extended state observers theory. To estimate the faults, a polynomial observer is presented by using the available measurements and know inputs of the system. In order to investigate the observability and diagnosability proper...

  13. Model-based fault detection and diagnosis in ALMA subsystems

    Science.gov (United States)

    Ortiz, José; Carrasco, Rodrigo A.

    2016-07-01

    The Atacama Large Millimeter/submillimeter Array (ALMA) observatory, with its 66 individual telescopes and other central equipment, generates a massive set of monitoring data every day, collecting information on the performance of a variety of critical and complex electrical, electronic and mechanical components. This data is crucial for most troubleshooting efforts performed by engineering teams. More than 5 years of accumulated data and expertise allow for a more systematic approach to fault detection and diagnosis. This paper presents model-based fault detection and diagnosis techniques to support corrective and predictive maintenance in a 24/7 minimum-downtime observatory.

  14. Fault Detection and Isolation using Viability Theory and Interval Observers

    Science.gov (United States)

    Ghaniee Zarch, Majid; Puig, Vicenç; Poshtan, Javad

    2017-01-01

    This paper proposes the use of interval observers and viability theory in fault detection and isolation (FDI). Viability theory develops mathematical and algorithmic methods for investigating the adaptation to viability constraints of evolutions governed by complex systems under uncertainty. These methods can be used for checking the consistency between observed and predicted behavior by using simple sets that approximate the exact set of possible behavior (in the parameter or state space). In this paper, fault detection is based on checking for an inconsistency between the measured and predicted behaviors using viability theory concepts and sets. Finally, an example is provided in order to show the usefulness of the proposed approach.

  15. Fault Detection and Isolation and Fault Tolerant Control of Wind Turbines Using Set-Valued Observers

    DEFF Research Database (Denmark)

    Casau, Pedro; Rosa, Paulo Andre Nobre; Tabatabaeipour, Seyed Mojtaba

    2012-01-01

    and Isolation (FDI) and Fault Tolerant Control (FTC) of wind turbines, by taking advantage of the recent advances in SVO theory for model invalidation. A simple wind turbine model is presented along with possible faulty scenarios. The FDI algorithm is built on top of the described model, taking into account......Research on wind turbine Operations & Maintenance (O&M) procedures is critical to the expansion of Wind Energy Conversion systems (WEC). In order to reduce O&M costs and increase the lifespan of the turbine, we study the application of Set-Valued Observers (SVO) to the problem of Fault Detection...

  16. Hall Sensor Output Signal Fault-Detection & Safety Implementation Logic

    Directory of Open Access Journals (Sweden)

    Lee SangHun

    2016-01-01

    Full Text Available Recently BLDC motors have been popular in various industrial applications and electric mobility. Recently BLDC motors have been popular in various industrial applications and electric mobility. In most brushless direct current (BLDC motor drives, there are three hall sensors as a position reference. Low resolution hall effect sensor is popularly used to estimate the rotor position because of its good comprehensive performance such as low cost, high reliability and sufficient precision. Various possible faults may happen in a hall effect sensor. This paper presents a fault-tolerant operation method that allows the control of a BLDC motor with one faulty hall sensor and presents the hall sensor output fault-tolerant control strategy. The situations considered are when the output from a hall sensor stays continuously at low or high levels, or a short-time pulse appears on a hall sensor signal. For fault detection, identification of a faulty signal and generating a substitute signal, this method only needs the information from the hall sensors. There are a few research work on hall effect sensor failure of BLDC motor. The conventional fault diagnosis methods are signal analysis, model based analysis and knowledge based analysis. The proposed method is signal based analysis using a compensation signal for reconfiguration and therefore fault diagnosis can be fast. The proposed method is validated to execute the simulation using PSIM.

  17. New detection method for rolling element and bearing defects

    Science.gov (United States)

    Burchill, R. F.; Frarey, J. L.

    1972-01-01

    Instrument for detecting defects in rolling elements of bearings is described. Detection depends on rate at which rolling elements impact defect and establishes envelope amplitude of ball resonant frequency. Block diagram of instrument is provided and results obtained in conducting tests are reported.

  18. Fuzzy model-based observers for fault detection in CSTR.

    Science.gov (United States)

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions.

  19. Similarity measure application to fault detection of flight system

    Institute of Scientific and Technical Information of China (English)

    KIM J H; LEE S H; WANG Hong-mei

    2009-01-01

    Fault detection technique is introduced with similarity measure. The characteristics of conventional similarity measure based on fuzzy number are discussed. With the help of distance measure, similarity measure is constructed explicitly. The designed distance-based similarity measure is applicable to general fuzzy membership functions including non-convex fuzzy membership function, whereas fuzzy number-based similarity measure has limitation to calculate the similarity of general fuzzy membership functions. The applicability of the proposed similarity measure to general fuzzy membership structures is proven by identifying the definition. To decide fault detection of flight system, the experimental data (pitching moment coefficients and lift coefficients) are transformed into fuzzy membership functions. Distance-based similarity measure is applied to the obtained fuzzy membership functions, and similarity computation and analysis are obtained with the fault and normal operation coefficients.

  20. Fault Detection in Coal Mills used in Power Plants

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Mataji, Babak

    2006-01-01

    In order to achieve high performance and efficiency of coal-fired power plants, it is highly important to control the coal flow into the furnace in the power plant. This means suppression of disturbances and force the coal mill to deliver the required coal flow, as well as monitor the coal mill...... in order to detect faults in the coal mill when they emerge. This paper deals with the second objective. Based on a simple dynamic model of the energy balance a residual is formed for the coal mill. An optimal unknown input observer is designed to estimate this residual. The estimated residual is following...... tested on measured data of a fault in a coal mill, it can hereby be concluded that this residual is very useful for detecting faults in the coal mill....

  1. Unknown input observer based detection of sensor faults in a wind turbine

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2010-01-01

    In this paper an unknown input observer is designed to detect three different sensor fault scenarios in a specified bench mark model for fault detection and accommodation of wind turbines. In this paper a subset of faults is dealt with, it are faults in the rotor and generator speed sensors as we...

  2. Fault detection in reciprocating compressor valves under varying load conditions

    Science.gov (United States)

    Pichler, Kurt; Lughofer, Edwin; Pichler, Markus; Buchegger, Thomas; Klement, Erich Peter; Huschenbett, Matthias

    2016-03-01

    This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method's performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions.

  3. Induction motor inter turn fault detection using infrared thermographic analysis

    Science.gov (United States)

    Singh, Gurmeet; Anil Kumar, T. Ch.; Naikan, V. N. A.

    2016-07-01

    Induction motors are the most commonly used prime movers in industries. These are subjected to various environmental, thermal and load stresses that ultimately reduces the motor efficiency and later leads to failure. Inter turn fault is the second most commonly observed faults in the motors and is considered the most severe. It can lead to the failure of complete phase and can even cause accidents, if left undetected or untreated. This paper proposes an online and non invasive technique that uses infrared thermography, in order to detect the presence of inter turn fault in induction motor drive. Two methods have been proposed that detect the fault and estimate its severity. One method uses transient thermal monitoring during the start of motor and other applies pseudo coloring technique on infrared image of the motor, after it reaches a thermal steady state. The designed template for pseudo-coloring is in acquiescence with the InterNational Electrical Testing Association (NETA) thermographic standard. An index is proposed to assess the severity of the fault present in the motor.

  4. 基于LMD和SVDD的滚动轴承故障诊断方法%Roller Bearing Fault Diagnosis Method Based on LMD And SVDD

    Institute of Scientific and Technical Information of China (English)

    刘震坤

    2012-01-01

    Aiming at the absence of fault samples in the engineering applications, a roller bearing fault diagnosis method based on local mean decomposition (LMD) and Support Vector Data Description (SVDD) is proposed. First, the rolling bearing fault vibration signals collected from the inner-race and outer-race were decomposed into a number of PFs by LMD; then, the ratios of amplitudes in the characteristic frequencies were defined as the characteristic amplitude ratios after the envelope spectra of some of PFs includ- ing the main fault information were obtained; finally, the characteristic amplitude ratios were served as the fault characteristic vectors, which were be trained and tested through SVDD. The result shows, the method based on LMD and SVDD has higher detection rate and better classification results.%针对在实际工程应用中缺乏故障样本的问题,论文提出一种基于局部均值分解(Localmeandecomposition,LMD)和支持向量数据描述(supponVectorDataDescription,SVDD)的故障诊断方法。该方法首先将采集到的滚动轴承内外圈故障振动信号进行LMD分解后为若干个PF分量,然后求出包含主要故障信息的若干个PF分量的包络谱,将包络谱中故障特征频率处的幅值作为故障特征向量,通过SVDD进行训练和测试。结果显示,基于LMD和SVDD的滚动轴承故障诊断方法的检测率较高、分类效果较好。

  5. Evaluation of Wind Farm Controller based Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Shafiei, Seyed Ehsan

    2015-01-01

    detection and isolation and fault tolerant control has previously been proposed. Based on this model, and international competition on wind farm FDI was organized. The contributions were presented at the IFAC World Congress 2014. In this paper the top three contributions to this competition are shortly...

  6. Adaptive partitioning PCA model for improving fault detection and isolation☆

    Institute of Scientific and Technical Information of China (English)

    Kangling Liu; Xin Jin; Zhengshun Fei; Jun Liang

    2015-01-01

    In chemical process, a large number of measured and manipulated variables are highly correlated. Principal com-ponent analysis (PCA) is widely applied as a dimension reduction technique for capturing strong correlation un-derlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physical y and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect. The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.

  7. Applying Parametric Fault Detection to a Mechanical System

    DEFF Research Database (Denmark)

    Felício, P.; Stoustrup, Jakob; Niemann, H.

    2002-01-01

    A way of doing parametric fault detection is described. It is based on the representation of parameter changes as linear fractional transformations (lfts). We describe a model with parametric uncertainty. Then a stabilizing controller is chosen and its robustness properties are studied via mu...

  8. Fault detection of a benchmark wind turbine using interval analysis

    DEFF Research Database (Denmark)

    Tabatabaeipour, Seyed Mojtaba; Odgaard, Peter Fogh; Bak, Thomas

    2012-01-01

    is nonlinear. We use an effective wind speed estimator to estimate the effective wind speed and then using interval analysis and monotonicity of the aerodynamic torque with respect to the effective wind speed, we can apply the method to the nonlinear system. The fault detection algorithm checks the consistency...

  9. Fault detection for nonlinear systems - A standard problem approach

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1998-01-01

    The paper describes a general method for designing (nonlinear) fault detection and isolation (FDI) systems for nonlinear processes. For a rich class of nonlinear systems, a nonlinear FDI system can be designed using convex optimization procedures. The proposed method is a natural extension...

  10. Optimal input design for fault detection and diagnosis

    DEFF Research Database (Denmark)

    Sadegh, Payman; Madsen, Henrik; Holst, J.

    1995-01-01

    In the paper, the design of optimal input signals for detection and diagnosis in a stochastic dynamical system is investigated. The design is based on maximization of Kullback measure between the model under fault and the model under normal operation conditions. It is established that the optimal...

  11. DSP-Based Sensor Fault Detection and Post Fault-Tolerant Control of an Induction Motor-Based Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Bekheïra Tabbache

    2012-01-01

    Full Text Available This paper deals with sensor fault detection within a reconfigurable direct torque control of an induction motor-based electric vehicle. The proposed strategy concerns current, voltage, and speed sensors faults that are detected and followed by post fault-tolerant control to allow the vehicle continuous operation. The proposed approach is validated through experiments on an induction motor drive and simulations on an electric vehicle using a European urban and extraurban driving cycle.

  12. On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features

    Directory of Open Access Journals (Sweden)

    Mark Frogley

    2013-01-01

    Full Text Available To reduce the maintenance cost, avoid catastrophic failure, and improve the wind transmission system reliability, online condition monitoring system is critical important. In the real applications, many rotating mechanical faults, such as bearing surface defect, gear tooth crack, chipped gear tooth and so on generate impulsive signals. When there are these types of faults developing inside rotating machinery, each time the rotating components pass over the damage point, an impact force could be generated. The impact force will cause a ringing of the support structure at the structural natural frequency. By effectively detecting those periodic impulse signals, one group of rotating machine faults could be detected and diagnosed. However, in real wind turbine operations, impulsive fault signals are usually relatively weak to the background noise and vibration signals generated from other healthy components, such as shaft, blades, gears and so on. Moreover, wind turbine transmission systems work under dynamic operating conditions. This will further increase the difficulties in fault detection and diagnostics. Therefore, developing advanced signal processing methods to enhance the impulsive signals is in great needs.In this paper, an adaptive filtering technique will be applied for enhancing the fault impulse signals-to-noise ratio in wind turbine gear transmission systems. Multiple statistical features designed to quantify the impulsive signals of the processed signal are extracted for bearing fault detection. The multiple dimensional features are then transformed into one dimensional feature. A minimum error rate classifier will be designed based on the compressed feature to identify the gear transmission system with defect. Real wind turbine vibration signals will be used to demonstrate the effectiveness of the presented methodology.

  13. Gyroscope Pivot Bearing Dimension and Surface Defect Detection

    OpenAIRE

    2011-01-01

    Because of the perceived lack of systematic analysis in illumination system design processes and a lack of criteria for design methods in vision detection a method for the design of a task-oriented illumination system is proposed. After detecting the micro-defects of a gyroscope pivot bearing with a high curvature glabrous surface and analyzing the characteristics of the surface detection and reflection model, a complex illumination system with coaxial and ring lights is proposed. The illumin...

  14. An application of LTR design in fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    1998-01-01

    as a standard Loop Transfer Recovery (LTR) design problem. As a consequence of the connection between LTR and FDI design, it is shown in an example how the LQG/LTR design method for full order and a proportional-integral observer can be applied with advantages in connection with FDI.......The fault detection and isolation (FDI) problem is considered in this paper. The FDI problem is formulated as a filter design problem, where the faults in the system is estimated and the disturbance acting on the system is rejected. It turns out that the filter design problem can be considered...

  15. Shock Waves Trigger Fault Weakening in Calcite-bearing Rocks During Earthquakes

    Science.gov (United States)

    Spagnuolo, E.; Plumper, O.; Violay, M.; Cavallo, A.; Di Toro, G.

    2014-12-01

    The weakening mechanism of calcite-bearing rocks is still poorly understood though many major earthquakes stroke within carbonate sequences. Insights derive from the laboratory: in experiments performed on calcite-bearing gouges, up to 90% drop in friction is associated to grain size reduction to the nanoscale and the formation of crystal-plastic microstructures suggesting the activation of debated weakening mechanisms (e.g., grain boundary sliding and diffusion creep; nanopowder lubrication). Whatever the case, it is unclear how nanoparticles form and what their role is at the initiation of sliding. To investigate initial fault instability we sheared with a rotary shear apparatus SHIVA pre-cut ring-shaped solid cylinders (50/30 mm ext/int diameter) of Carrara marble (99.9% CaCO3). Rock cylinders were slid for few millimetres(0, 1.5 mm and 5mm) at accelerations (6.5 ms-2) and normal stresses (10 MPa) approaching seismic deformation conditions. Initial slip (nano-fracturing have occurred preserving the grain shape (pulverization) and (3) reaction products attributable to high pressure and high temperature conditions (i.e. calcite decomposition into amorphous carbon rimming the nanograins). All the above features are typical of shock-induced changes in minerals. We interpret the above observations as follows: pre-existing grain boundaries or newly formed defects are the nuclei for the generation of dislocations and for their pile-up; the fast release of those piles-up in avalanches under rapid stress loading (fast moving dislocations) may explain the origin of such a shock-like behaviour responsible for large initial frictional weakening. The passage of the shock wave induces pervasive nanofracturing with grain size reduction to the nano-scale and an abrupt temperature rise responsible for calcite decarbonation and formation of carbon amorphous material.

  16. Clustering of frequency spectrums from different bearing fault using principle component analysis

    Directory of Open Access Journals (Sweden)

    Yusof M.F.M.

    2017-01-01

    Full Text Available In studies associated with the defect in rolling element bearing, signal clustering are one of the popular approach taken in attempt to identify the type of defect. However, the noise interruption are one of the major issues which affect the degree of effectiveness of the applied clustering method. In this paper, the application of principle component analysis (PCA as a pre-processing method for hierarchical clustering analysis on the frequency spectrum of the vibration signal was proposed. To achieve the aim, the vibration signal was acquired from the operating bearings with different condition and speed. In the next stage, the principle component analysis was applied to the frequency spectrums of the acquired signals for pattern recognition purpose. Meanwhile the mahalanobis distance model was used to cluster the result from PCA. According to the results, it was found that the change in amplitude at the respective fundamental frequencies can be detected as a result from the application of PCA. Meanwhile, the application of mahalanobis distance was found to be suitable for clustering the results from principle component analysis. Uniquely, it was discovered that the spectrums from healthy and inner race defect bearing can be clearly distinguished from each other even though the change in amplitude pattern for inner race defect frequency spectrum was too small compared to the healthy one. In this work, it was demonstrated that the use of principle component analysis could sensitively detect the change in the pattern of the frequency spectrums. Likewise, the implementation of mahalanobis distance model for clustering purpose was found to be significant for bearing defect identification.

  17. Fault detection filter design for an anaerobic digestion process

    Energy Technology Data Exchange (ETDEWEB)

    Aubrun, C.; Garnier, O. [Univ. Henri Poincare - Nancy 1, Vandoeuvre (France); Harmand, J.; Steyer, J.P. [LBE-INRA, Narbonne (France)

    2000-05-01

    In this paper, a Fault Detection and Isolation observer based method has been applied to biological wastewater treatment process. The method is designed with a dynamic model and the observer is determined using the eigenstructure assignment approach. The efficiency of the method is demonstrated for both detection and isolation of an actuator and a sensor failure using experimental data from a pilot scale anaerobic digestion process for the treatment of an industrial wine distillery vinasses. (orig.)

  18. Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

    Science.gov (United States)

    Li, Gang; McDonald, Geoff L.; Zhao, Qing

    2017-01-01

    This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method.

  19. Fault detection and diagnosis using neural network approaches

    Science.gov (United States)

    Kramer, Mark A.

    1992-01-01

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

  20. Fault Detection and Isolation (Fdi Via Neural Networks

    Directory of Open Access Journals (Sweden)

    Neeraj Prakash Srivastava,

    2014-01-01

    Full Text Available Recent approaches to fault detection and isolation for dynamic systems using methods of integrating quantitative and qualitative model information, based upon soft computing (SC methods are used. In this study, the use of SC methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained neural network (NN can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model. This main difficulty can be overcome using qualitative modeling or rule-based inference methods. The paper presents the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of Neural network. Keywords: Soft computing methods, fault-diagnosis, FDI

  1. Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

    Directory of Open Access Journals (Sweden)

    Mauricio Holguín-Londoño

    2016-01-01

    Full Text Available Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM. This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

  2. Battery Fault Detection with Saturating Transformers

    Science.gov (United States)

    Davies, Francis J. (Inventor); Graika, Jason R. (Inventor)

    2013-01-01

    A battery monitoring system utilizes a plurality of transformers interconnected with a battery having a plurality of battery cells. Windings of the transformers are driven with an excitation waveform whereupon signals are responsively detected, which indicate a health of the battery. In one embodiment, excitation windings and sense windings are separately provided for the plurality of transformers such that the excitation waveform is applied to the excitation windings and the signals are detected on the sense windings. In one embodiment, the number of sense windings and/or excitation windings is varied to permit location of underperforming battery cells utilizing a peak voltage detector.

  3. Adaptive sparsest narrow-band decomposition method and its applications to rolling element bearing fault diagnosis

    Science.gov (United States)

    Cheng, Junsheng; Peng, Yanfeng; Yang, Yu; Wu, Zhantao

    2017-02-01

    Enlightened by ASTFA method, adaptive sparsest narrow-band decomposition (ASNBD) method is proposed in this paper. In ASNBD method, an optimized filter must be established at first. The parameters of the filter are determined by solving a nonlinear optimization problem. A regulated differential operator is used as the objective function so that each component is constrained to be a local narrow-band signal. Afterwards, the signal is filtered by the optimized filter to generate an intrinsic narrow-band component (INBC). ASNBD is proposed aiming at solving the problems existed in ASTFA. Gauss-Newton type method, which is applied to solve the optimization problem in ASTFA, is irreplaceable and very sensitive to initial values. However, more appropriate optimization method such as genetic algorithm (GA) can be utilized to solve the optimization problem in ASNBD. Meanwhile, compared with ASTFA, the decomposition results generated by ASNBD have better physical meaning by constraining the components to be local narrow-band signals. Comparisons are made between ASNBD, ASTFA and EMD by analyzing simulation and experimental signals. The results indicate that ASNBD method is superior to the other two methods in generating more accurate components from noise signal, restraining the boundary effect, possessing better orthogonality and diagnosing rolling element bearing fault.

  4. Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

    Directory of Open Access Journals (Sweden)

    Paulo Antonio Delgado-Arredondo

    2015-01-01

    Full Text Available Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG, the time-frequency Morlet scalogram (TFMS, multiple signal classification (MUSIC, and fast Fourier transform (FFT. The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.

  5. Nonlinear observer based fault detection and isolation for a momentum wheel

    DEFF Research Database (Denmark)

    Jensen, Hans-Christian Becker; Wisniewski, Rafal

    2001-01-01

    This article realizes nonlinear Fault Detection and Isolation for a momentum wheel. The Fault Detection and Isolation is based on a Failure Mode and Effect Analysis, which states which faults might occur and can be detected. The algorithms presented in this paper are based on a geometric approach...... toachieve nonlinear Fault Detection and Isolation. The proposed algorithms are tested in a simulation study and the pros and cons of the algorithm are discussed....

  6. PCB Fault Detection Using Image Processing

    Science.gov (United States)

    Nayak, Jithendra P. R.; Anitha, K.; Parameshachari, B. D., Dr.; Banu, Reshma, Dr.; Rashmi, P.

    2017-08-01

    The importance of the Printed Circuit Board inspection process has been magnified by requirements of the modern manufacturing environment where delivery of 100% defect free PCBs is the expectation. To meet such expectations, identifying various defects and their types becomes the first step. In this PCB inspection system the inspection algorithm mainly focuses on the defect detection using the natural images. Many practical issues like tilt of the images, bad light conditions, height at which images are taken etc. are to be considered to ensure good quality of the image which can then be used for defect detection. Printed circuit board (PCB) fabrication is a multidisciplinary process, and etching is the most critical part in the PCB manufacturing process. The main objective of Etching process is to remove the exposed unwanted copper other than the required circuit pattern. In order to minimize scrap caused by the wrongly etched PCB panel, inspection has to be done in early stage. However, all of the inspections are done after the etching process where any defective PCB found is no longer useful and is simply thrown away. Since etching process costs 0% of the entire PCB fabrication, it is uneconomical to simply discard the defective PCBs. In this paper a method to identify the defects in natural PCB images and associated practical issues are addressed using Software tools and some of the major types of single layer PCB defects are Pattern Cut, Pin hole, Pattern Short, Nick etc., Therefore the defects should be identified before the etching process so that the PCB would be reprocessed. In the present approach expected to improve the efficiency of the system in detecting the defects even in low quality images

  7. Bear

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The famous physicist made for his scholars this riddle. A fellow encountered a bear in a wasteland. There was nobody else there. Both were frightened and ran away. Fellow to the north, bear to the west. Suddenly the fellow stopped, aimed his gun to the south and shot the bear. What colour was the bear?

  8. Strengthen Bearing Test, Prevent Locomotive Fault%加强轴承检测,防止机车故障

    Institute of Scientific and Technical Information of China (English)

    达正雄; 朱元高; 陆玮

    2001-01-01

    总结了几年来使用JSC-206轴承检测仪的经验、教训及取得的成效,对保证机车运行安全起到了很大的作用。%This Paper sums up the experiences, lectures and achievement of using “JSC-206 Diagnosis Instrument for Bearing Fault", the instrument does very important function for ensuring the safety of locomotive.

  9. Nonlinear observer based fault detection and isolation for a momentum wheel

    DEFF Research Database (Denmark)

    Jensen, Hans-Christian Becker; Wisniewski, Rafal

    2001-01-01

    This article realizes nonlinear Fault Detection and Isolation for a momentum wheel. The Fault Detection and Isolation is based on a Failure Mode and Effect Analysis, which states which faults might occur and can be detected. The algorithms presented in this paper are based on a geometric approach...

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

    Directory of Open Access Journals (Sweden)

    Xiao Yu

    2015-11-01

    Full Text Available Because roller element bearings (REBs failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC to select salient features from the marginal spectrum of vibration signals by Hilbert–Huang Transform (HHT. In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS into window spectrums, following which Rand Index (RI criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs. Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines. The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU. The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault

  11. Faults

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Through the study of faults and their effects, much can be learned about the size and recurrence intervals of earthquakes. Faults also teach us about crustal...

  12. Digital electronic engine control fault detection and accommodation flight evaluation

    Science.gov (United States)

    Baer-Ruedhart, J. L.

    1984-01-01

    The capabilities and performance of various fault detection and accommodation (FDA) schemes in existing and projected engine control systems were investigated. Flight tests of the digital electronic engine control (DEEC) in an F-15 aircraft show discrepancies between flight results and predictions based on simulation and altitude testing. The FDA methodology and logic in the DEEC system, and the results of the flight failures which occurred to date are described.

  13. Automatic fault detection on BIPV systems without solar irradiation data

    CERN Document Server

    Leloux, Jonathan; Luna, Alberto; Desportes, Adrien

    2014-01-01

    BIPV systems are small PV generation units spread out over the territory, and whose characteristics are very diverse. This makes difficult a cost-effective procedure for monitoring, fault detection, performance analyses, operation and maintenance. As a result, many problems affecting BIPV systems go undetected. In order to carry out effective automatic fault detection procedures, we need a performance indicator that is reliable and that can be applied on many PV systems at a very low cost. The existing approaches for analyzing the performance of PV systems are often based on the Performance Ratio (PR), whose accuracy depends on good solar irradiation data, which in turn can be very difficult to obtain or cost-prohibitive for the BIPV owner. We present an alternative fault detection procedure based on a performance indicator that can be constructed on the sole basis of the energy production data measured at the BIPV systems. This procedure does not require the input of operating conditions data, such as solar ...

  14. Sliding mode fault detection and fault-tolerant control of smart dampers in semi-active control of building structures

    Science.gov (United States)

    Yeganeh Fallah, Arash; Taghikhany, Touraj

    2015-12-01

    Recent decades have witnessed much interest in the application of active and semi-active control strategies for seismic protection of civil infrastructures. However, the reliability of these systems is still in doubt as there remains the possibility of malfunctioning of their critical components (i.e. actuators and sensors) during an earthquake. This paper focuses on the application of the sliding mode method due to the inherent robustness of its fault detection observer and fault-tolerant control. The robust sliding mode observer estimates the state of the system and reconstructs the actuators’ faults which are used for calculating a fault distribution matrix. Then the fault-tolerant sliding mode controller reconfigures itself by the fault distribution matrix and accommodates the fault effect on the system. Numerical simulation of a three-story structure with magneto-rheological dampers demonstrates the effectiveness of the proposed fault-tolerant control system. It was shown that the fault-tolerant control system maintains the performance of the structure at an acceptable level in the post-fault case.

  15. Fuzzy-Expert Diagnostics for Detecting and Locating Internal Faults in Three Phase Induction Motors

    Institute of Scientific and Technical Information of China (English)

    DONG Mingchui; CHEANG Takson; SEKAR Booma Devi; CHAN Sileong

    2008-01-01

    Internal faults in three phase induction motors can result in serious performance degradation and eventual system failures if not properly detected and treated in time. Artificial intelligence techniques, the core of soft-computing, have numerous advantages over conventional fault diagnostic approaches; therefore, a soft-computing system was developed to detect and diagnose electric motor faults. The fault diagnostic system for three-phase induction motors samples the fault symptoms and then uses a fuzzy-expert forward inference model to identify the fault. This paper describes how to define the membership functions and fuzzy sets based on the fault symptoms and how to construct the hierarchical fuzzy inference nets with the propagation of probabilities concerning the uncertainty of faults. The designed hierarchical fuzzy inference nets efficiently detect and diagnose the fault type and exact location in a three phase induction motor. The validity and effectiveness of this approach is clearly shown from obtained testing results.

  16. Chemical controls on fault behavior: weakening of serpentinite sheared against quartz-bearing rocks and its significance for fault creep in the San Andreas system

    Science.gov (United States)

    Moore, Diane E.; Lockner, David A.

    2013-01-01

    The serpentinized ultramafic rocks found in many plate-tectonic settings commonly are juxtaposed against crustal rocks along faults, and the chemical contrast between the rock types potentially could influence the mechanical behavior of such faults. To investigate this possibility, we conducted triaxial experiments under hydrothermal conditions (200-350°C), shearing serpentinite gouge between forcing blocks of granite or quartzite. In an ultramafic chemical environment, the coefficient of friction, µ, of lizardite and antigorite serpentinite is 0.5-0.6, and µ increases with increasing temperature over the tested range. However, when either lizardite or antigorite serpentinite is sheared against granite or quartzite, strength is reduced to µ ~ 0.3, with the greatest strength reductions at the highest temperatures (temperature weakening) and slowest shearing rates (velocity strengthening). The weakening is attributed to a solution-transfer process that is promoted by the enhanced solubility of serpentine in pore fluids whose chemistry has been modified by interaction with the quartzose wall rocks. The operation of this process will promote aseismic slip (creep) along serpentinite-bearing crustal faults at otherwise seismogenic depths. During short-term experiments serpentine minerals reprecipitate in low-stress areas, whereas in longer experiments new Mg-rich phyllosilicates crystallize in response to metasomatic exchanges across the serpentinite-crustal rock contact. Long-term shear of serpentinite against crustal rocks will cause the metasomatic mineral assemblages, which may include extremely weak minerals such as saponite or talc, to play an increasingly important role in the mechanical behavior of the fault. Our results may explain the distribution of creep on faults in the San Andreas system.

  17. Method for detecting an open-switch fault in a grid-connected NPC inverter system

    DEFF Research Database (Denmark)

    Choi, Ui-Min; Jeong, Hae-Gwang; Lee, Kyo-Beum;

    2012-01-01

    This paper proposes a fault-detection method for an open-switch fault in the switches of grid-connected neutral-point-clamped inverter systems. The proposed method can not only detect the fault condition but also identify the location of the faulty switch. In the proposed method, which is designed...

  18. Fluid-injection and the mechanics of frictional stability of shale-bearing faults

    Science.gov (United States)

    Scuderi, Marco Maria; Collettini, Cristiano; Marone, Chris

    2017-04-01

    Fluid overpressure is one of the primary mechanisms for triggering tectonic fault slip and human-induced seismicity. This mechanism is appealing because fluids lubricate the fault and reduce the effective normal stress that holds the fault in place. However, current models of earthquake nucleation, based on rate- and state- friction, imply that stable sliding is favored by the increase of pore fluid pressure. Despite this apparent dilemma, there are a few studies on the role of fluid pressure in frictional stability under controlled, laboratory conditions. Here, we describe laboratory experiments on shale fault gouge, conducted in the double direct shear configuration in a true-triaxial machine. To characterize frictional stability and hydrological properties we performed three types of experiments: 1) stable sliding shear experiment to determine the material failure envelope resulting in fault strength of µ=0.28 and fault zone permeability (k 10-19m2); 2) velocity step experiments to determine the rate- and state- frictional properties, characterized by a velocity strengthening behavior with a negative rate parameter b, indicative of stable aseismic creep; 3) creep experiment to study fault slip evolution with increasing pore-fluid pressure. In these creep experiments fault slip history can be divided in three main stages: a) for low fluid pressure the fault is locked and undergoes compaction; b) with increasing fluid pressurization, we observe aseismic creep (i.e. v=0.0001 µm/s) associated with fault dilation, with maintained low permeability; c) As fluid pressure is further increased and we approach the failure criteria fault begins to accelerate, the dilation rate increases causing an increase in permeability. Following the first acceleration we document complex fault slip behavior characterized by periodic accelerations and decelerations with slip velocity that remains slow (i.e. v 200 µm/s), never approaching dynamic slip rates. Surprisingly, this complex

  19. Nonlinear analysis of r.c. framed buildings retrofitted with elastomeric and friction bearings under near-fault earthquakes

    Science.gov (United States)

    Mazza, Mirko

    2015-12-01

    Reinforced concrete (r.c.) framed buildings designed in compliance with inadequate seismic classifications and code provisions present in many cases a high vulnerability and need to be retrofitted. To this end, the insertion of a base isolation system allows a considerable reduction of the seismic loads transmitted to the superstructure. However, strong near-fault ground motions, which are characterised by long-duration horizontal pulses, may amplify the inelastic response of the superstructure and induce a failure of the isolation system. The above considerations point out the importance of checking the effectiveness of different isolation systems for retrofitting a r.c. framed structure. For this purpose, a numerical investigation is carried out with reference to a six-storey r.c. framed building, which, primarily designed (as to be a fixed-base one) in compliance with the previous Italian code (DM96) for a medium-risk seismic zone, has to be retrofitted by insertion of an isolation system at the base for attaining performance levels imposed by the current Italian code (NTC08) in a high-risk seismic zone. Besides the (fixed-base) original structure, three cases of base isolation are studied: elastomeric bearings acting alone (e.g. HDLRBs); in-parallel combination of elastomeric and friction bearings (e.g. high-damping-laminated-rubber bearings, HDLRBs and steel-PTFE sliding bearings, SBs); friction bearings acting alone (e.g. friction pendulum bearings, FPBs). The nonlinear analysis of the fixed-base and base-isolated structures subjected to horizontal components of near-fault ground motions is performed for checking plastic conditions at the potential critical (end) sections of the girders and columns as well as critical conditions of the isolation systems. Unexpected high values of ductility demand are highlighted at the lower floors of all base-isolated structures, while re-centring problems of the base isolation systems under near-fault earthquakes are

  20. Fault detection and accommodation via neural network and variable structure control

    Institute of Scientific and Technical Information of China (English)

    Hao YANG; Bin JIANG

    2007-01-01

    This paper proposes a novel idea that classifies faults into two different kinds: serious faults and small faults,and treats them with different strategies respectively. A kind of artificial neural network (ANN) is proposed for detecting serious faults, and variable structure (VS) model-following control is constructed for accommodating small faults. The proposed framework takes both advantages of qualitative way and quantitative way of fault detection and accommodation.Moreover, the uncertainty case is investigated and the VS controller is modified. Simulation results of a remotely piloted aircraft with control actuator failures illustrate the performance of the developed algorithm.

  1. On the bi-dimensional variational decomposition applied to nonstationary vibration signals for rolling bearing crack detection in coal cutters

    Science.gov (United States)

    Jiang, Yu; Li, Zhixiong; Zhang, Chao; Hu, Chao; Peng, Z.

    2016-06-01

    This work aims to detect rolling bearing cracks using a variational approach. An original method that appropriately incorporates bi-dimensional variational mode decomposition (BVMD) into discriminant diffusion maps (DDM) is proposed to analyze the nonstationary vibration signals recorded from the cracked rolling bearings in coal cutters. The advantage of this variational decomposition based diffusion map (VDDM) method in comparison to the current DDM is that the intrinsic vibration mode of the crack can be filtered into a limited bandwidth in the frequency domain with an estimated central frequency, thus discarding the interference signal components in the vibration signals and significantly improving the crack detection performance. In addition, the VDDM is able to simultaneously process two-channel sensor signals to reduce information leakage. Experimental validation using rolling bearing crack vibration signals demonstrates that the VDDM separated the raw signals into four intrinsic modes, including one roller vibration mode, one roller cage vibration mode, one inner race vibration mode, and one outer race vibration mode. Hence, reliable fault features were extracted from the outer race vibration mode, and satisfactory crack identification performance was achieved. The comparison between the proposed VDDM and existing approaches indicated that the VDDM method was more efficient and reliable for crack detection in coal cutter rolling bearings. As an effective catalyst for rolling bearing crack detection, this newly proposed method is useful for practical applications.

  2. Fault and meal detection by redundant continuous glucose monitors and the unscented Kalman filter

    DEFF Research Database (Denmark)

    Mahmoudi, Zeinab; Nørgaard, Kirsten; Poulsen, Niels Kjølstad

    2017-01-01

    The purpose of this study is to develop a method for detecting and compensating the anomalies of continuous glucose monitoring (CGM) sensors as well as detecting unannounced meals. Both features, sensor fault detection/correction and meal detection, are necessary to have a reliable artificial...... is in terms of a patient simulation model, where the model in the detector is the same as the patient simulation model used for evaluation of the detector. The detection module consists of two CGM sensors, two fault detectors, a fault isolator, and an adaptive unscented Kalman filter (UKF). Two types...... of sensor faults, i.e., drift and pressure induced sensor attenuation (PISA), are simulated by a Gaussian random walk model. Each of the fault detectors has a local UKF that receives the signal from the associated sensor, detects faults, and finally tunes the adaptive UKF. A fault isolator that accepts data...

  3. Fault Diagnosis for Actuators in a Class of Nonlinear Systems Based on an Adaptive Fault Detection Observer

    Directory of Open Access Journals (Sweden)

    Runxia Guo

    2016-01-01

    Full Text Available The problem of actuators’ fault diagnosis is pursued for a class of nonlinear control systems that are affected by bounded measurement noise and external disturbances. A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect.

  4. Investigating the Spatial Extent of a Barely Prehistoric Earthquake on the Bear River Normal Fault, Wyoming and Utah

    Science.gov (United States)

    Hecker, S.; Schwartz, D. P.; Cinti, F. R.; Civico, R.; West, M. W.; Stoller, A. R.; DeLong, S.; Pickering, A.

    2016-12-01

    To better constrain the length of a young prehistoric (significantly post-AD 1630) surface-rupturing earthquake recently discovered near the south end of the Bear River normal fault in Utah (Hecker and Schwartz, 2015), we excavated a trench on a strand of the fault 25 km to the north in Wyoming, where previous work had found clear evidence of two older late Holocene events (West, 1993). These two events, which have been identified to the south as well, were interpreted as comprising the entire history of this very young fault. The new trench across the 5-m-high scarp at the northern site exposed a 6-m-wide zone of faulting and two packages of colluvial-wedge deposits, each tabular and 0.5-1 m thick. The colluvial deposits, which bury Pleistocene alluvial deposits that in turn overlie Eocene bedrock, appear correlative with West's two-event stratigraphy. In the latest trench, however, both wedges are faulted, with strands extending to the ground surface, evidence of a third, younger event. The amount of displacement in the most recent event (MRE) in the trench is small (few 10s of cm at most) and distributed and has resulted in only minor colluviation. The event record is complicated by a shallow slope failure in the soil A-horizon on the scarp that we interpret as possibly occurring during the MRE. The slide formed a head scarp at a location underlain by MRE faulting and built a low bench at least 100 m long on the surface below the scarp. We sampled buried in-place soil below the slide for radiocarbon analysis, which should allow age comparison with the earthquake identified farther south. Ultra-high-resolution topography from balloon photography and terrestrial lidar enable detailed morphologic study of surface processes and deformation at the site.

  5. Stable isotopes to detect food-conditioned bears and to evaluate human-bear management

    Science.gov (United States)

    Hopkins, John B.; Koch, Paul L.; Schwartz, Charles C.; Ferguson, Jake M.; Greenleaf, Schuyler S.; Kalinowski, Steven T.

    2012-01-01

    We used genetic and stable isotope analysis of hair from free-ranging black bears (Ursus americanus) in Yosemite National Park, California, USA to: 1) identify bears that consume human food, 2) estimate the diets of these bears, and 3) evaluate the Yosemite human–bear management program. Specifically, we analyzed the isotopic composition of hair from bears known a priori to be food-conditioned or non-food-conditioned and used these data to predict whether bears with an unknown management status were food-conditioned (FC) or non-food-conditioned (NFC). We used a stable isotope mixing model to estimate the proportional contribution of natural foods (plants and animals) versus human food in the diets of FC bears. We then used results from both analyses to evaluate proactive (population-level) and reactive (individual-level) human–bear management, and discussed new metrics to evaluate the overall human–bear management program in Yosemite. Our results indicated that 19 out of 145 (13%) unknown bears sampled from 2005 to 2007 were food-conditioned. The proportion of human food in the diets of known FC bears likely declined from 2001–2003 to 2005–2007, suggesting proactive management was successful in reducing the amount of human food available to bears. In contrast, reactive management was not successful in changing the management status of known FC bears to NFC bears, or in reducing the contribution of human food to the diets of FC bears. Nine known FC bears were recaptured on 14 occasions from 2001 to 2007; all bears were classified as FC during subsequent recaptures, and human–bear management did not reduce the amount of human food in the diets of FC bears. Based on our results, we suggest Yosemite continue implementing proactive human–bear management, reevaluate reactive management, and consider removing problem bears (those involved in repeated bear incidents) from the population.

  6. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    Directory of Open Access Journals (Sweden)

    Wonhee Lee

    2014-02-01

    Full Text Available A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT. Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU.

  7. Latest Progress of Fault Detection and Localization in Complex Electrical Engineering

    Science.gov (United States)

    Zhao, Zheng; Wang, Can; Zhang, Yagang; Sun, Yi

    2014-01-01

    In the researches of complex electrical engineering, efficient fault detection and localization schemes are essential to quickly detect and locate faults so that appropriate and timely corrective mitigating and maintenance actions can be taken. In this paper, under the current measurement precision of PMU, we will put forward a new type of fault detection and localization technology based on fault factor feature extraction. Lots of simulating experiments indicate that, although there are disturbances of white Gaussian stochastic noise, based on fault factor feature extraction principal, the fault detection and localization results are still accurate and reliable, which also identifies that the fault detection and localization technology has strong anti-interference ability and great redundancy.

  8. Delineating Concealed Faults within Cogdell Oil Field via Earthquake Detection

    Science.gov (United States)

    Aiken, C.; Walter, J. I.; Brudzinski, M.; Skoumal, R.; Savvaidis, A.; Frohlich, C.; Borgfeldt, T.; Dotray, P.

    2016-12-01

    Cogdell oil field, located within the Permian Basin of western Texas, has experienced several earthquakes ranging from magnitude 1.7 to 4.6, most of which were recorded since 2006. Using the Earthscope USArray, Gan and Frohlich [2013] relocated some of these events and found a positive correlation in the timing of increased earthquake activity and increased CO2 injection volume. However, focal depths of these earthquakes are unknown due to 70 km station spacing of the USArray. Accurate focal depths as well as new detections can delineate subsurface faults and establish whether earthquakes are occurring in the shallow sediments or in the deeper basement. To delineate subsurface fault(s) in this region, we first detect earthquakes not currently listed in the USGS catalog by applying continuous waveform-template matching algorithms to multiple seismic data sets. We utilize seismic data spanning the time frame of 2006 to 2016 - which includes data from the U.S. Geological Survey Global Seismographic Network, the USArray, and the Sweetwater, TX broadband and nodal array located 20-40 km away. The catalog of earthquakes enhanced by template matching reveals events that were well recorded by the large-N Sweetwater array, so we are experimenting with strategies for optimizing template matching using different configurations of many stations. Since earthquake activity in the Cogdell oil field is on-going (a magnitude 2.6 occurred on May 29, 2016), a temporary deployment of TexNet seismometers has been planned for the immediate vicinity of Cogdell oil field in August 2016. Results on focal depths and detection of small magnitude events are pending this small local network deployment.

  9. Robust Nonlinear Analytic Redundancy for Fault Detection and Isolation in Mobile Robot

    Institute of Scientific and Technical Information of China (English)

    Bibhrajit Halder; Nilanjan Sarkar

    2007-01-01

    A robust nonlinear analytical redundancy (RNLAR) technique is presented to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch (MPM) and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors (PRV), which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are then transformed into a set of structured residual vectors (SRV) for fault isolation. Experimental results on a Pioneer 3-DX mobile robot are presented to justify the effectiveness of the RNLAR scheme.

  10. Fault detection and isolation of sensors in aeration control systems.

    Science.gov (United States)

    Carlsson, Bengt; Zambrano, Jesús

    2016-01-01

    In this paper, we consider the problem of fault detection (FD) and isolation in the aeration system of an activated sludge process. For this study, the dissolved oxygen in each aerated zone is assumed to be controlled automatically. As the basis for an FD method we use the ratio of air flow rates into different zones. The method is evaluated in two scenarios: using the Benchmark Simulation Model no. 1 (BSM1) by Monte Carlo simulations and using data from a wastewater treatment plant. The FD method shows good results for a correct and early FD and isolation.

  11. Internal Leakage Fault Detection and Tolerant Control of Single-Rod Hydraulic Actuators

    Directory of Open Access Journals (Sweden)

    Jianyong Yao

    2014-01-01

    Full Text Available The integration of internal leakage fault detection and tolerant control for single-rod hydraulic actuators is present in this paper. Fault detection is a potential technique to provide efficient condition monitoring and/or preventive maintenance, and fault tolerant control is a critical method to improve the safety and reliability of hydraulic servo systems. Based on quadratic Lyapunov functions, a performance-oriented fault detection method is proposed, which has a simple structure and is prone to implement in practice. The main feature is that, when a prescribed performance index is satisfied (even a slight fault has occurred, there is no fault alarmed; otherwise (i.e., a severe fault has occurred, the fault is detected and then a fault tolerant controller is activated. The proposed tolerant controller, which is based on the parameter adaptive methodology, is also prone to realize, and the learning mechanism is simple since only the internal leakage is considered in parameter adaptation and thus the persistent exciting (PE condition is easily satisfied. After the activation of the fault tolerant controller, the control performance is gradually recovered. Simulation results on a hydraulic servo system with both abrupt and incipient internal leakage fault demonstrate the effectiveness of the proposed fault detection and tolerant control method.

  12. Detecting Hidden Faults and Other Lineations with UAVSAR

    Science.gov (United States)

    Parker, J. W.; Glasscoe, M. T.; Donnellan, A.

    2013-12-01

    Jay Parker, Margaret Glasscoe, Andrea Donnellan Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA The M7.2 El Mayor Cucapah Earthquake of April 4, 2010 is the main earthquake to date observed by the NASA UAVSAR. By observing with repeat passes (October 2009, April 2010 captures the coseismic strain pattern, and subsequent flights capture the postseismic process) over the adjoining portion of California, the interferometric phase maps of geodetic displacements are exceptionally high definition (pixel size is roughly 7 m) records of the extended deformation field from the earthquake process, including revelation of a rich network of plate parallel and conjugate faulting, apparently slipping sympathetically to the earthquake-induced quasistatic changes in stress. While the most significant of these faults have been documented by cooperative use of UAVSAR maps and field research, a subsequent opportunity arises: to use this data to develop and validate an automated approach to detecting faults and other lineations directly from the UAVSAR unwrapped phase product that corresponds to a single-component deformation map. The Canny edge detection algorithm is employed, after a preparation stage to clean the data. This preprocessing step is tailored to the nature of the radar phase data: data dropouts in single pixels and extended areas (blown sand dunes, farms) are a much larger problem than background white noise. Blocks of typically 3x3 pixels are currently reduced to a single value, the average after bad pixels are discarded. The smoothing methods typically used with the Canny method are minimized (smoothing makes data drop-out problems worse). The aperture size that determines a gradient estimation is chosen large (7 vs. the typical 3), as this is found to produce continuous (rather than dashed) lineations. The main Canny threshold is chosen to correspond to a user selected slip threshold in mm. Reasonable maps of lineations in the Salton

  13. DPHM: A FAULT DETECTION PROTOCOL BASED ON HEARTBEAT OF MULTIPLE MASTER-NODES

    Institute of Scientific and Technical Information of China (English)

    Dong Jian; Zuo Decheng; Liu Hongwei; Yang Xiaozong

    2007-01-01

    In most of fault detection algorithms of distributed system, fault model is restricted to fault of process, and link failure is simply masked, or modeled by process failure. Both methods can soon use up system resource and potentially reduce the availability of system. A fault Detection Protocol based on Heartbeat of multiple Master-nodes (DPHM) is proposed, which can immediately and accurately detect and locate faulty links by adopting voting and electing mechanism among master-nodes. Thus,DPHM can effectively improve availability of system. In addition, in contrast with other detection protocols, DPHM reduces greatly the detection cost due to the structure of master-nodes.

  14. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

    Science.gov (United States)

    Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies.

  15. FSN-based fault modelling for fault detection and troubleshooting in CANDU stations

    Energy Technology Data Exchange (ETDEWEB)

    Nasimi, E., E-mail: elnara.nasimi@brucepower.com [Bruce Power LLP., Tiverton, Ontario(Canada); Gabbar, H.A. [Univ. of Ontario Inst. of Tech., Oshawa, Ontario (Canada)

    2013-07-01

    An accurate fault modeling and troubleshooting methodology is required to aid in making risk-informed decisions related to design and operational activities of current and future generation of CANDU designs. This paper presents fault modeling approach using Fault Semantic Network (FSN) methodology with risk estimation. Its application is demonstrated using a case study of Bruce B zone-control level oscillations. (author)

  16. Active Fault Detection and Isolation for Hybrid Systems

    DEFF Research Database (Denmark)

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas

    2009-01-01

    An algorithm for active fault detection and isolation is proposed. In order to observe the failure hidden due to the normal operation of the controllers or the systems, an optimization problem based on minimization of test signal is used. The optimization based method imposes the normal and fault...... to the two tank bench mark example in presence of two faults....

  17. Fault Detection and Isolation for a Supermarket Refrigeration System - Part One

    DEFF Research Database (Denmark)

    Yang, Zhenyu; Rasmussen, Karsten B.; Kieu, Anh T.

    2011-01-01

    Fault Detection and Isolation (FDI) using the Kalman Filter (KF) technique for a supermarket refrigeration system is explored. Four types of sensor fault scenarios, namely drift, offset, freeze and hard-over, are considered for two temperature sensors, and one type of parametric fault scenario...

  18. FAULT DETECTION AND LOCALIZATION IN MOTORCYCLES BASED ON THE CHAIN CODE OF PSEUDOSPECTRA AND ACOUSTIC SIGNALS

    Directory of Open Access Journals (Sweden)

    B. S. Anami

    2013-06-01

    Full Text Available Vehicles produce sound signals with varying temporal and spectral properties under different working conditions. These sounds are indicative of the condition of the engine. Fault diagnosis is a significantly difficult task in geographically remote places where expertise is scarce. Automated fault diagnosis can assist riders to assess the health condition of their vehicles. This paper presents a method for fault detection and location in motorcycles based on the chain code of the pseudospectra and Mel-frequency cepstral coefficient (MFCC features of acoustic signals. The work comprises two stages: fault detection and fault location. The fault detection stage uses the chain code of the pseudospectrum as a feature vector. If the motorcycle is identified as faulty, the MFCCs of the same sample are computed and used as features for fault location. Both stages employ dynamic time warping for the classification of faults. Five types of faults in motorcycles are considered in this work. Observed classification rates are over 90% for the fault detection stage and over 94% for the fault location stage. The work identifies other interesting applications in the development of acoustic fingerprints for fault diagnosis of machinery, tuning of musical instruments, medical diagnosis, etc.

  19. Fault Detection and Isolation Using Analytical Redundancy Relations for the Ship Propulsion Benchmark

    DEFF Research Database (Denmark)

    Izadi-Zamanabadi, Roozbeh

    The prime objective of Fault-tolerant Control (FTC) systems is to handle faults and discrepancies using appropriate accommodation policies. The issue of obtaining information about various parameters and signals, which have to be monitored for fault detection purposes, becomes a rigorous task wit...... is illustrated on the ship propulsion benchmark....

  20. Detection of faults in rotating machinery using periodic time-frequency sparsity

    Science.gov (United States)

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

    2016-11-01

    This paper addresses the problem of extracting periodic oscillatory features in vibration signals for detecting faults in rotating machinery. To extract the feature, we propose an approach in the short-time Fourier transform (STFT) domain where the periodic oscillatory feature manifests itself as a relatively sparse grid. To estimate the sparse grid, we formulate an optimization problem using customized binary weights in the regularizer, where the weights are formulated to promote periodicity. In order to solve the proposed optimization problem, we develop an algorithm called augmented Lagrangian majorization-minimization algorithm, which combines the split augmented Lagrangian shrinkage algorithm (SALSA) with majorization-minimization (MM), and is guaranteed to converge for both convex and non-convex formulation. As examples, the proposed approach is applied to simulated data, and used as a tool for diagnosing faults in bearings and gearboxes for real data, and compared to some state-of-the-art methods. The results show that the proposed approach can effectively detect and extract the periodical oscillatory features.

  1. Gyroscope Pivot Bearing Dimension and Surface Defect Detection

    Directory of Open Access Journals (Sweden)

    Xudong Li

    2011-03-01

    Full Text Available Because of the perceived lack of systematic analysis in illumination system design processes and a lack of criteria for design methods in vision detection a method for the design of a task-oriented illumination system is proposed. After detecting the micro-defects of a gyroscope pivot bearing with a high curvature glabrous surface and analyzing the characteristics of the surface detection and reflection model, a complex illumination system with coaxial and ring lights is proposed. The illumination system is then optimized based on the analysis of illuminance uniformity of target regions by simulation and grey scale uniformity and articulation that are calculated from grey imagery. Currently, in order to apply the Pulse Coupled Neural Network (PCNN method, structural parameters must be tested and adjusted repeatedly. Therefore, this paper proposes the use of a particle swarm optimization (PSO algorithm, in which the maximum between cluster variance rules is used as fitness function with a linearily reduced inertia factor. This algorithm is used to adaptively set PCNN connection coefficients and dynamic threshold, which avoids algorithmic precocity and local oscillations. The proposed method is used for pivot bearing defect image processing. The segmentation results of the maximum entropy and minimum error method and the one described in this paper are compared using buffer region matching, and the experimental results show that the method of this paper is effective.

  2. Gyroscope pivot bearing dimension and surface defect detection.

    Science.gov (United States)

    Ge, Wenqian; Zhao, Huijie; Li, Xudong

    2011-01-01

    Because of the perceived lack of systematic analysis in illumination system design processes and a lack of criteria for design methods in vision detection a method for the design of a task-oriented illumination system is proposed. After detecting the micro-defects of a gyroscope pivot bearing with a high curvature glabrous surface and analyzing the characteristics of the surface detection and reflection model, a complex illumination system with coaxial and ring lights is proposed. The illumination system is then optimized based on the analysis of illuminance uniformity of target regions by simulation and grey scale uniformity and articulation that are calculated from grey imagery. Currently, in order to apply the Pulse Coupled Neural Network (PCNN) method, structural parameters must be tested and adjusted repeatedly. Therefore, this paper proposes the use of a particle swarm optimization (PSO) algorithm, in which the maximum between cluster variance rules is used as fitness function with a linearily reduced inertia factor. This algorithm is used to adaptively set PCNN connection coefficients and dynamic threshold, which avoids algorithmic precocity and local oscillations. The proposed method is used for pivot bearing defect image processing. The segmentation results of the maximum entropy and minimum error method and the one described in this paper are compared using buffer region matching, and the experimental results show that the method of this paper is effective.

  3. Fault detection in processes represented by PLS models using an EWMA control scheme

    KAUST Repository

    Harrou, Fouzi

    2016-10-20

    Fault detection is important for effective and safe process operation. Partial least squares (PLS) has been used successfully in fault detection for multivariate processes with highly correlated variables. However, the conventional PLS-based detection metrics, such as the Hotelling\\'s T and the Q statistics are not well suited to detect small faults because they only use information about the process in the most recent observation. Exponentially weighed moving average (EWMA), however, has been shown to be more sensitive to small shifts in the mean of process variables. In this paper, a PLS-based EWMA fault detection method is proposed for monitoring processes represented by PLS models. The performance of the proposed method is compared with that of the traditional PLS-based fault detection method through a simulated example involving various fault scenarios that could be encountered in real processes. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS method.

  4. RESEARCH ON EXPERT SYSTEM OF FAULT DETECTION AND DIAGNOSING FOR PNEUMATIC SYSTEM OF AUTOMATIC PRODUCTION LINE

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Fault detection and diagnosis for pneumatic system of automatic production line are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosis instrument are designed. The mathematical model of various pneumatic faults and experimental device are built. In the end, some experiments are done, which shows that the expert system using fuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.

  5. Fault Detection, Isolation, and Accommodation for LTI Systems Based on GIMC Structure

    Directory of Open Access Journals (Sweden)

    D. U. Campos-Delgado

    2008-01-01

    Full Text Available In this contribution, an active fault-tolerant scheme that achieves fault detection, isolation, and accommodation is developed for LTI systems. Faults and perturbations are considered as additive signals that modify the state or output equations. The accommodation scheme is based on the generalized internal model control architecture recently proposed for fault-tolerant control. In order to improve the performance after a fault, the compensation is considered in two steps according with a fault detection and isolation algorithm. After a fault scenario is detected, a general fault compensator is activated. Finally, once the fault is isolated, a specific compensator is introduced. In this setup, multiple faults could be treated simultaneously since their effect is additive. Design strategies for a nominal condition and under model uncertainty are presented in the paper. In addition, performance indices are also introduced to evaluate the resulting fault-tolerant scheme for detection, isolation, and accommodation. Hard thresholds are suggested for detection and isolation purposes, meanwhile, adaptive ones are considered under model uncertainty to reduce the conservativeness. A complete simulation evaluation is carried out for a DC motor setup.

  6. Faults detection approach using PCA and SOM algorithm in PMSG-WT system

    Directory of Open Access Journals (Sweden)

    Mohamed Lamine FADDA

    2016-07-01

    Full Text Available In this paper, a new approach for faults detection in observable data system wind turbine - permanent magnet synchronous generator (WT-PMSG, the studying objective, illustrate the combination (SOM-PCA to build Multi-local-PCA models faults detection in system (WT-PMSG, the performance of the method suggested to faults detection in system data, finding good results in simulation experiment.

  7. Study on Fault Detection and PCB Picture-Location Method of Logic Circuit

    Directory of Open Access Journals (Sweden)

    Mingping Xia

    2013-05-01

    Full Text Available The main content of logic circuit fault detection includes describing circuit to be diagnosed, determining fault and circuit initial information, generating circuit location test set. In this study, LASAR is used to carry out the logic circuit simulation so as to create such documents as fault dictionary, node truth value table, etc. for the preparation of fault detection. Due to the limitation of circuit observability and testing vectors, the diagnosis program can not accurately locate the fault just once in the process of diagnosis because the circuit is complex and users are not quite familiarity with the circuit. Therefore, the new circuit-fault-detection technology incorporates techniques of PCB picture-location so that the users can locate the fault quickly and accurately.

  8. A Novel Arc Fault Detector for Early Detection of Electrical Fires

    Directory of Open Access Journals (Sweden)

    Kai Yang

    2016-04-01

    Full Text Available Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires.

  9. A new private communication scheme based on the idea of fault detection and identification

    Energy Technology Data Exchange (ETDEWEB)

    Chen Maoyin [Department of Automation, Tsinghua University, Beijing 100084 (China)]. E-mail: maoyinchen@163.com; Zhou Donghua [Department of Automation, Tsinghua University, Beijing 100084 (China); Shang Yun [College of Mathematics and Information Science, Shaanxi Normal University, Xi' an 710062 (China)

    2006-02-27

    By use of the idea of fault detection and identification, this Letter proposes a new scheme to resolve the problem of chaotic private communication. From the point of view of fault detection and identification the scalar message signal hidden in the chaotic systems can be regarded as the component fault signal, thereby it can be detected and recovered using the model-based methods of fault detection and identification. The famous Duffing oscillator is used to illustrate and verify the effectiveness of this scheme.

  10. Discrete wavelet transform-based fault diagnosis for driving system of pipeline detection robot arm

    Institute of Scientific and Technical Information of China (English)

    Deng Huiyu; Wang Xinli; Ma Peisun

    2005-01-01

    A real-time wavelet multi-resolution analysis (MRA)-based fault detection algorithm is proposed. The first stage detailed MRA signals extracted from the original signals were used as the criteria for fault detection. By measuring sharp variations in the detailed MRA signals, faults in the motor driving system of pipeline detection robot arm could be detected. The fault type was then identified by comparison of the three-phase MRA sharp variations. The effects of the faults were examined. The simulation results show that this algorithm is effective and robust, it is promising for fault detection in a robot's joint driving system. The method is simple, rapid and it can operate in real time.

  11. Design of H(infinity) robust fault detection filter for linear uncertain time-delay systems.

    Science.gov (United States)

    Bai, Leishi; Tian, Zuohua; Shi, Songjiao

    2006-10-01

    In this paper, the robust fault detection filter design problem for linear time-delay systems with both unknown inputs and parameter uncertainties is studied. Using a multiobjective optimization technique, a new performance index is introduced, which takes into account the robustness of the fault detection filter against disturbances and sensitivity to faults simultaneously. The reference residual model is then designed based on this performance index to formulate the robust fault detection filter design problem as an H(infinity) model-matching problem. By applying robust H(infinity) optimization control technique, the existence condition of the robust fault detection filter for linear time-delay systems with both unknown inputs and parameter uncertainties is presented in terms of linear matrix inequality formulation, independently of time delay. In order to detect the fault, an adaptive threshold which depends on the inputs is finally determined. An illustrative design example is used to demonstrate the validity of the proposed approach.

  12. The Influence of Exotic Calcite on the Mechanical Behavior of Quartz Bearing Fault Gouge

    Science.gov (United States)

    Carpenter, B. M.; Di Stefano, G.; Collettini, C.

    2014-12-01

    The interseismic recovery of frictional strength is a fundamental part of the seismic cycle. This restrengthening, and related phenomena, plays a key role in determining the stability and mode of tectonic faulting. Recent experimental data has shown that gouge mineralogy has a strong influence on the rate of frictional healing, with calcite-dominated gouges showing the highest rates. Combining these data with widespread observations of calcite as cement or veins in non-carbonate hosted faults, indicates that the presence of calcite within a fault gouge could play an important role in shallow- and mid-crustal earthquakes. We report on laboratory experiments designed to explore the mechanical behavior of quartz/calcite mixtures as a means to better understand the evolution of fault behavior in faults where carbonate materials are present. We sheared mixtures of powdered Carrara marble (>98% CaCO3) and disaggregated Ottawa sand (99.8% SiO2) at constant normal stress of 5 MPa under saturated conditions at room temperature. We performed slide-hold-slide tests, 1-3,000 seconds, and velocity stepping tests, 0.1-1000 μm/s, to measure the amount of frictional healing and velocity dependence of friction respectively. Small subsets of experiments were conducted at different boundary conditions. Preliminary results show that the presence of calcite in quartz-based fault gouge has a hardening effect, both in overall frictional strength, where the strength of our mixtures increases with increasing calcite content, and in single experiments, where mixtures with low percentages of calcite show a consistent strain-hardening trend. We also observe that the rates of frictional healing and creep relaxation increase with increasing calcite content. Finally, our results show that the addition of as little as 2.5% calcite within a fault gouge results in a 30% increase in the rate of frictional healing, with further increases in calcite content resulting in larger increases in the rate

  13. A Component Based Approach to Industrial Fault Detection and Accommodation

    DEFF Research Database (Denmark)

    Blanke, M.

    1996-01-01

    This paper presents a new method for design of fault handling as a supervisory part of a control system.......This paper presents a new method for design of fault handling as a supervisory part of a control system....

  14. Industrial Actuator Benchmark for Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Blanke, M.; Patton, R.J.

    1995-01-01

    Feedback control systems are vulnerable to faults within the control loop, because feedback actions may cause abrupt responses and......Feedback control systems are vulnerable to faults within the control loop, because feedback actions may cause abrupt responses and...

  15. Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications

    Data.gov (United States)

    National Aeronautics and Space Administration — Sensor faults continue to be a major hurdle for sys- tems health management to reach its full potential. At the same time, few recorded instances of sensor faults...

  16. Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids.

    Science.gov (United States)

    Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

    2017-04-28

    Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability.

  17. Detection of Partial Demagnetization Fault in PMSMs Operating under Nonstationary Conditions

    DEFF Research Database (Denmark)

    Wang, Chao; Delgado Prieto, Miguel; Romeral, Luis

    2016-01-01

    Demagnetization fault detection of in-service Permanent Magnet Synchronous Machines (PMSMs) is a challenging task because most PMSMs operate under nonstationary circumstances in industrial applications. A novel approach based on tracking characteristic orders of stator current using Vold-Kalman F......Demagnetization fault detection of in-service Permanent Magnet Synchronous Machines (PMSMs) is a challenging task because most PMSMs operate under nonstationary circumstances in industrial applications. A novel approach based on tracking characteristic orders of stator current using Vold......-Kalman Filter is proposed to detect the partial demagnetization fault in PMSMs running at nonstationary conditions. Amplitude of envelope of the fault characteristic orders is used as fault indictor. Experimental results verify the superiority of the proposed method on partial demagnetization online fault...... detection of PMSMs under various speed and load conditions....

  18. Nonlinear Statistical Process Monitoring and Fault Detection Using Kernel ICA

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xi; YAN Wei-wu; ZHAO Xu; SHAO Hui-he

    2007-01-01

    A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis (ICA) is proposed. The kernel ICA method is a two-phase algorithm: whitened kernel principal component (KPCA) plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed process monitoring method based on kernel ICA can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.

  19. On Line Current Monitoring and Application of a Residual Method for Eccentricity Fault Detection

    Directory of Open Access Journals (Sweden)

    METATLA, A.

    2011-02-01

    Full Text Available This work concerns the monitoring and diagnosis of faults in induction motors. We develop an approach based on residual analysis of stator currents to detect and diagnose faults eccentricity static, dynamic and mixed in three phase induction motor. To simulate the behavior of motor failure, a model is proposed based on the approach of magnetically coupled coils. The simulation results show the importance of the approach applied for the detection and diagnosis of fault in three phase induction motor.

  20. Fault detection and diagnosis in a food pasteurization process with Hidden Markov Models

    OpenAIRE

    Tokatlı, Figen; Cinar, Ali

    2004-01-01

    Hidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a ...

  1. Analytical Model-based Fault Detection and Isolation in Control Systems

    DEFF Research Database (Denmark)

    Vukic, Z.; Ozbolt, H.; Blanke, M.

    1998-01-01

    The paper gives an introduction and an overview of the field of fault detection and isolation for control systems. The summary of analytical (quantitative model-based) methodds and their implementation are presented. The focus is given to mthe analytical model-based fault-detection and fault...... diagnosis methods, often viewed as the classical or deterministic ones. Emphasis is placed on the algorithms suitable for ship automation, unmanned underwater vehicles, and other systems of automatic control....

  2. Actuator Fault Detection for Sampled-Data Systems in H∞ Setting

    Institute of Scientific and Technical Information of China (English)

    YANG Xiao-jun; WENG Zheng-xin; TIAN Zuo-hua

    2005-01-01

    Actuator fault detection for sampled-data systems was investigated from the viewpoint of jump systems.With the aid of a prior frequency information on fault, such a problem is converted to an augmented H∞ filtering problem. A simple state-space approach is then proposed todeal with sampled-data actuator fault detection problem. Compared with the existed approaches, the proposed approach allows parameters of the sampled-data system being time-varying with consideration of measurement noise.

  3. Wavelet Based Fault Detection, Classification in Transmission System with TCSC Controllers

    Directory of Open Access Journals (Sweden)

    G.Satyanarayana,

    2015-08-01

    Full Text Available This paper presents simulation results of the application of distance relays for the protection of transmission systems employing flexible alternating current transmission controllers such as Thyristor Controlled Series Capacitor (TCSC. The complete digital simulation of TCSC within a transmission system is performed in the MATLAB/Simulink environment using the Power System Block set (PSB. This paper presents an efficient method based on wavelet transforms both fault detection and classification which is almost independent of fault impedance, fault location and fault inception angle of transmission line fault currents with FACTS controllers.

  4. A Survey on Distributed Filtering and Fault Detection for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hongli Dong

    2014-01-01

    Full Text Available In recent years, theoretical and practical research on large-scale networked systems has gained an increasing attention from multiple disciplines including engineering, computer science, and mathematics. Lying in the core part of the area are the distributed estimation and fault detection problems that have recently been attracting growing research interests. In particular, an urgent need has arisen to understand the effects of distributed information structures on filtering and fault detection in sensor networks. In this paper, a bibliographical review is provided on distributed filtering and fault detection problems over sensor networks. The algorithms employed to study the distributed filtering and detection problems are categorised and then discussed. In addition, some recent advances on distributed detection problems for faulty sensors and fault events are also summarized in great detail. Finally, we conclude the paper by outlining future research challenges for distributed filtering and fault detection for sensor networks.

  5. A New Fault-tolerant Switched Reluctance Motor with reliable fault detection capability

    DEFF Research Database (Denmark)

    Lu, Kaiyuan

    2014-01-01

    while no extra search coil is actually needed. The motor itself is able to continue to work under any faulted conditions, providing fault-tolerant features. The working principle, performance evaluation of this motor will be demonstrated in this paper and Finite Element Analysis results are provided....

  6. New algorithm to detect modules in a fault tree for a PSA

    Energy Technology Data Exchange (ETDEWEB)

    Jung, Woo Sik [Sejong University, Seoul (Korea, Republic of)

    2015-05-15

    A module or independent subtree is a part of a fault tree whose child gates or basic events are not repeated in the remaining part of the fault tree. Modules are necessarily employed in order to reduce the computational costs of fault tree quantification. This paper presents a new linear time algorithm to detect modules of large fault trees. The size of cut sets can be substantially reduced by replacing independent subtrees in a fault tree with super-components. Chatterjee and Birnbaum developed properties of modules, and demonstrated their use in the fault tree analysis. Locks expanded the concept of modules to non-coherent fault trees. Independent subtrees were manually identified while coding a fault tree for computer analysis. However, nowadays, the independent subtrees are automatically identified by the fault tree solver. A Dutuit and Rauzy (DR) algorithm to detect modules of a fault tree for coherent or non-coherent fault tree was proposed in 1996. It has been well known that this algorithm quickly detects modules since it is a linear time algorithm. The new algorithm minimizes computational memory and quickly detects modules. Furthermore, it can be easily implemented into industry fault tree solvers that are based on traditional Boolean algebra, binary decision diagrams (BDDs), or Zero-suppressed BDDs. The new algorithm employs only two scalar variables in Eqs. to that are volatile information. After finishing the traversal and module detection of each node, the volatile information is destroyed. Thus, the new algorithm does not employ any other additional computational memory and operations. It is recommended that this method be implemented into fault tree solvers for efficient probabilistic safety assessment (PSA) of nuclear power plants.

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

    Science.gov (United States)

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

    2016-09-01

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

  8. Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Bingyong Yan

    2015-01-01

    Full Text Available A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach.

  9. Minimum Error Entropy Filter for Fault Detection of Networked Control Systems

    OpenAIRE

    Guolian Hou; Mifeng Ren; Lilong Du; Jianhua Zhang

    2012-01-01

    In this paper, fault detection of networked control systems with random delays, packet dropout and noises is studied. The filter is designed using a minimum error entropy criterion. The residual generated by the filter is then evaluated to detect faults in networked control systems. An illustrative networked control system is used to verify the effectiveness of the proposed approach.

  10. Minimum Error Entropy Filter for Fault Detection of Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Guolian Hou

    2012-03-01

    Full Text Available In this paper, fault detection of networked control systems with random delays, packet dropout and noises is studied. The filter is designed using a minimum error entropy criterion. The residual generated by the filter is then evaluated to detect faults in networked control systems. An illustrative networked control system is used to verify the effectiveness of the proposed approach.

  11. Wind Turbine Fault Detection based on Artificial Neural Network Analysis of SCADA Data

    DEFF Research Database (Denmark)

    Herp, Jürgen; S. Nadimi, Esmaeil

    2015-01-01

    Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wind...

  12. A PLL-based resampling technique for vibration analysis in variable-speed wind turbines with PMSG: A bearing fault case

    Science.gov (United States)

    Pezzani, Carlos M.; Bossio, José M.; Castellino, Ariel M.; Bossio, Guillermo R.; De Angelo, Cristian H.

    2017-02-01

    Condition monitoring in permanent magnet synchronous machines has gained interest due to the increasing use in applications such as electric traction and power generation. Particularly in wind power generation, non-invasive condition monitoring techniques are of great importance. Usually, in such applications the access to the generator is complex and costly, while unexpected breakdowns results in high repair costs. This paper presents a technique which allows using vibration analysis for bearing fault detection in permanent magnet synchronous generators used in wind turbines. Given that in wind power applications the generator rotational speed may vary during normal operation, it is necessary to use special sampling techniques to apply spectral analysis of mechanical vibrations. In this work, a resampling technique based on order tracking without measuring the rotor position is proposed. To synchronize sampling with rotor position, an estimation of the rotor position obtained from the angle of the voltage vector is proposed. This angle is obtained from a phase-locked loop synchronized with the generator voltages. The proposed strategy is validated by laboratory experimental results obtained from a permanent magnet synchronous generator. Results with single point defects in the outer race of a bearing under variable speed and load conditions are presented.

  13. Real-time fault detection method based on belief rule base for aircraft navigation system

    Institute of Scientific and Technical Information of China (English)

    Zhao Xin; Wang Shicheng; Zhang Jinsheng; Fan Zhiliang; Min Haibo

    2013-01-01

    Real-time and accurate fault detection is essential to enhance the aircraft navigation system's reliability and safety.The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults.On account of this reason,we propose an online detection solution based on non-analytical model.In this article,the navigation system fault detection model is established based on belief rule base (BRB),where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output.To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update,a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization (EM) algorithm.Furthermore,the proposed method is verified by navigation experiment.Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model.The output of the detection model can track the fault state very well,and the faults can be diagnosed in real time and accurately.In addition,the detection ability,especially in the probability of false detection,is superior to offline optimization method,and thus the system reliability has great improvement.

  14. Construction of adaptive redundant multiwavelet packet and its application to compound faults detection of rotating machinery

    Institute of Scientific and Technical Information of China (English)

    CHEN JingLong; ZI YanYang; HE ZhengJia; WANG XiaoDong

    2012-01-01

    It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under non-stationary operation turns to be a challenging task.Multiwavelet with two or more base functions may match two or more features of compound faults,which may supply a possible solution to compound faults detection.However,the fixed basis functions of multiwavelet transform,which are not related with the vibration signal,may reduce the accuracy of compound faults detection.Moreover,the decomposition results of multiwavelet transform not being own time-invariant is harmful to extract the features of periodical impulses.Furthermore,multiwavelet transform only focuses on the multi-resolution analysis in the low frequency band,and may leave out the useful features of compound faults.To overcome these shortcomings,a novel method called adaptive redundant multiwavelet packet (ARMP) is proposed based on the two-scale similarity transforms.Besides,the relative energy ratio at the characteristic frequency of the concerned component is computed to select the sensitive frequency bands of multiwavelet packet coefficients.The proposed method was used to analyze the compound faults of rolling element beating.The results showed that the proposed method could enhance the ability of compound faults detection of rotating machinery.

  15. Fault Detection and Isolation of Wind Energy Conversion Systems using Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    N. Talebi

    2014-07-01

    Full Text Available Reliability of Wind Energy Conversion Systems (WECSs is greatly important regarding to extract the maximum amount of available wind energy. In order to accurately study WECSs during occurrence of faults and to explore the impact of faults on each component of WECSs, a detailed model is required in which mechanical and electrical parts of WECSs are properly involved. In addition, a Fault Detection and Isolation System (FDIS is required by which occurred faults can be diagnosed at the appropriate time in order to ensure safe system operation and avoid heavy economic losses. This can be performed by subsequent actions through fast and accurate detection and isolation of faults. In this paper, by utilizing a comprehensive dynamic model of the WECS, an FDIS is presented using dynamic recurrent neural networks. In industrial processes, dynamic neural networks are known as a good mathematical tool for fault detection. Simulation results show that the proposed FDIS detects faults of the generator's angular velocity sensor, pitch angle sensors and pitch actuators appropriately. The suggested FDIS is capable to detect and isolate the faults shortly while owing very low false alarms rate. The presented FDIS scheme can be used to identify faults in other parts of the WECS.

  16. Auto-OBSD: Automatic parameter selection for reliable Oscillatory Behavior-based Signal Decomposition with an application to bearing fault signature extraction

    Science.gov (United States)

    Huang, Huan; Baddour, Natalie; Liang, Ming

    2017-03-01

    Bearing signals are often contaminated by in-band interferences and random noise. Oscillatory Behavior-based Signal Decomposition (OBSD) is a new technique which decomposes a signal according to its oscillatory behavior, rather than frequency or scale. Due to the low oscillatory transients of bearing fault-induced signals, the OBSD can be used to effectively extract bearing fault signatures from a blurred signal. However, the quality of the result highly relies on the selection of method-related parameters. Such parameters are often subjectively selected and a systematic approach has not been reported in the literature. As such, this paper proposes a systematic approach to automatic selection of OBSD parameters for reliable extraction of bearing fault signatures. The OBSD utilizes the idea of Morphological Component Analysis (MCA) that optimally projects the original signal to low oscillatory wavelets and high oscillatory wavelets established via the Tunable Q-factor Wavelet Transform (TQWT). In this paper, the effects of the selection of each parameter on the performance of the OBSD for bearing fault signature extraction are investigated. It is found that some method-related parameters can be fixed at certain values due to the nature of bearing fault-induced impulses. To adaptively tune the remaining parameters, index-guided parameter selection algorithms are proposed. A Convergence Index (CI) is proposed and a CI-guided self-tuning algorithm is developed to tune the convergence-related parameters, namely, penalty factor and number of iterations. Furthermore, a Smoothness Index (SI) is employed to measure the effectiveness of the extracted low oscillatory component (i.e. bearing fault signature). It is shown that a minimum SI implies an optimal result with respect to the adjustment of relevant parameters. Thus, two SI-guided automatic parameter selection algorithms are also developed to specify two other parameters, i.e., Q-factor of high-oscillatory wavelets and

  17. Robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels

    Institute of Scientific and Technical Information of China (English)

    Niu Erzhuo; Wang Qing; Dong Chaoyang

    2014-01-01

    The observer-based robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels and norm bounded modeling uncertainties are addressed. The network of unmanned vehicles is modeled as a discrete-time uncertain Markovian jump system. Based on the model, a residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of linear matrix inequality. Furthermore, a time domain optimization approach is proposed to improve the performance of the fault detection system. The problem of detecting small faults can be formulated as an optimization problem and its solution is given. For preventing false alarms, a new adaptive threshold function is established. The combined fault detection and optimization algorithm and the adaptive threshold are then applied to a network of highly maneuverable technology vehicles to illustrate the effective-ness of the proposed approach.

  18. Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input

    Directory of Open Access Journals (Sweden)

    Zhang Wei

    2017-01-01

    Full Text Available Periodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D temporal vibration signal into a 2-D image. With the signal image, convolutional Neural Network (CNN is used to train the raw vibration data. As powerful feature extractor and classifier for image recognition, CNN can learn to acquire features most suitable for the classification task by being trained. With the image format of vibration signals, the neuron in fully-connected layer of CNN can see farther and capture the periodic feature of signals. According to the results of the experiments, when fed in enough training samples, the proposed method outperforms other common methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems.

  19. A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

    Science.gov (United States)

    Kim, Pyung Soo

    2017-04-01

    In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noisefree systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, extensive simulations are performed for the discretized DC motor system with two types of sensor faults, incipient soft bias-type fault and abrupt bias-type fault. In particular, according to diverse noise levels and windows lengths, meaningful simulation results are given for the abrupt bias-type fault.

  20. Application of H-Infinity Fault Detection to Model-Scale Autonomous Aircraft

    Science.gov (United States)

    Vasconcelos, J. F.; Rosa, P.; Kerr, Murray; Latorre Sierra, Antonio; Recupero, Cristina; Hernandez, Lucia

    2015-09-01

    This paper describes the development of a fault detection system for a model scale autonomous aircraft. The considered fault scenario is defined by malfunctions in the elevator, namely bias and stuck-in-place of the surface. The H∞ design methodology is adopted, with an LFT description of the aircraft longitudinal dynamics, that allows for fault detection explicitly synthesized for a wide range of operating airspeeds. The obtained filter is validated in two stages: in a Functional Engineering Simulator (FES), providing preliminary results of the filter performance; and with experimental data, collected in field tests with actual injection of faults in the elevator surface.

  1. Fault detection of a Five-Phase Permanent-Magnet Machine

    DEFF Research Database (Denmark)

    Bianchini, Claudio; Matzen, Torben N.; Bianchi, Nicola;

    2008-01-01

    The paper focuses on the fault detection of a five-phase Permanent-Magnet (PM) machine. This machine has been de-signed for fault tolerant applications, and it is characterised by a mutual inductance equal to zero and a high self inductance, with the purpose to limit the short circuit current....... The effects of a limited number of short-circuited turns were investigated by theoretical and Finite Element (FE) analysis, and then a procedure for fault detection has been proposed, focusing on the severity of the fault (i.e. the number of short-circuited turns and the related current)....

  2. Machine Fault Signature Analysis

    Directory of Open Access Journals (Sweden)

    Pratesh Jayaswal

    2008-01-01

    Full Text Available The objective of this paper is to present recent developments in the field of machine fault signature analysis with particular regard to vibration analysis. The different types of faults that can be identified from the vibration signature analysis are, for example, gear fault, rolling contact bearing fault, journal bearing fault, flexible coupling faults, and electrical machine fault. It is not the intention of the authors to attempt to provide a detailed coverage of all the faults while detailed consideration is given to the subject of the rolling element bearing fault signature analysis.

  3. Early Oscillation Detection for DC/DC Converter Fault Diagnosis

    Science.gov (United States)

    Wang, Bright L.

    2011-01-01

    The electrical power system of a spacecraft plays a very critical role for space mission success. Such a modern power system may contain numerous hybrid DC/DC converters both inside the power system electronics (PSE) units and onboard most of the flight electronics modules. One of the faulty conditions for DC/DC converter that poses serious threats to mission safety is the random occurrence of oscillation related to inherent instability characteristics of the DC/DC converters and design deficiency of the power systems. To ensure the highest reliability of the power system, oscillations in any form shall be promptly detected during part level testing, system integration tests, flight health monitoring, and on-board fault diagnosis. The popular gain/phase margin analysis method is capable of predicting stability levels of DC/DC converters, but it is limited only to verification of designs and to part-level testing on some of the models. This method has to inject noise signals into the control loop circuitry as required, thus, interrupts the DC/DC converter's normal operation and increases risks of degrading and damaging the flight unit. A novel technique to detect oscillations at early stage for flight hybrid DC/DC converters was developed.

  4. A New UKF Based Fault Detection Method in Non-linear Systems

    Institute of Scientific and Technical Information of China (English)

    GE Zhe-xue; YANG Yong-min; HU Zheng

    2006-01-01

    To detect the bias fault in stochastic non-linear dynamic systems, a new Unscented Kalman Filtering(UKF) based real-time recursion detection method is brought forward with the consideration of the flaws of traditional Extended Kalman Filtering(EKF). It uses the UKF as the residual generation method and the Weighted-Sum Squared Residual (WSSR) as the fault detection strategy. The simulation results are provided which demonstrate better effectiveness and a higher detection ratio of the developed methods.

  5. Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

    Directory of Open Access Journals (Sweden)

    Mehdi Shadaram

    2010-10-01

    Full Text Available In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG. The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.

  6. Fault detection and identification based on combining logic and model in a wall-climbing robot

    Institute of Scientific and Technical Information of China (English)

    Yong JIANG; Hongguang WANG; Lijin FANG; Mingyang ZHAO

    2009-01-01

    A combined logic- and model-based approach to fault detection and identification (FDI) in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis. Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system by evaluating the basic events (elementary causes), which can lead to a root event (a particular fault). Then, a multiple-model adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI of the robot.

  7. Final Technical Report Recovery Act: Online Nonintrusive Condition Monitoring and Fault Detection for Wind Turbines

    Energy Technology Data Exchange (ETDEWEB)

    Wei Qiao

    2012-05-29

    characteristic frequencies in the current or current demodulated signals, where 1P stands for the shaft rotating frequency of a WTG; (4) Developed a wavelet filter for effective signature extraction of wind turbine faults without characteristic frequencies in the current or current demodulated signals; (5) Developed an effective adaptive noise cancellation method as an alternative to the wavelet filter method for signature extraction of wind turbine faults without characteristic frequencies in the current or current demodulated signals; (6) Developed a statistical analysis-based impulse detection method for effective fault signature extraction and evaluation of WTGs based on the 1P-invariant PSD of the current or current demodulated signals; (7) Validated the proposed current-based wind turbine CMFD technologies through extensive computer simulations and experiments for small direct-drive WTGs without gearboxes; and (8) Showed, through extensive experiments for small direct-drive WTGs, that the performance of the proposed current-based wind turbine CMFD technologies is comparable to traditional vibration-based methods. The proposed technologies have been successfully applied for detection of major failures in blades, shafts, bearings, and generators of small direct-drive WTGs. The proposed technologies can be easily integrated into existing wind turbine control, protection, and monitoring systems and can be implemented remotely from the wind turbines being monitored. The proposed technologies provide an alternative to vibration-sensor-based CMFD. This will reduce the cost and hardware complexity of wind turbine CMFD systems. The proposed technologies can also be combined with vibration-sensor-based methods to improve the accuracy and reliability of wind turbine CMFD systems. When there are problems with sensors, the proposed technologies will ensure proper CMFD for the wind turbines, including their sensing systems. In conclusion, the proposed technologies offer an effective

  8. A correlation based fault detection method for short circuits in battery packs

    Science.gov (United States)

    Xia, Bing; Shang, Yunlong; Nguyen, Truong; Mi, Chris

    2017-01-01

    This paper presents a fault detection method for short circuits based on the correlation coefficient of voltage curves. The proposed method utilizes the direct voltage measurements from the battery cells, and does not require any additional hardware or effort in modeling during fault detection. Moreover, the inherent mathematical properties of the correlation coefficient ensure the robustness of this method as the battery pack ages or is imbalanced in real applications. In order to apply this method online, the recursive moving window correlation coefficient calculation is adopted to maintain the detection sensitivity to faults during operation. An additive square wave is designed to prevent false positive detections when the batteries are at rest. The fault isolation can be achieved by identifying the overlapped cell in the correlation coefficients with fault flags. Simulation and experimental results validated the feasibility and demonstrated the advantages of this method.

  9. A Novel Approach for Eccentricity Fault Detection in Squirrel Cage Induction Motors

    Directory of Open Access Journals (Sweden)

    Mehdi Ahmadi

    2013-01-01

    Full Text Available In this paper, static eccentricity fault detection in induction motors is studied. Two dimensional finite element method (2D-FEM is used for faultless and eccentric condition modeling in induction motors. Also current and speed signals are compared in two experimental and simulation cases for model validating. For fault detection, fast Fourier transform is used at first. In this method, high order harmonics with small amplitude can alarms the fault occurrence. For this reason, the fault detection process is difficult.To overcome these drawbacks, it is suggested that two test coils contrive around the air-gap. So, any changes in air-gap can be detected easily. Moreover this test coils are used in open circuit case. So, these test coils do not effect on motor dynamics. Also, the results show that modulated voltage can be alarm the fault occurrence, type and percent well.

  10. Application of a Fault Detection and Isolation System on a Rotary Machine

    Directory of Open Access Journals (Sweden)

    Silvia M. Zanoli

    2013-01-01

    Full Text Available The paper illustrates the design and the implementation of a Fault Detection and Isolation (FDI system to a rotary machine like a multishaft centrifugal compressor. A model-free approach, that is, the Principal Component Analysis (PCA, has been employed to solve the fault detection issue. For the fault isolation purpose structured residuals have been adopted while an adaptive threshold has been designed in order to detect and to isolate the faults. To prove the goodness of the proposed FDI system, historical data of a nitrogen centrifugal compressor employed in a refinery plant are considered. Tests results show that detection and isolation of single as well as multiple faults are successfully achieved.

  11. A first approach on fault detection and isolation for cardiovascular anomalies detection

    KAUST Repository

    Ledezma, Fernando

    2015-07-01

    In this paper, we use an extended version of the cardiovascular system\\'s state space model presented by [1] and propose a fault detection and isolation methodology to study the problem of detecting cardiovascular anomalies that can originate from variations in physiological parameters and deviations in the performance of the heart\\'s mitral and aortic valves. An observer-based approach is discussed as the basis of the method. The approach contemplates a bank of Extended Kalman Filters to achieve joint estimation of the model\\'s states and parameters and to detect malfunctions in the valves\\' performance. © 2015 American Automatic Control Council.

  12. Fault Detection and Isolation for a Supermarket Refrigeration System - Part One

    DEFF Research Database (Denmark)

    Yang, Zhenyu; Rasmussen, Karsten B.; Kieu, Anh T.;

    2011-01-01

    Fault Detection and Isolation (FDI) using the Kalman Filter (KF) technique for a supermarket refrigeration system is explored. Four types of sensor fault scenarios, namely drift, offset, freeze and hard-over, are considered for two temperature sensors, and one type of parametric fault scenario, n....... The test results show that the EKF-based FDI method generally performances better and faster than the KF-based method does. However, both methods can not handle the isolation between sensor faults and parametric fault.......Fault Detection and Isolation (FDI) using the Kalman Filter (KF) technique for a supermarket refrigeration system is explored. Four types of sensor fault scenarios, namely drift, offset, freeze and hard-over, are considered for two temperature sensors, and one type of parametric fault scenario...... isolation purpose, a bank of KFs arranged by splitting measurements is constructed for sensor fault isolation, while the Multi-Model Adaptive Estimation (MMAE) method is employed to handle parametric fault isolation. All these approaches are extended and checked by using Extended KF technique afterwards...

  13. Fault detection and diagnosis for refrigerator from compressor sensor

    Energy Technology Data Exchange (ETDEWEB)

    Keres, Stephen L.; Gomes, Alberto Regio; Litch, Andrew D.

    2016-12-06

    A refrigerator, a sealed refrigerant system, and method are provided where the refrigerator includes at least a refrigerated compartment and a sealed refrigerant system including an evaporator, a compressor, a condenser, a controller, an evaporator fan, and a condenser fan. The method includes monitoring a frequency of the compressor, and identifying a fault condition in the at least one component of the refrigerant sealed system in response to the compressor frequency. The method may further comprise calculating a compressor frequency rate based upon the rate of change of the compressor frequency, wherein a fault in the condenser fan is identified if the compressor frequency rate is positive and exceeds a condenser fan fault threshold rate, and wherein a fault in the evaporator fan is identified if the compressor frequency rate is negative and exceeds an evaporator fan fault threshold rate.

  14. Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

    KAUST Repository

    Harrou, Fouzi

    2017-09-18

    This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array\\'s residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.

  15. Aircraft Fault Detection Using Real-Time Frequency Response Estimation

    Science.gov (United States)

    Grauer, Jared A.

    2016-01-01

    A real-time method for estimating time-varying aircraft frequency responses from input and output measurements was demonstrated. The Bat-4 subscale airplane was used with NASA Langley Research Center's AirSTAR unmanned aerial flight test facility to conduct flight tests and collect data for dynamic modeling. Orthogonal phase-optimized multisine inputs, summed with pilot stick and pedal inputs, were used to excite the responses. The aircraft was tested in its normal configuration and with emulated failures, which included a stuck left ruddervator and an increased command path latency. No prior knowledge of a dynamic model was used or available for the estimation. The longitudinal short period dynamics were investigated in this work. Time-varying frequency responses and stability margins were tracked well using a 20 second sliding window of data, as compared to a post-flight analysis using output error parameter estimation and a low-order equivalent system model. This method could be used in a real-time fault detection system, or for other applications of dynamic modeling such as real-time verification of stability margins during envelope expansion tests.

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

    Science.gov (United States)

    Wua, Jianjun; Tanb, Songlin

    2002-01-01

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

  17. New method for online interturn faults detection in power transformer with using probabilistic neural network

    Directory of Open Access Journals (Sweden)

    S. hajiaghasi

    2014-07-01

    Full Text Available In recent years with notice increase reliability in power system and Intelligent Systems and also notice that transformers are one of the main part of the transmission and distribution systems, online monitoring of these equipment in power system are require. In this paper, a new method for online interturn fault detection base on leakage flux in power transformer are propose. When an interturn fault occur the symmetry of flux destruction and leakage flux increase or decrease and for various location and severity of fault leakage flux is different and it can be used for fault detection. In this paper for measure these flux we using search coils that mounted on HV winding. To fault detection and classify we using probabilistic neural network. and for decrease the information volume PCA is used. The simulation results are compare and verified with experimental result and show that this propose method is very good.

  18. Design of parametric fault detection systems:An H-infinity optimization approach

    Institute of Scientific and Technical Information of China (English)

    Maiying ZHONG; Chuanfeng MA; Steven X.DING

    2005-01-01

    Problems related to the design of observer-based parametric fault detection (PFD) systems are studied.The core of our study is to first describe the faults occurring in system actuators,sensors and components in the form of additive parameter deviations,then to transform the PFD problems into a similar additive fault setup,based on which an optimal observer-based optimization fault detection approach is proposed.A constructive solution optimal in the sense of minimizing a certain performance index is developed.The main results consist of defining parametric fault detectability,formulating a PFD optimization problem and its solution.A numerical example to demonstrate the effectiveness of the proposed approach is provided.

  19. Wind Turbine Fault Detection based on Artificial Neural Network Analysis of SCADA Data

    DEFF Research Database (Denmark)

    Herp, Jürgen; S. Nadimi, Esmaeil

    2015-01-01

    Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wi...... detection upon a generalized-likelihood-test. An upper and a lower control bounds are established for x and y respectively, given a minimum false alarm probability η based on the statistical characteristics of the data....

  20. 基于小波包熵的轴承状态监测和早期故障诊断技术%Fault Diagnosis of Rolling Bearing Based on Wavelet Energy Entropy

    Institute of Scientific and Technical Information of China (English)

    冯桓榰; 张来斌; 石帅; 梁伟

    2013-01-01

    Rolling beating is one of the most important components in rotary machines.The objection of the study is to detect the failure early and diagnosis the fault mode accurately.Wavelet energy entropy is introduced to bearing condition monitoring and least square support vector machine (LS-SVM) is used for fault diagnosis.In order to validate the effectiveness of the proposed method,a bearing accelerated life test is performed on the accelerated bearing life tester.The results show that wavelet energy entropy has better performance and can forecast fault development earlier compared to conventional signal features.LS-SVM method can distinguish beating fault modes well.%滚动轴承是旋转机械中最常用的轴承之一.但其早期故障诊断仍是亟待解决的问题.研究旨在通过基于小波包熵的状态监测技术提早发现轴承的早期故障,进而利用最小二乘支持向量机(LS-SVM)精确诊断轴承的故障类型.利用加速轴承寿命试验台的轴承全寿命数据和故障轴承数据验证了方法的有效性和实用性.结果表明,相比传统监测指标,小波包熵有较好的早期预警能力;结合小波相对能量指标,LS-SVM能快速有效地诊断滚动轴承早期故障.

  1. FABRIC FAULT DETECTION USING IMAGE PROCESSING WITH ATMEL MICROCONTROLLER

    Directory of Open Access Journals (Sweden)

    R.THILEPA,

    2010-11-01

    Full Text Available In this paper it is explained how a faulty fabric image is processed through a microcontroller. If there is any fault how it is identified through this ATMEL microcontroller by the circuit operation. For programming the controller Atmel program is used. In addition to that a stepper motor is connected in the output. This motor operates if there is no fault and fails if the fault is identified. This can be implemented in states like Tamilnadu so that the textile field’s income may be increased in an enormous way which in turn raises the country’s income [7].

  2. Fault detection and diagnosis for compliance monitoring in international supply chains

    NARCIS (Netherlands)

    Wang, Yuxin; Tian, Yifu; Teixeira, André; Hulstijn, Joris; Tan, Yao-Hua

    Currently international supply chains are facing risks concerning faults in compliance, such as altering shipping documentations, fictitious inventory, and inter-company manipulations. In this paper a method to detect and diagnose fault scenarios regarding customs compliance in supply chains is

  3. Fault detection and diagnosis for compliance monitoring in international supply chains

    NARCIS (Netherlands)

    Wang, Yuxin; Tian, Yifu; Teixeira, André; Hulstijn, Joris; Tan, Yao-Hua

    2016-01-01

    Currently international supply chains are facing risks concerning faults in compliance, such as altering shipping documentations, fictitious inventory, and inter-company manipulations. In this paper a method to detect and diagnose fault scenarios regarding customs compliance in supply chains is prop

  4. Design of a Fault Detection and Isolation System for Intelligent Vehicle Navigation System

    Directory of Open Access Journals (Sweden)

    Wei Huang

    2015-01-01

    Full Text Available This paper deals with the design of a fault detection and isolation (FDI system for an intelligent vehicle, a vehicle equipped with advanced driver assistance system (ADAS. The ADASs are outfitted with sensors for acquiring various information about the vehicle and its surroundings. Since these sensors are sensitive to faults, an efficient FDI system should be developed. The designed FDI system is comprised of three parts: a detection part, a decision part, and a fault management part. The detection part applies a generalized observer scheme (GOS. In the GOS, there is bank of extended Kalman filters (EKFs, each excited by all except one sensor measurement. The residual generated from the measurement update of each EKF is therefore sensitive to all sensor faults but one. This way, the fault sensitivity pattern of the residual makes it possible to detect a fault and locate the faulty sensor. The designed FDI system has been implemented and tested off-line with actual experiment data. Good results have been obtained with diagnosing individual sensor faults and outputting fault-free vehicle states.

  5. Robust fault detection in bond graph framework using interval analysis and Fourier-Motzkin elimination technique

    Science.gov (United States)

    Jha, Mayank Shekhar; Chatti, Nizar; Declerck, Philippe

    2017-09-01

    This paper addresses the fault diagnosis problem of uncertain systems in the context of Bond Graph modelling technique. The main objective is to enhance the fault detection step based on Interval valued Analytical Redundancy Relations (named I-ARR) in order to overcome the problems related to false alarms, missed alarms and robustness issues. These I-ARRs are a set of fault indicators that generate the interval bounds called thresholds. A fault is detected once the nominal residuals (point valued part of I-ARRs) exceed the thresholds. However, the existing fault detection method is limited to parametric faults and it presents various limitations with regards to estimation of measurement signal derivatives, to which I-ARRs are sensitive. The novelties and scientific interest of the proposed methodology are: (1) to improve the accuracy of the measurements derivatives estimation by using a dedicated sliding mode differentiator proposed in this work, (2) to suitably integrate the Fourier-Motzkin Elimination (FME) technique within the I-ARRs based diagnosis so that measurements faults can be detected successfully. The latter provides interval bounds over the derivatives which are included in the thresholds. The proposed methodology is studied under various scenarios (parametric and measurement faults) via simulations over a mechatronic torsion bar system.

  6. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2011-01-01

    This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal ...

  7. Sensor fault detection and isolation over wireless sensor network based on hardware redundancy

    Science.gov (United States)

    Hao, Jingjing; Kinnaert, Michel

    2017-01-01

    In order to diagnose sensor faults with small magnitude in wireless sensor networks, distinguishability measures are defined to indicate the performance for fault detection and isolation (FDI) at each node. A systematic method is then proposed to determine the information to be exchanged between nodes to achieve FDI specifications while limiting the computation complexity and communication cost.

  8. DETECTION OF INCIPIENT LOCALIZED GEAR FAULTS IN GEARBOX BY COMPLEX CONTINUOUS WAVELET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    Han Zhennan; Xiong Shibo; Li Jinbao

    2003-01-01

    As far as the vibration signal processing is concerned, composition of vibration signal resulting from incipient localized faults in gearbox is too weak to be detected by traditional detecting technology available now. The method, which includes two steps: vibration signal from gearbox is first processed by synchronous average sampling technique and then it is analyzed by complex continuous wavelet transform to diagnose gear fault, is introduced. Two different kinds of faults in the gearbox, i.e.shaft eccentricity and initial crack in tooth fillet, are detected and distinguished from each other successfully.

  9. Observer-based and Regression Model-based Detection of Emerging Faults in Coal Mills

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Lin, Bao; Jørgensen, Sten Bay

    2006-01-01

    In order to improve the reliability of power plants it is important to detect fault as fast as possible. Doing this it is interesting to find the most efficient method. Since modeling of large scale systems is time consuming it is interesting to compare a model-based method with data driven ones....... In this paper three different fault detection approaches are compared using a example of a coal mill, where a fault emerges. The compared methods are based on: an optimal unknown input observer, static and dynamic regression model-based detections. The conclusion on the comparison is that observer-based scheme...

  10. A New Method for the Detections of Multiple Faults Using Binary Decision Diagrams

    Institute of Scientific and Technical Information of China (English)

    PAN Zhongliang; CHEN Ling; ZHANG Guangzhao

    2006-01-01

    With the complexity of integrated circuits is continually increasing, a local defect in circuits may cause multiple faults. The behavior of a digital circuit with a multiple fault may significantly differ from that of a single fault. A new method for the detection of multiple faults in digital circuits is presented in this paper, the method is based on binary decision diagram (BDD). First of all, the BDDs for the normal circuit and faulty circuit are built respectively. Secondly, a test BDD is obtained by the XOR operation of the BDDs corresponds to normal circuit and faulty circuit. In the test BDD, each input assignment that leads to the leaf node labeled 1 is a test vector of multiple faults. Therefore, the test set of multiple faults is generated by searching for the type of input assignments in the test BDD. Experimental results on some digital circuits show the feasibility of the approach presented in this paper.

  11. Rotor Faults Detection in Induction Motor by Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Neelam Mehala

    2009-12-01

    Full Text Available Motor current signature analysis has been successfully used for fault diagnosis in induction motors. However, this method does not always achieve good results when the speed or the load torque is not constant, because this cause variation on the motor slip and fast Fourier transform problems appear due to non-stationary signal. This paper experimentally describes the effects of rotor broken bar fault in the stator current of induction motor operating under non-constant load conditions. To achieve this, broken rotor bar fault is eplicated in a laboratory and its effect on the motor current has been studied. To diagnose the broken rotor bar fault, a new approach based on wavelet transform is applied by using ‘Labview 8.2 software’ of National Instrument (NI. The diagnosis procedure was performed by using the virtual instruments. The theoretical basis of proposed method is proved by laboratory tests.

  12. Application of D-S Evidence Fusion Method in the Fault Detection of Temperature Sensor

    Directory of Open Access Journals (Sweden)

    Zheng Dou

    2014-01-01

    Full Text Available Due to the complexity and dangerousness of drying process, the fault detection of temperature sensor is very difficult and dangerous in actual working practice and the detection effectiveness is not satisfying. For this problem, in this paper, based on the idea of information fusion and the requirements of D-S evidence method, a D-S evidence fusion structure with two layers was introduced to detect the temperature sensor fault in drying process. The first layer was data layer to establish the basic belief assignment function of evidence which could be realized by BP Neural Network. The second layer was decision layer to detect and locate the sensor fault which could be realized by D-S evidence fusion method. According to the numerical simulation results, the working conditions of sensors could be described effectively and accurately by this method, so that it could be used to detect and locate the sensor fault.

  13. Wayside Bearing Fault Diagnosis Based on Envelope Analysis Paved with Time-Domain Interpolation Resampling and Weighted-Correlation-Coefficient-Guided Stochastic Resonance

    Directory of Open Access Journals (Sweden)

    Yongbin Liu

    2017-01-01

    Full Text Available Envelope spectrum analysis is a simple, effective, and classic method for bearing fault identification. However, in the wayside acoustic health monitoring system, owing to the high relative moving speed between the railway vehicle and the wayside mounted microphone, the recorded signal is embedded with Doppler effect, which brings in shift and expansion of the bearing fault characteristic frequency (FCF. What is more, the background noise is relatively heavy, which makes it difficult to identify the FCF. To solve the two problems, this study introduces solutions for the wayside acoustic fault diagnosis of train bearing based on Doppler effect reduction using the improved time-domain interpolation resampling (TIR method and diagnosis-relevant information enhancement using Weighted-Correlation-Coefficient-Guided Stochastic Resonance (WCCSR method. First, the traditional TIR method is improved by incorporating the original method with kinematic parameter estimation based on time-frequency analysis and curve fitting. Based on the estimated parameters, the Doppler effect is removed using the TIR easily. Second, WCCSR is employed to enhance the diagnosis-relevant period signal component in the obtained Doppler-free signal. Finally, paved with the above two procedures, the local fault is identified using envelope spectrum analysis. Simulated and experimental cases have verified the effectiveness of the proposed method.

  14. Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

    Directory of Open Access Journals (Sweden)

    A. Moosavian

    2013-01-01

    Full Text Available Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC engine based on power spectral density (PSD technique and two classifiers, namely, K-nearest neighbor (KNN and artificial neural network (ANN. Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

  15. DYNAMIC SOFTWARE TESTING MODELS WITH PROBABILISTIC PARAMETERS FOR FAULT DETECTION AND ERLANG DISTRIBUTION FOR FAULT RESOLUTION DURATION

    Directory of Open Access Journals (Sweden)

    A. D. Khomonenko

    2016-07-01

    Full Text Available Subject of Research.Software reliability and test planning models are studied taking into account the probabilistic nature of error detection and discovering. Modeling of software testing enables to plan the resources and final quality at early stages of project execution. Methods. Two dynamic models of processes (strategies are suggested for software testing, using error detection probability for each software module. The Erlang distribution is used for arbitrary distribution approximation of fault resolution duration. The exponential distribution is used for approximation of fault resolution discovering. For each strategy, modified labeled graphs are built, along with differential equation systems and their numerical solutions. The latter makes it possible to compute probabilistic characteristics of the test processes and states: probability states, distribution functions for fault detection and elimination, mathematical expectations of random variables, amount of detected or fixed errors. Evaluation of Results. Probabilistic characteristics for software development projects were calculated using suggested models. The strategies have been compared by their quality indexes. Required debugging time to achieve the specified quality goals was calculated. The calculation results are used for time and resources planning for new projects. Practical Relevance. The proposed models give the possibility to use the reliability estimates for each individual module. The Erlang approximation removes restrictions on the use of arbitrary time distribution for fault resolution duration. It improves the accuracy of software test process modeling and helps to take into account the viability (power of the tests. With the use of these models we can search for ways to improve software reliability by generating tests which detect errors with the highest probability.

  16. APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    Based on radial basis function (RBF) neural networks, the healthy working model of each sub-system of robot in FMS is established. A new approach to fault on-line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi-layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS.

  17. Detection of bearing defects in three-phase induction motors using Park’s transform and radial basis function neural networks

    Indian Academy of Sciences (India)

    Izzet Y Önel; K Burak Dalci; İbrahim Senol

    2006-06-01

    This paper investigates the application of induction motor stator current signature analysis (MCSA) using Park’s transform for the detection of rolling element bearing damages in three-phase induction motor. The paper first discusses bearing faults and Park’s transform, and then gives a brief overview of the radial basis function (RBF) neural networks algorithm. Finally, system information and the experimental results are presented. Data acquisition and Park’s transform algorithm are achieved by using LabVIEW and the neural network algorithm is achieved by using MATLAB programming language. Experimental results show that it is possible to detect bearing damage in induction motors using an ANN algorithm.

  18. ASCS online fault detection and isolation based on an improved MPCA

    Science.gov (United States)

    Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

    2014-09-01

    Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

  19. ASCS Online Fault Detection and Isolation Based on an Improved MPCA

    Institute of Scientific and Technical Information of China (English)

    PENG Jianxin; LIU Haiou; HU Yuhui; XI Junqiang; CHEN Huiyan

    2014-01-01

    Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling (T2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

  20. Mine-hoist fault-condition detection based on the wavelet packet transform and kernel PCA

    Institute of Scientific and Technical Information of China (English)

    XIA Shi-xiong; NIU Qiang; ZHOU Yong; ZHANG Lei

    2008-01-01

    A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Principal Component Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization. It is suitable for analysing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimension feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification.

  1. Dynamic Reconstruction-Based Fuzzy Neural Network Method for Fault Detection in Chaotic System

    Institute of Scientific and Technical Information of China (English)

    YANG Hongying; YE Hao; WANG Guizeng

    2008-01-01

    This paper presents a method for detecting weak fault signals in chaotic systems based on the chaotic dynamics reconstruction technique and the fuzzy neural system (FNS). The Grassberger-Procaccia algorithm and least squares regression were used to calculate the correlation dimension for the model order estimate. Based on the model order, an appropriately structured FNS model was designed to predict system faults. Through reasonable analysis of predicted errors, the disturbed signal can be extracted efficiently and correctly from the chaotic background. Satisfactory results were obtained by using several kinds of simula-tive faults which were extracted from the practical chaotic fault systems. Experimental results demonstra tethat the proposed approach has good prediction accuracy and can deal with data having a -40 dB signal to noise ratio (SNR). The low SNR requirement makes the approach a powerful tool for early fault detection.

  2. Fault Detection and Diagnosis in Process Data Using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Fang Wu

    2014-01-01

    Full Text Available For the complex industrial process, it has become increasingly challenging to effectively diagnose complicated faults. In this paper, a combined measure of the original Support Vector Machine (SVM and Principal Component Analysis (PCA is provided to carry out the fault classification, and compare its result with what is based on SVM-RFE (Recursive Feature Elimination method. RFE is used for feature extraction, and PCA is utilized to project the original data onto a lower dimensional space. PCA T2, SPE statistics, and original SVM are proposed to detect the faults. Some common faults of the Tennessee Eastman Process (TEP are analyzed in terms of the practical system and reflections of the dataset. PCA-SVM and SVM-RFE can effectively detect and diagnose these common faults. In RFE algorithm, all variables are decreasingly ordered according to their contributions. The classification accuracy rate is improved by choosing a reasonable number of features.

  3. 基于LMD和MED的滚动轴承故障特征提取方法%Fault feature extraction method for rolling element bearings based on LMD and MED

    Institute of Scientific and Technical Information of China (English)

    周士帅; 窦东阳; 薛斌

    2016-01-01

    components embedded in the original signal. These included the cyclic impulse response, deterministic component and noise. However, the cyclic impulse responses were always submerged by noises and they were helpless to make a decision of fault. The MED filter was adopted to search for an optimum set of filter coefficients that recover the output signal (of an inverse filter) with the maximum value of kurtosis. The MED filter was capable of deconvolution of the periodic impulsive excitations from a mixture of response signals and thus enhanced the impulses arising from spalls and cracks in rolling bearings. The MED filter can also be used to remove most noises. Therefore, the former four PFs were further processed by the MED to enhance the fault impulse information. At last,the signal after processed by the LMD and MED was analyzed by envelop analysis. Through this envelop spectrum,the fault features were ultimately extracted. Experimental investigation of 6205-2RS JEM SKF bearings with rolling element defects was performed. The vibration data were obtained from a test rig for simulating various bearing faults in an electrical engineering lab of the Case Western Reserve University. Single point defects were introduced to the test bearings by the electro-discharge machining with the diameters of 0.177 8 mm. The faulty bearings were installed in the drive end, but the accelerometers were placed at the fan end, so the noise was very strong. Using our LMD-MED method, the fault features were successfully extracted. We concluded based on the experiment that the fep index, which indicates the ratio of the peak value at the fault characteristic frequency versus the mean value of the spectrum in 200 Hz band, was increased by 96.4% compared with the original signal. At the same time, the signal-to-noise ratio (SNR) was raised by approximately 18.3% after the signal processing by the LMD and MED. The experiment results proved that the method was effective to detect and extract the fault

  4. Patellar position in weight-bearing radiographs compared with non-weight-bearing: significance for the detection of osteoarthritis.

    Science.gov (United States)

    Skou, Nikolaj; Egund, Niels

    2017-03-01

    Background Diagnosis and treatment of patellofemoral disorders including osteoarthritis are currently often based on imaging and clinical assessment with patients in the supine position. Purpose To evaluate differences in patellar position in the trochlear groove and to assess the detection of medial and lateral patellofemoral (PF) osteoarthritis (OA) on axial radiographs in supine and standing positions, respectively. Material and Methods Thirty-five women and 23 men (mean age, 56 years; age range, 18-87 years) referred for routine radiographic examinations of the knees were included. Axial radiographs of the PF joint in both supine non-weight-bearing and standing weight-bearing position in 30° knee flexion were obtained of 111 knees. Measurements performed on the radiographs: patellar tilt, patellar displacement, joint space width, and grade of OA according to Ahlbäck. Results From supine to standing position the patella moved medially and medial joint space width and lateral patellar tilt angle decreased ( P < 0.0001 for the three measured parameters). In the standing position, medial PF OA was observed in 19 knees compared to three knees in the supine position. Fourteen knees had lateral PF OA with almost unchanged grade of OA irrespective of position. Conclusion In weight-bearing positions, the patella is positioned medially in the trochlear groove compared to supine non-weight-bearing positions. Therefore, this study suggests that the common occurrence of medial PF OA can generally not be detected on axial radiographs in supine non-weight-bearing positions and confirms the importance of imaging the PF joint in standing weight-bearing positions.

  5. Automatic Supervision And Fault Detection In PV System By Wireless Sensors With Interfacing By Labview Program

    Directory of Open Access Journals (Sweden)

    Yousra M Abbas

    2015-08-01

    Full Text Available In this work a wireless monitoring system are designed for automatic detection localization fault in photovoltaic system. In order to avoid the use of modeling and simulation of the PV system we detected the fault by monitoring the output of each individual photovoltaic panel connected in the system by Arduino and transmit this data wirelessly to laptop then interface it by LabVIEW program which made comparison between this data and the measured data taking from reference module at the same condition. The proposed method is very simple but effective detecting and diagnosing the main faults of a PV system and was experimentally validated and has demonstrated its effectiveness in the detection and diagnosing of main faults present in the DC side of PV system.

  6. Unweighted Betweenness Centrality for Critical Fault Detection for Cascading Outage Assessment

    DEFF Research Database (Denmark)

    Petersen, Pauli Fríðheim; Jóhannsson, Hjörtur; Nielsen, Arne Hejde

    2016-01-01

    This paper analyses the possible use of unweighted betweenness centrality instead of weighted betweenness centrality, for critical fault detection for assessment of cascading failures. As unweighted betweenness centrality is significantly faster to compute, the possible use of this will significa...

  7. Framework for the Design and Implementation of Fault Detection and Isolation Project

    Data.gov (United States)

    National Aeronautics and Space Administration — SySense, Inc. proposes to develop a framework for the design and implementation of fault detection and isolation (FDI) systems. The framework will include protocols...

  8. Model-based fault detection of blade pitch system in floating wind turbines

    Science.gov (United States)

    Cho, S.; Gao, Z.; Moan, T.

    2016-09-01

    This paper presents a model-based scheme for fault detection of a blade pitch system in floating wind turbines. A blade pitch system is one of the most critical components due to its effect on the operational safety and the dynamics of wind turbines. Faults in this system should be detected at the early stage to prevent failures. To detect faults of blade pitch actuators and sensors, an appropriate observer should be designed to estimate the states of the system. Residuals are generated by a Kalman filter and a threshold based on H optimization, and linear matrix inequality (LMI) is used for residual evaluation. The proposed method is demonstrated in a case study that bias and fixed output in pitch sensors and stuck in pitch actuators. The simulation results show that the proposed method detects different realistic fault scenarios of wind turbines under the stochastic external winds.

  9. Fault Detection and Isolation of Satellite Formations using a Ground Station Project

    Data.gov (United States)

    National Aeronautics and Space Administration — This proposal is for the development a fault detection and isolation (FDI) algorithm for a formation of satellites but processed at a ground station. The algorithm...

  10. Fault detection for a class of Markov jump systems with unknown disturbances

    Institute of Scientific and Technical Information of China (English)

    Shuping HE; Fei LIU

    2009-01-01

    An optimized fault detection observer is designed for a class of Markov jump systems with unknown disturbances.By reconstructing the system,the residual error dynamic characteristics of unknown input and fault signals,including unknown disturbances and modeling error are obtained.The energy norm indexes of disturbance and fault signals of the residual error are selected separately to reflect the restraint of disturbance and the sensitivity of faults,and the design of the fault detection observer is described as an optimization problem.By using the constructed Lyapunov function and linear matrix inequalities,a sufficient condition that the solution to the fault detection observer exists is given and proved,and an optimized design approach is presented.The designed observer makes the systems have stochastic stability and better capability of restraining disturbances,and the given norm index is satisfied.Simulation results demonstrate that the proposed observer can detect the faults sensitively,and the influence of unknown distur-bance on residual error can be restrained to a given range.

  11. Robust PCA-Based Abnormal Traffic Flow Pattern Isolation and Loop Detector Fault Detection

    Institute of Scientific and Technical Information of China (English)

    JIN Xuexiang; ZHANG Yi; LI Li; HU Jianming

    2008-01-01

    One key function of intelligent transportation systems is to automatically detect abnormal traffic phenomena and to help further investigations of the cause of the abnormality. This paper describes a robust principal components analysis (RPCA)-based abnormal traffic flow pattern isolation and loop detector fault detection method. The results show that RPCA is a useful tool to distinguish regular traffic flow from abnor-mal traffic flow patterns caused by accidents and loop detector faults. This approach gives an effective traffic flow data pre-processing method to reduce the human effort in finding potential loop detector faults. The method can also be used to further investigate the causes of the abnormality.

  12. Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors

    Directory of Open Access Journals (Sweden)

    D. Camarena-Martinez

    2015-02-01

    Full Text Available Detection of failures in induction motors is one of the most important concerns in industry. An unexpected fault in the induction motors can cause a loss of financial resources and waste of time that most companies cannot afford. The contribution of this paper is a fusion of the Empirical Mode Decomposition (EMD and Multiple Signal Classification (MUSIC methodologies for detection of multiple combined faults which provides an accurate and effective strategy for the motor condition diagnosis.

  13. An optimized ensemble local mean decomposition method for fault detection of mechanical components

    Science.gov (United States)

    Zhang, Chao; Li, Zhixiong; Hu, Chao; Chen, Shuai; Wang, Jianguo; Zhang, Xiaogang

    2017-03-01

    Mechanical transmission systems have been widely adopted in most of industrial applications, and issues related to the maintenance of these systems have attracted considerable attention in the past few decades. The recently developed ensemble local mean decomposition (ELMD) method shows satisfactory performance in fault detection of mechanical components for preventing catastrophic failures and reducing maintenance costs. However, the performance of ELMD often heavily depends on proper selection of its model parameters. To this end, this paper proposes an optimized ensemble local mean decomposition (OELMD) method to determinate an optimum set of ELMD parameters for vibration signal analysis. In OELMD, an error index termed the relative root-mean-square error (Relative RMSE) is used to evaluate the decomposition performance of ELMD with a certain amplitude of the added white noise. Once a maximum Relative RMSE, corresponding to an optimal noise amplitude, is determined, OELMD then identifies optimal noise bandwidth and ensemble number based on the Relative RMSE and signal-to-noise ratio (SNR), respectively. Thus, all three critical parameters of ELMD (i.e. noise amplitude and bandwidth, and ensemble number) are optimized by OELMD. The effectiveness of OELMD was evaluated using experimental vibration signals measured from three different mechanical components (i.e. the rolling bearing, gear and diesel engine) under faulty operation conditions.

  14. Methods and apparatus using commutative error detection values for fault isolation in multiple node computers

    Science.gov (United States)

    Almasi, Gheorghe [Ardsley, NY; Blumrich, Matthias Augustin [Ridgefield, CT; Chen, Dong [Croton-On-Hudson, NY; Coteus, Paul [Yorktown, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk I [Ossining, NY; Singh, Sarabjeet [Mississauga, CA; Steinmacher-Burow, Burkhard D [Wernau, DE; Takken, Todd [Brewster, NY; Vranas, Pavlos [Bedford Hills, NY

    2008-06-03

    Methods and apparatus perform fault isolation in multiple node computing systems using commutative error detection values for--example, checksums--to identify and to isolate faulty nodes. When information associated with a reproducible portion of a computer program is injected into a network by a node, a commutative error detection value is calculated. At intervals, node fault detection apparatus associated with the multiple node computer system retrieve commutative error detection values associated with the node and stores them in memory. When the computer program is executed again by the multiple node computer system, new commutative error detection values are created and stored in memory. The node fault detection apparatus identifies faulty nodes by comparing commutative error detection values associated with reproducible portions of the application program generated by a particular node from different runs of the application program. Differences in values indicate a possible faulty node.

  15. Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine

    Science.gov (United States)

    Wang, Yujing; Kang, Shouqiang; Jiang, Yicheng; Yang, Guangxue; Song, Lixin; Mikulovich, V. I.

    2012-05-01

    Effective classification of a rolling bearing fault location and especially its degree of performance degradation provides an important basis for appropriate fault judgment and processing. Two methods are introduced to extract features of the rolling bearing vibration signal—one combining empirical mode decomposition (EMD) with the autoregressive model, whose model parameters and variances of the remnant can be obtained using the Yule-Walker or Ulrych-Clayton method, and the other combining EMD with singular value decomposition. Feature vector matrices obtained are then regarded as the input of the improved hyper-sphere-structured multi-class support vector machine (HSSMC-SVM) for classification. Thereby, multi-status intelligent diagnosis of normal rolling bearings and faulty rolling bearings at different locations and the degrees of performance degradation of the faulty rolling bearings can be achieved simultaneously. Experimental results show that EMD combined with singular value decomposition and the improved HSSMC-SVM intelligent method requires less time and has a higher recognition rate.

  16. Potential fault region detection in TFDS images based on convolutional neural network

    Science.gov (United States)

    Sun, Junhua; Xiao, Zhongwen

    2016-10-01

    In recent years, more than 300 sets of Trouble of Running Freight Train Detection System (TFDS) have been installed on railway to monitor the safety of running freight trains in China. However, TFDS is simply responsible for capturing, transmitting, and storing images, and fails to recognize faults automatically due to some difficulties such as such as the diversity and complexity of faults and some low quality images. To improve the performance of automatic fault recognition, it is of great importance to locate the potential fault areas. In this paper, we first introduce a convolutional neural network (CNN) model to TFDS and propose a potential fault region detection system (PFRDS) for simultaneously detecting four typical types of potential fault regions (PFRs). The experimental results show that this system has a higher performance of image detection to PFRs in TFDS. An average detection recall of 98.95% and precision of 100% are obtained, demonstrating the high detection ability and robustness against various poor imaging situations.

  17. Observer-based fault detection scheme for a class of discrete time-delay systems

    Institute of Scientific and Technical Information of China (English)

    Zhong Maiying(钟麦英); Zhang Chenghui(张承慧); Ding Steven X; Lam James

    2004-01-01

    In this contribution, robust fault detection problems for discrete time-delay systems with l2-norm bounded un-known inputs are studied. The basic idea of our study is first to introduce a state-memoryless observer-based fault detec-tion filter (FDF) as the residual generator and then to formulate such a FDF design problem as an H∞ optimization prob-lem in the sense of increasing the sensitivity of residual to the faults, while simultaneously enhancing the robustness of residual to unknown input as well as plant input. The main results consist of the formulation of such a residual generation optimization problem, solvability conditions and the derivation of an analytic solution. The residual evaluation problem is also considered, which includes the determination of residual evaluation function and threshold. A numerical example is used to demonstrate the proposed fault detection scheme.

  18. Fault detection and diagnosis in nonlinear systems a differential and algebraic viewpoint

    CERN Document Server

    Martinez-Guerra, Rafael

    2014-01-01

    The high reliability required in industrial processes has created the necessity of detecting abnormal conditions, called faults, while processes are operating. The term fault generically refers to any type of process degradation, or degradation in equipment performance because of changes in the process's physical characteristics, process inputs or environmental conditions. This book is about the fundamentals of fault detection and diagnosis in a variety of nonlinear systems which are represented by ordinary differential equations. The fault detection problem is approached from a differential algebraic viewpoint, using residual generators based upon high-gain nonlinear auxiliary systems (‘observers’). A prominent role is played by the type of mathematical tools that will be used, requiring knowledge of differential algebra and differential equations. Specific theorems tailored to the needs of the problem-solving procedures are developed and proved. Applications to real-world problems, both with constant an...

  19. Robust fault detection for switched positive linear systems with time-varying delays.

    Science.gov (United States)

    Xiang, Mei; Xiang, Zhengrong

    2014-01-01

    This paper investigates the problem of robust fault detection for a class of switched positive linear systems with time-varying delays. The fault detection filter is used as the residual generator, in which the filter parameters are dependent on the system mode. Attention is focused on designing the positive filter such that, for model uncertainties, unknown inputs and the control inputs, the error between the residual and fault is minimized. The problem of robust fault detection is converted into a positive L1 filtering problem. Subsequently, by constructing an appropriate multiple co-positive type Lyapunov-Krasovskii functional, as well as using the average dwell time approach, sufficient conditions for the solvability of this problem are established in terms of linear matrix inequalities (LMIs). Two illustrative examples are provided to show the effectiveness and applicability of the proposed results.

  20. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  1. Fault Detection and Recovery for Full Range of Hydrogen Sensor Based on Relevance Vector Machine

    Institute of Scientific and Technical Information of China (English)

    Kai Song; Bing Wang; Ming Diao; Hongquan Zhang; Zhenyu Zhang

    2015-01-01

    In order to improve the reliability of hydrogen sensor, a novel strategy for full range of hydrogen sensor fault detection and recovery is proposed in this paper. Three kinds of sensors are integrated to realize the measurement for full range of hydrogen concentration based on relevance vector machine ( RVM ) . Failure detection of hydrogen sensor is carried out by using the variance detection method. When a sensor fault is detected, the other fault⁃free sensors can recover the fault data in real⁃time by using RVM predictor accounting for the relevance of sensor data. Analysis, together with both simulated and experimental results, a full⁃range hydrogen detection and hydrogen sensor self⁃validating experiment is presented to demonstrate that the proposed strategy is superior at accuracy and runtime compared with the conventional methods. Results show that the proposed methodology provides a better solution to the full range of hydrogen detection and the reliability improvement of hydrogen sensor.

  2. Application of Uncertainty Reasoning Theory to satellite Fault Detection and Diagnosis

    Institute of Scientific and Technical Information of China (English)

    YangTianshe; LiHuaizu; SunYanbong

    2004-01-01

    Reasoning theories are divided into certainty reasoning theories and uncertainty reasoning theories.Now,only certainty reason-ing theories use to deitcs are used to detect and diagnose satellite faults.However,in practice,it is difficult to detect and diagnose some faults of the satellite autiomatically only by use of ccrtainty.Fortunately.uncerlainty Reasoning theories are applied to detect and diagnose satellite faults.Uncertainty reasoning theories include several kinds of theories,such as inclusion degree theory,rough set theory,evidence reasoning theory,probabilisticresoning theory,fuzzy,fuzzy reasoningteory,and so on.Inclusion degree theory.rough set theory and evidence reasoning theory are three advanced ones,Based on these three theories respectively.the audhor introduces three new methods to detect and diagnose satellite faults in this paper.It is shown that the methods,suitable for detecting and diagnosing satellite faults,especially uncertainty faults,can remedy the defects of the current methods.

  3. Detecting intermittent resistive faults in digital CMOS circuits

    NARCIS (Netherlands)

    Ebrahimi, Hassan; Kerkhoff, Hans G.; Rohani, A.

    2016-01-01

    Interconnection reliability threats dependability of highly critical electronic systems. One of most challenging interconnection-induced reliability threats are intermittent resistive faults (IRFs). The occurrence rate of this kind of defects can take e.g. one month, and the duration of defects can

  4. Optimal Threshold Functions for Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, H.; Harbo, Anders La-Cour

    2003-01-01

    Fault diagnosis systems usually comprises two parts: a filtering part and a decision part, the latter typically based on threshold functions. In this paper, systematic ways to choose the threshold values are proposed. Two different test functions for the filtered signals are discussed and a method...

  5. Fault Detection and Isolation using Multi Objective Controller Design Techniques

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1996-01-01

    Abstract: This paper describes a method for designing fault detectionand isolation filters. The method is multi objective in the sense thatit follows optimization with arbitrarily mixed criteria specified ine.g. the QTR H-infinity or the QTR H^2 norm. Moreover,the involved optimization yields less...

  6. Fault Detection and Localization Method for Modular Multilevel Converters

    DEFF Research Database (Denmark)

    Deng, Fujin; Chen, Zhe; Khan, Mohammad Rezwan;

    2015-01-01

    in the MMC. The proposed method can be implemented with less computational intensity and complexity, even in case that multiple SMs faults occur in a short time interval. The proposed method is not only implemented in simulations with professional tool PSCAD/EMTDC, but also verified with a down-scale MMC...

  7. Similarity ratio analysis for early stage fault detection with optical emission spectrometer in plasma etching process.

    Science.gov (United States)

    Yang, Jie; McArdle, Conor; Daniels, Stephen

    2014-01-01

    A Similarity Ratio Analysis (SRA) method is proposed for early-stage Fault Detection (FD) in plasma etching processes using real-time Optical Emission Spectrometer (OES) data as input. The SRA method can help to realise a highly precise control system by detecting abnormal etch-rate faults in real-time during an etching process. The method processes spectrum scans at successive time points and uses a windowing mechanism over the time series to alleviate problems with timing uncertainties due to process shift from one process run to another. A SRA library is first built to capture features of a healthy etching process. By comparing with the SRA library, a Similarity Ratio (SR) statistic is then calculated for each spectrum scan as the monitored process progresses. A fault detection mechanism, named 3-Warning-1-Alarm (3W1A), takes the SR values as inputs and triggers a system alarm when certain conditions are satisfied. This design reduces the chance of false alarm, and provides a reliable fault reporting service. The SRA method is demonstrated on a real semiconductor manufacturing dataset. The effectiveness of SRA-based fault detection is evaluated using a time-series SR test and also using a post-process SR test. The time-series SR provides an early-stage fault detection service, so less energy and materials will be wasted by faulty processing. The post-process SR provides a fault detection service with higher reliability than the time-series SR, but with fault testing conducted only after each process run completes.

  8. Similarity ratio analysis for early stage fault detection with optical emission spectrometer in plasma etching process.

    Directory of Open Access Journals (Sweden)

    Jie Yang

    Full Text Available A Similarity Ratio Analysis (SRA method is proposed for early-stage Fault Detection (FD in plasma etching processes using real-time Optical Emission Spectrometer (OES data as input. The SRA method can help to realise a highly precise control system by detecting abnormal etch-rate faults in real-time during an etching process. The method processes spectrum scans at successive time points and uses a windowing mechanism over the time series to alleviate problems with timing uncertainties due to process shift from one process run to another. A SRA library is first built to capture features of a healthy etching process. By comparing with the SRA library, a Similarity Ratio (SR statistic is then calculated for each spectrum scan as the monitored process progresses. A fault detection mechanism, named 3-Warning-1-Alarm (3W1A, takes the SR values as inputs and triggers a system alarm when certain conditions are satisfied. This design reduces the chance of false alarm, and provides a reliable fault reporting service. The SRA method is demonstrated on a real semiconductor manufacturing dataset. The effectiveness of SRA-based fault detection is evaluated using a time-series SR test and also using a post-process SR test. The time-series SR provides an early-stage fault detection service, so less energy and materials will be wasted by faulty processing. The post-process SR provides a fault detection service with higher reliability than the time-series SR, but with fault testing conducted only after each process run completes.

  9. Rolling bearing fault diagnosis technology simple use%滚动轴承故障诊断技术的简单运用

    Institute of Scientific and Technical Information of China (English)

    周云

    2012-01-01

    重点介绍了利用振动信号分析手段来诊断滚动轴承故障的技术,并将其运用于齿轮箱、泵类等生产设备的状态监测,为准确分析及诊断故障提供参考。%This paper mainly introduced the use of vibration signal analysis method to the diagnosis of rolling bearing fault technology, and its applications in gear box, pumps and other production equipment condition monito- ring and fault diagnosis for accurate analysis and provide the reference and its applications in gear box, motor, large units and other production equipment condition monitoring and fault diagnosis for accurate analysis and provide the reference.

  10. Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

    Directory of Open Access Journals (Sweden)

    Saud Altaf

    2017-01-01

    Full Text Available In this paper, broken rotor bar (BRB fault is investigated by utilizing the Motor Current Signature Analysis (MCSA method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.

  11. Development Ground Fault Detecting System for D.C Voltage Line

    Energy Technology Data Exchange (ETDEWEB)

    Kim Taek Soo; Song Ung Il; Gwon, Young Dong; Lee Hyoung Kee [Korea Electric Power Research Institute, Taejon (Korea, Republic of)

    1996-12-31

    It is necessary to keep the security of reliability and to maximize the efficiency of maintenance by prompt detection of a D.C feeder ground fault point at the built ed or a building power plants. At present, the most of the power plants are set up the ground fault indicator lamp in the monitor room. If a ground fault occurs on DC voltage feeder, a current through the ground fault relay is adjusted and the lamps have brightened while the current flows the relay coil. In order to develop such a system, it is analyzed a D.C feeder ground circuit theoretically and studied a principles which can determine ground fault point or a polarity discrimination and a phase discrimination of the line. So, the developed system through this principles can compute a resistance ground fault current and a capacitive ground fault current. It shows that the system can defect a ground fault point or a bad insulated line by measuring a power plant D.C feeder insulation resistance at the un interruptible power status, and therefore the power plant could protect an unexpected service interruption . (author). 18 refs., figs.

  12. Gear-box fault detection using time-frequency based methods

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2015-01-01

    Gear-box fault monitoring and detection is important for optimization of power generation and availability of wind turbines. The current industrial approach is to use condition monitoring systems, which runs in parallel with the wind turbine control system, using expensive additional sensors...... in the gear-box resonance frequency can be detected. Two different time–frequency based approaches are presented in this paper. One is a filter based approach and the other is based on a Karhunen–Loeve basis. Both of them detect the gear-box fault with an acceptable detection delay of maximum 100s, which...

  13. Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

    Science.gov (United States)

    Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)

    2001-01-01

    This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.

  14. A geometric approach for fault detection and isolation of stator short circuit failure in a single asynchronous machine

    KAUST Repository

    Khelouat, Samir

    2012-06-01

    This paper deals with the problem of detection and isolation of stator short-circuit failure in a single asynchronous machine using a geometric approach. After recalling the basis of the geometric approach for fault detection and isolation in nonlinear systems, we will study some structural properties which are fault detectability and isolation fault filter existence. We will then design filters for residual generation. We will consider two approaches: a two-filters structure and a single filter structure, both aiming at generating residuals which are sensitive to one fault and insensitive to the other faults. Some numerical tests will be presented to illustrate the efficiency of the method.

  15. Robust fault detection of wind energy conversion systems based on dynamic neural networks.

    Science.gov (United States)

    Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

    2014-01-01

    Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  16. Active Fault Detection and Isolation for Hybrid Systems

    DEFF Research Database (Denmark)

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas;

    2009-01-01

    models predicted outputs such that their discrepancies are observable by passive fault diagnosis technique. Isolation of different faults is done by implementation a bank of Extended Kalman Filter (EKF) where the convergence criterion for EKF is confirmed by Genetic Algorithm (GA). The method is applied......An algorithm for active fault detection and isolation is proposed. In order to observe the failure hidden due to the normal operation of the controllers or the systems, an optimization problem based on minimization of test signal is used. The optimization based method imposes the normal and faulty...

  17. Detection and Diagnosis of Gear Fault By the Single Gear Tooth Analysis Technique

    Institute of Scientific and Technical Information of China (English)

    MENG Tao; LIAO Ming-fu

    2003-01-01

    This paper presents a procedure of single gear tooth analysis for early detection and diagnosis of gear faults. The objective of this procedure is to develop a method for more sensitive detection of the incipient faults and locating the faults in the gear. The main idea of the single gear tooth analysis is that the vibration signals collected with a high sampling rate are divided into a number of segments with the same time interval. The number of signal segments is equal to that of the gear teeth. The analysis of individual segments reveals more sensitively the changes of the vibration signals in both time and frequency domain caused by gear faults. In addition, the location of a failed tooth can be indicated in terms of the position of the segment that deviates from the normal segments. An experimental investigation verified the advantages of the single gear tooth analysis.

  18. An Imperfect-debugging Fault-detection Dependent-parameter Software

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Software reliability growth models (SRGMs) incorporating the imperfect debugging and learning phenomenon of developers have recently been developed by many researchers to estimate software reliability measures such as the number of remaining faults and software reliability. However, the model parameters of both the fault content rate function and fault detection rate function of the SRGMs are often considered to be independent from each other. In practice, this assumption may not be the case and it is worth to investigate what if it is not. In this paper, we aim for such study and propose a software reliability model connecting the imperfect debugging and learning phenomenon by a common parameter among the two functions, called the imperfect-debugging fault-detection dependent-parameter model. Software testing data collected from real applications are utilized to illustrate the proposed model for both the descriptive and predictive power by determining the non-zero initial debugging process.

  19. Robust recurrent neural network modeling for software fault detection and correction prediction

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Q.P. [Quality and Innovation Research Centre, Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119260 (Singapore)]. E-mail: g0305835@nus.edu.sg; Xie, M. [Quality and Innovation Research Centre, Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119260 (Singapore)]. E-mail: mxie@nus.edu.sg; Ng, S.H. [Quality and Innovation Research Centre, Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119260 (Singapore)]. E-mail: isensh@nus.edu.sg; Levitin, G. [Israel Electric Corporation, Reliability and Equipment Department, R and D Division, Aaifa 31000 (Israel)]. E-mail: levitin@iec.co.il

    2007-03-15

    Software fault detection and correction processes are related although different, and they should be studied together. A practical approach is to apply software reliability growth models to model fault detection, and fault correction process is assumed to be a delayed process. On the other hand, the artificial neural networks model, as a data-driven approach, tries to model these two processes together with no assumptions. Specifically, feedforward backpropagation networks have shown their advantages over analytical models in fault number predictions. In this paper, the following approach is explored. First, recurrent neural networks are applied to model these two processes together. Within this framework, a systematic networks configuration approach is developed with genetic algorithm according to the prediction performance. In order to provide robust predictions, an extra factor characterizing the dispersion of prediction repetitions is incorporated into the performance function. Comparisons with feedforward neural networks and analytical models are developed with respect to a real data set.

  20. Detection and classification of winding faults in windmill generators using Wavelet Transform and ANN

    Energy Technology Data Exchange (ETDEWEB)

    Gketsis, Zacharias E.; Zervakis, Michalis E.; Stavrakakis, George [Department of Electronics and Computer Engineering, Technical University of Crete, Chania 73100 (Greece)

    2009-11-15

    This paper exploits the Wavelet Transform (WT) analysis along with Artificial Neural Networks (ANN) for the diagnosis of electrical machines winding faults. A novel application is presented exploring the problem of automatically identifying short circuits of windings, which often appear during machine manufacturing and operation. Such faults are usually resulting from electrodynamics forces generated during the flow of large short circuit currents, as well as forces occurring when the machines are transported. The early detection and classification of winding failures is of particular importance, as these kinds of defects can lead to winding damage due to overheating, imbalance, etc. Application results and investigations of windmill generator winding turn-to-turn faults between adjacent winding wires are presented. The ANN approach is proven effective in detecting and classifying faults based on WT features extracted from high frequency measurements of the admittance, current, or voltage responses. (author)

  1. Fault detection and diagnosis for compliance monitoring in international supply chains

    OpenAIRE

    2016-01-01

    Currently international supply chains are facing risks concerning faults in compliance, such as altering shipping documentations, fictitious inventory, and inter-company manipulations. In this paper a method to detect and diagnose fault scenarios regarding customs compliance in supply chains is proposed. This method forms part of a general approach called model-based auditing, which is based on a normative meta-model of the movement of money and goods or services. The modeling framework is pr...

  2. Detecting eccentricity faults in a PMSM in non-stationary conditions

    OpenAIRE

    Javier Rosero García; José Luis Romeral; Esteban Rosero García

    2012-01-01

    Permanent magnet alternating current machines are being widely used in applications demanding high and rugged performance, such as industrial automation and the aerospace and automotive industries. This paper presents a study of a permanent magnet synchronous machine (PMSM) running in eccentricity; these machines’ condition monitoring and fault detection would provide added value and they are also assuming growing importance. This paper investigates the effect of eccentricity faults on PMSM m...

  3. Technique for optimal placement of transducers for fault detection in rotating machines

    OpenAIRE

    2013-01-01

    Online fault detection and diagnosis of rotating machinery requires a number of transducers that can be significantly expensive for industrial processes. The sensitivity of various transducers and their appropriate positioning are dependent on different types of fault conditions. It is critical to formulate a method to systematically determine the effectiveness of transducer locations for monitoring the condition of a machine. In this article, number of independent sources analysis is used as...

  4. New procedure for gear fault detection and diagnosis using instantaneous angular speed

    Science.gov (United States)

    Li, Bing; Zhang, Xining; Wu, Jili

    2017-02-01

    Besides the extreme complexity of gear dynamics, the fault diagnosis results in terms of vibration signal are sometimes easily misled and even distorted by the interference of transmission channel or other components like bearings, bars. Recently, the research field of Instantaneous Angular Speed (IAS) has attracted significant attentions due to its own advantages over conventional vibration analysis. On the basis of IAS signal's advantages, this paper presents a new feature extraction method by combining the Empirical Mode Decomposition (EMD) and Autocorrelation Local Cepstrum (ALC) for fault diagnosis of sophisticated multistage gearbox. Firstly, as a pre-processing step, signal reconstruction is employed to address the oversampled issue caused by the high resolution of the angular sensor and the test speed. Then the adaptive EMD is used to acquire a number of Intrinsic Mode Functions (IMFs). Nevertheless, not all the IMFs are needed for the further analysis since different IMFs have different sensitivities to fault. Hence, the cosine similarity metric is introduced to select the most sensitive IMF. Even though, the sensitive IMF is still insufficient for the gear fault diagnosis due to the weakness of the fault component related to the gear fault. Therefore, as the final step, ALC is used for the purpose of signal de-noising and feature extraction. The effectiveness and robustness of the new approach has been validated experimentally on the basis of two gear test rigs with gears under different working conditions. Diagnosis results show that the new approach is capable of effectively handling the gear fault diagnosis i.e., the highlighted quefrency and its rahmonics corresponding to the rotary period and its multiple are displayed clearly in the cepstrum record of the proposed method.

  5. Fault detection of a spur gear using vibration signal with multivariable statistical parameters

    Directory of Open Access Journals (Sweden)

    Songpon Klinchaeam

    2014-10-01

    Full Text Available This paper presents a condition monitoring technique of a spur gear fault detection using vibration signal analysis based on time domain. Vibration signals were acquired from gearboxes and used to simulate various faults on spur gear tooth. In this study, vibration signals were applied to monitor a normal and various fault conditions of a spur gear such as normal, scuffing defect, crack defect and broken tooth. The statistical parameters of vibration signal were used to compare and evaluate the value of fault condition. This technique can be applied to set alarm limit of the signal condition based on statistical parameter such as variance, kurtosis, rms and crest factor. These parameters can be used to set as a boundary decision of signal condition. From the results, the vibration signal analysis with single statistical parameter is unclear to predict fault of the spur gears. The using at least two statistical parameters can be clearly used to separate in every case of fault detection. The boundary decision of statistical parameter with the 99.7% certainty ( 3   from 300 referenced dataset and detected the testing condition with 99.7% ( 3   accuracy and had an error of less than 0.3 % using 50 testing dataset.

  6. A Novel Method for Detection and Classification of Covered Conductor Faults

    Directory of Open Access Journals (Sweden)

    Stanislav Misak

    2016-01-01

    Full Text Available Medium-Voltage (MV overhead lines with Covered Conductors (CCs are increasingly being used around the world primarily in forested or dissected terrain areas or in urban areas where it is not possible to utilize MV cable lines. The CC is specific in high operational reliability provided by the conductor core insulation compared to Aluminium-Conductor Steel-Reinforced (ACSR overhead lines. The only disadvantage of the CC is rather the problematic detection of faults compared to the ACSR. In this work, we consider the following faults: the contact of a tree branch with a CC and the fall of a conductor on the ground. The standard protection relays are unable to detect the faults and so the faults pose a risk for individuals in the vicinity of the conductor as well as it compromises the overall safety and reliability of the MV distribution system. In this article, we continue with our previous work aimed at the method enabling detection of the faults and we introduce a method enabling a classification of the fault type. Such a classification is especially important for an operator of an MV distribution system to plan the optimal maintenance or repair the faulty conductors since the fall of a tree branch can be solved later whereas the breakdown of a conductor means an immediate action of the operator.

  7. Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Frank, Stephen; Heaney, Michael; Jin, Xin; Robertson, Joseph; Cheung, Howard; Elmore, Ryan; Henze, Gregor

    2016-08-01

    Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.

  8. Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

    Energy Technology Data Exchange (ETDEWEB)

    Frank, Stephen; Heaney, Michael; Jin, Xin; Robertson, Joseph; Cheung, Howard; Elmore, Ryan; Henze, Gregor

    2016-08-26

    Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.

  9. Fault Detection and Localization in Transmission Lines with a Static Synchronous Series Compensator

    Directory of Open Access Journals (Sweden)

    REYES-ARCHUNDIA, E.

    2015-08-01

    Full Text Available This paper proposes a fault detection and localization method for power transmission lines with a Static Synchronous Series Compensator (SSSC. The algorithm is based on applying a modal transformation to the current and voltage signals sampled at high frequencies. Then, the wavelet transform is used for calculating the current and voltage traveling waves, avoiding low frequency interference generated by the system and the SSSC. Finally, by using reflectometry principles, straightforward expressions for fault detection and localization in the transmission line are derived. The algorithm performance was tested considering several study cases, where some relevant parameters such as voltage compensation level, fault resistance and fault inception angle are varied. The results indicate that the algorithm can be successfully be used for fault detection and localization in transmission lines compensated with a SSSC. The estimated error in calculating the distance to the fault is smaller than 1% of the transmission line length. The test system is simulated in PSCAD platform and the algorithm is implemented in MATLAB software.

  10. Detecting and isolating faults of an air-handling unit using on- line diagnostic tests

    Energy Technology Data Exchange (ETDEWEB)

    Pakanen, J. [VTT Building Technology, Espoo (Finland). Building Services and Fire Technology

    1996-12-31

    On-line diagnostic testing is one choice, when practical and robust fault detection and isolation methods are considered for automated processes. Performing a test means exciting a process by means of prescribed input signals, supervising responses and comparing results with a process model. An on-line diagnostic test is repeated similarly every time, in similar process conditions, making modelling an uncomplicated task. Fault detection is a direct consequence of the comparison, but fault isolation is based on elementary constraints, decomposed from the process model. A rough description of a fault can be achieved by heuristic reasoning, which enables application of the method in practice. A more specified fault description is accomplished by learning from old solutions. The reasoner accumulates information making decisions of the classifier gradually more precise through acquired experience. The method is best for successive installations, in which knowledge can be cumulated. On-line diagnostic tests are generic in character, but in this paper they are configured for an air handling unit of an office building and applied in its preheating subprocess. The paper presents the development, simulation and field tests of the fault detection and isolation method and its configuration as a part of a diagnostic system. (orig.) (35 refs.)

  11. Condition monitoring of induction motor bearing based on bearing damage index

    Directory of Open Access Journals (Sweden)

    Patel R.K.

    2017-03-01

    Full Text Available The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become “transient” in nature. The purpose of this paper is to briefly discuss the identification of an Inner Raceway Fault (IRF and an Outer Raceway Fault (ORF with the different fault severity levels. The conventional statistical analysis was only able to detect the existence of a fault but unable to discriminate between IRF and ORF. In the present work, a detection technique named as bearing damage index (BDI has been proposed. The proposed BDI technique uses wavelet packet node energy coefficient analysis method. The well-known combination of Hilbert transform (HT and Fast Fourier Transform (FFT has been carried out in order to identify the IRF and ORF faults. The results show that wavelet packet node energy coefficients are not only sensitive to detect the faults in bearing but at the same time they are able to detect the severity level of the fault. The proposed bearing damage index method for fault identification may be considered as an ‘index’ representing the health condition of rotating machines.

  12. Improved Data-based Fault Detection Strategy and Application to Distillation Columns

    KAUST Repository

    Madakyaru, Muddu

    2017-01-31

    Chemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative statistical approach that exploits the advantages of multiscale partial least squares (MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes. Specifically, we combine an MSPLS algorithm with wavelet analysis to create our modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.

  13. A hybrid fault detection and isolation strategy for a team of cooperating unmanned vehicles

    Science.gov (United States)

    Tousi, M. M.; Khorasani, K.

    2015-01-01

    In this paper, a hybrid fault detection and isolation (FDI) methodology is developed for a team of cooperating unmanned vehicles. The proposed approach takes advantage of the cooperative nature of the team to detect and isolate relatively low-severity actuator faults that are otherwise not detectable and isolable by the vehicles themselves individually. The approach is hybrid and consists of both low-level (agent/team level) and high-level [discrete-event systems (DES) level] FDI modules. The high-level FDI module is formulated in the DES supervisory control framework, whereas the low-level FDI module invokes classical FDI techniques. By properly integrating the two FDI modules, a larger class of faults can be detected and isolated as compared to the existing techniques in the literature that rely on each level separately. Simulation results for a team of five unmanned aerial vehicles are also presented to demonstrate the effectiveness and capabilities of our proposed methodology.

  14. Distributed Fault Detection and Isolation for Flocking in a Multi-robot System with Imperfect Communication

    Directory of Open Access Journals (Sweden)

    Shao Shiliang

    2014-06-01

    Full Text Available In this paper, we focus on distributed fault detection and isolation (FDI for a multi-robot system where multiple robots execute a flocking task. Firstly, we propose a fault detection method based on the local-information-exchange and sensor-measurement technologies to cover cases of both perfect communication and imperfect communication. The two detection technologies can be adaptively selected according to the packet loss rate (PLR. Secondly, we design a fault isolation method, considering a situation in which faulty robots still influence the behaviours of other robots. Finally, a complete FDI scheme, based on the proposed detection and isolation methods, is simulated in various scenarios. The results demonstrate that our FDI scheme is effective.

  15. Induced Voltages Ratio-Based Algorithm for Fault Detection, and Faulted Phase and Winding Identification of a Three-Winding Power Transformer

    Directory of Open Access Journals (Sweden)

    Byung Eun Lee

    2014-09-01

    Full Text Available This paper proposes an algorithm for fault detection, faulted phase and winding identification of a three-winding power transformer based on the induced voltages in the electrical power system. The ratio of the induced voltages of the primary-secondary, primary-tertiary and secondary-tertiary windings is the same as the corresponding turns ratio during normal operating conditions, magnetic inrush, and over-excitation. It differs from the turns ratio during an internal fault. For a single phase and a three-phase power transformer with wye-connected windings, the induced voltages of each pair of windings are estimated. For a three-phase power transformer with delta-connected windings, the induced voltage differences are estimated to use the line currents, because the delta winding currents are practically unavailable. Six detectors are suggested for fault detection. An additional three detectors and a rule for faulted phase and winding identification are presented as well. The proposed algorithm can not only detect an internal fault, but also identify the faulted phase and winding of a three-winding power transformer. The various test results with Electromagnetic Transients Program (EMTP-generated data show that the proposed algorithm successfully discriminates internal faults from normal operating conditions including magnetic inrush and over-excitation. This paper concludes by implementing the algorithm into a prototype relay based on a digital signal processor.

  16. Implementation of a high-impedance fault detection algorithm. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Balser, S.J.; Lawrence, D.J.; Caprino, B.; Delaney, L.

    1985-05-01

    A digital computer based algorithm was developed to detect high impedance faults on distribution systems using statistical methods. The algorithm is written in PL/M 86 and PASCAL and implemented on an INTEL SYS380 microcomputer system, designed to operate in real time and interface with acquisition software. The report contains a description of the calculation procedures comprising the detection algorithm, implementation requirements, and test results for algorithm verification. A discussion of hardware limitations and an estimation of fault detection rate based on historical records is also presented.

  17. Fault detection based on H∞ states observer for networked control systems

    Institute of Scientific and Technical Information of China (English)

    Zhu Zhangqing; Jiao Xiaocheng

    2009-01-01

    The influence of random short time-delay to networked control systems (NCS) is changed into an unknown bounded uncertain part. Without changing the structure of the system, an H∞ states observer is designed for NCS with short time-delay. Based on the designed states observer, a robust fault detection approach is proposed for NCS. In addition, an optimization method for the selection of the detection threshold is introduced for better tradeoff between the robustness and the sensitivity. Finally, some simulation results demonstrate that the presented states observer is robust and the fault detection for NCS is effective.

  18. Fault Detection of Inline Reciprocating Diesel Engine: A Mass and Gas-Torque Approach

    Directory of Open Access Journals (Sweden)

    S. H. Gawande

    2012-01-01

    Full Text Available Early fault detection and diagnosis for medium-speed diesel engines are important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion-related fault detection capability of crankshaft torsional vibrations. Proposed methodology state the way of early fault detection in the operating six-cylinder diesel engine. The model of six cylinders DI Diesel engine is developed appropriately. As per the earlier work by the same author the torsional vibration amplitudes are used to superimpose the mass and gas torque. Further mass and gas torque analysis is used to detect fault in the operating engine. The DFT of the measured crankshaft’s speed, under steady-state operating conditions at constant load shows significant variation of the amplitude of the lowest major harmonic order. This is valid both for uniform operating and faulty conditions and the lowest harmonic orders may be used to correlate its amplitude to the gas pressure torque and mass torque for a given engine. The amplitudes of the lowest harmonic orders (0.5, 1, and 1.5 of the gas pressure torque and mass torque are used to map the fault. A method capable to detect faulty cylinder of operating Kirloskar diesel engine of SL90 Engine-SL8800TA type is developed, based on the phases of the lowest three harmonic orders.

  19. E-core transverse flux machine with integrated fault detection system

    DEFF Research Database (Denmark)

    Rasmussen, Peter Omand; Runólfsson, Gunnar; Thorsdóttir, Thórunn Ágústa;

    2011-01-01

    circuit faults have been developed. For other types of machines the single and partial turn short circuit is very difficult to deal with and requires normally very comprehensive detection and calculation schemes. The developed detection algorithm combined with the E-core transverse flux machine...

  20. Observer-based Fault Detection and Isolation for Nonlinear Systems

    DEFF Research Database (Denmark)

    Lootsma, T.F.

    . Then the geometric approach is applied to a nonlinear ship propulsion system benchmark. The calculations and application results are presented in detail to give an illustrative example. The obtained subsystems are considered for the design of nonlinear observers in order to obtain FDI. Additionally, an adaptive...... for the observers designed for the ship propulsion system. Furthermore, it stresses the importance of the time-variant character of the linearization along a trajectory. It leads to a different stability analysis than for linearization at one operation point. Finally, the preliminary concept of (actuator) fault...