A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features

Wang, Dong; Sun, Shilong; Tse, Peter W.

doi:10.1016/j.ymssp.2014.07.005

Cited by 40 publications

(26 citation statements)

References 33 publications

(63 reference statements)

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“…Mao and Todd [256] proposed a Bayesian recursive framework for ball-bearing damage classification, and selected the frequency response function as the main features. Wang et al [257] proposed a Bayesian approach to extract bearing fault features, which represented a joint posterior probability density function of wavelet parameters using a set of random particles. Subsequently, Wang et al [258] proposed a Gauss-Hermite integration based Bayesian inference method for estimating the posterior distribution of wavelet parameters.…”

Section: Statistical Approachesmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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Section: Statistical Approachesmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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“…Commonly used TF methods are Fourier Transform (FT), modifications of periodogram [12], TF varying autoregressive process (TFAR) [13], and continuous wavelet transform (CWT) [14,15]. The last one becomes very popular, especially in the last decade, for its good time resolution.…”

Section: Introductionmentioning

confidence: 99%

“…In all cases, authors' uses testing statistic with devoted distribution corrected with respect to the background spectrum distribution and sampling period. An alternative approach, presented in [15] or [14], uses Monte Carlo (MC) simulation for identification of critical values.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Co-movement Significance Testing with Respect to Gaussian White Noise Background

2018

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Abstract. The paper deals with significance testing of time series co-movement measured via wavelet analysis, namely via the wavelet cross-spectra. This technique is very popular for its better time resolution compare to other techniques. Such approach put in evidence the existence of both long-run and short-run co-movement. In order to have better predictive power it is suitable to support and validate obtained results via some testing approach. We investigate the test of wavelet power cross-spectrum with respect to the Gaussian white noise background with the use of the Bessel function. Our experiment is performed on real data, i.e. seasonally adjusted quarterly data of gross domestic product of the United Kingdom, Korea and G7 countries. To validate the test results we perform Monte Carlo simulation. We describe the advantages and disadvantages of both approaches and formulate recommendations for its using.

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“…Lucia et al [22] proposed a novel technique based on the stray flux measurement in different positions around the electrical machine, and reports an extensive survey on the stray-flux-based fault detection methods for induction motors. Wang et al [23] proposed a general sequential Monte Carlo method and a joint posterior probability density function of wavelet parameters by a set of random particles with their associated weights for extracting bearing fault features. He et al [24] proposed an ensemble super-wavelet transform based on the combination of tunable Q-factor wavelet transform and Hilbert transform for investigating the vibration features of induction motor bearing faults.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Zhao

Sun

Deng

et al. 2016

Entropy

183

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Abstract:Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

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A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features

Cited by 40 publications

References 33 publications

Basic research on machinery fault diagnostics: Past, present, and future trends

Basic research on machinery fault diagnostics: Past, present, and future trends

Wavelet Co-movement Significance Testing with Respect to Gaussian White Noise Background

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

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