An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing

Zheng, Jianhua; Huang, Siqi; Pan, Haiyang; Jiang, Kuosheng

doi:10.1109/access.2019.2940627

Cited by 18 publications

(10 citation statements)

References 24 publications

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“…Comparative analysis shows that generalization of the failure mode identification model is relatively poor. (4) METHOD PROPOSED IN PAPER [29] SVM rolling bearing fault pattern recognition method with IEWT and RCMDE feature vector value is summarized in this section [29].…”

Section: Methods Comparison (1) Different Fault Feature Vectorsmentioning

confidence: 99%

“…RCMDE is a multi-scale method to the original signal, that is, equidistant division and then the average is calculate, further refinement on the basis of multi-scale dispersion entropy MDE [27]. When the RCMDE with a scale factor of τ is calculated, first the original signal is divided into segments of length τ continuously according to the initial points of [1, τ], then the average value of each segment is calculated, and then the average values is arranged in order as a coarse-grained sequence, a total of τ coarse-grained sequences are obtained [29].…”

Section: ) Refinde Composite Multi-scale Dispersion Entropymentioning

confidence: 99%

“…The effectiveness of this method was experimentally validated. Zheng et al [29] proposed a rolling bearing fault diagnosis method based on improved empirical wavelet transform (IEWT) and RCMDE. The proposed method can accurately distinguish rolling bearing failure types and has high fault diagnosis accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

et al. 2021

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In this paper, one of most widely utilized rolling bearings in rotating machinery is selected as the research object. Automatic rolling bearing fault identification model including support vector machine (SVM) training module, fault classification knowledge base module, and fault automatic identification module is proposed. A generalized method for automatic identification of rolling bearing faults based on refined composite multi-scale dispersion entropy (RCMDE) is developed. First, in order to solve the problem of setting the value range of the decomposition level K based on empirical knowledge for variational modal decomposition (VMD), a maximum kurtosis value method is proposed to determine the preset value range of K in whale optimization algorithm. Then, an improved VMD method is used to adaptively decompose the signal into a series of intrinsic mode function components. Next, the correlation coefficient method is employed to screen effective feature components of bearings in different health states for reconstruction. Through theoretical analysis, the calculated RCMDE value of reconstructed signal is screened and input as a feature value into the optimized SVM classifier for fault pattern recognition. The input of rolling bearing vibration data without preprocessing and the output of the fault identification which don't rely on empirical knowledge of external experts is realized. Experimental and engineering case data of rolling bearings under different equipment and operating environments are tested and validated. The results indicate that the model proposed in this paper shows good fault identification, demonstrates good generalization performance, and has beneficial industrial application prospect.

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Section: Methods Comparison (1) Different Fault Feature Vectorsmentioning

confidence: 99%

Section: ) Refinde Composite Multi-scale Dispersion Entropymentioning

confidence: 99%

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A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

et al. 2021

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“…Therefore, a variety of nonlinear signal analysis methods have been widely used in bearing fault diagnosis. Due to its unique advantages in feature extraction, more and more attention has been paid to entropy by researchers in ever-increasing fields, and a series of research achievements have been made [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Yang

Jiang

et al. 2021

Entropy

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In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

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“…And the multiscale permutation entropy was proposed by Aziz and Arif to mitigate the limitation of the single case [28]. In addition, dispersion entropy [29] has been widely used in the field of biological signals, and dispersion entropy is extended to multiscale dispersion entropy [30], refined composite multiscale dispersion entropy [31] and refined composite multivariate multiscale dispersion entropy [32], [33]. Recent years, Wang.…”

Section: Introductionmentioning

confidence: 99%

Generalized Composite Multiscale Diversity Entropy and Its Application for Fault Diagnosis of Rolling Bearing in Automotive Production Line

Liang

Chen

2021

IEEE Access

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This paper considers the entropy based feature extraction method for the fault diagnosis of rolling bearings in automobile production line, where the fault information is difficult to identify due to the strong nonlinear and non-stationary characteristics of the fault vibration signals. In our work, a novel entropy based method called generalized composite multiscale diversity entropy (GCMDiEn) is developed. This method can effectively track the inside pattern changes of the time series by the description of cosine similarity between adjacent orbits. Unlike most of the existing entropy based results which concentrate on the static orderliness, we analyze the dynamic complexity characteristics of the arbitrary time series. Moreover, compared with the multiscale diversity entropy, GCMDiEn calculates all coarse-grained time series entropy value with the same scale and extends the first-order moment to second-order moment to mitigate the shortcomings of the short time series instability and losing part of the frequency region information. This paper proposed a new rolling bearing fault diagnosis framework which combined the GCMDiEn with the empirical wavelet transform (EWT), Laplacian score (LS), and particle swarm optimization-based support vector machine (PSO-SVM). Finally, the simulation results show the superiority of the GCMDiEn method over the multiscale diversity entropy method. The proposed framework has a higher fault recognition rate (99.38%) than the existing methods.

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An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing

Cited by 18 publications

References 24 publications

A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Generalized Composite Multiscale Diversity Entropy and Its Application for Fault Diagnosis of Rolling Bearing in Automotive Production Line

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