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

The vibration signals collected by the sensor often have non-stationary and non-linear characteristics owing to the complexity of working environment of rolling bearing, so it is difficult to obtain useful and stable vibration information for diagnosis. Empirical Wavelet Transform (EWT) can effectiv...

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Main Authors: Jinde Zheng, Siqi Huang, Haiyang Pan, Kuosheng Jiang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8830419/
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author Jinde Zheng
Siqi Huang
Haiyang Pan
Kuosheng Jiang
author_facet Jinde Zheng
Siqi Huang
Haiyang Pan
Kuosheng Jiang
author_sort Jinde Zheng
collection DOAJ
description The vibration signals collected by the sensor often have non-stationary and non-linear characteristics owing to the complexity of working environment of rolling bearing, so it is difficult to obtain useful and stable vibration information for diagnosis. Empirical Wavelet Transform (EWT) can effectively decompose non-stationary and nonlinear signals, but it is not suitable for signal analysis of bearing with a complicated spectrum. In this paper, an improved EWT (IEWT) method is proposed by developing a new segmentation approach. Meanwhile, the IEWT is compared with empirical mode decomposition (EMD) and EWT to verify the superiority of IEWT in decomposition accuracy. By combining with the refined composite multiscale dispersion entropy (RCMDE), which is a powerful nonlinear tool for irregularity measurement of vibration signals, a new diagnosis method based on IEWT, RCMDE, multi-cluster feature selection and support vector machine is proposed. Then the method is applied to analysis of bearing in this paper and the results show that the new method has higher identifying rate and better performance than that of the methods of RCMDE combining with EMD or EWT. Also, the superiority of RCMDE to dispersion entropy and multiscale dispersion entropy is investigated, together with the superiority of MCFS for feature selection.
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spelling doaj.art-82566cdea23f48098d50e94b0f9617442022-12-21T17:14:27ZengIEEEIEEE Access2169-35362020-01-01816873216874210.1109/ACCESS.2019.29406278830419An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling BearingJinde Zheng0Siqi Huang1Haiyang Pan2https://orcid.org/0000-0001-9868-8154Kuosheng Jiang3https://orcid.org/0000-0003-0300-1976School of Mechanical Engineering, Anhui University of Technology, Ma’anshan, ChinaSchool of Mechanical Engineering, Anhui University of Technology, Ma’anshan, ChinaSchool of Mechanical Engineering, Anhui University of Technology, Ma’anshan, ChinaAnhui Key Laboratory of Mine Intelligent Equipment and Technology, Anhui University of Science and Technology, Huainan, ChinaThe vibration signals collected by the sensor often have non-stationary and non-linear characteristics owing to the complexity of working environment of rolling bearing, so it is difficult to obtain useful and stable vibration information for diagnosis. Empirical Wavelet Transform (EWT) can effectively decompose non-stationary and nonlinear signals, but it is not suitable for signal analysis of bearing with a complicated spectrum. In this paper, an improved EWT (IEWT) method is proposed by developing a new segmentation approach. Meanwhile, the IEWT is compared with empirical mode decomposition (EMD) and EWT to verify the superiority of IEWT in decomposition accuracy. By combining with the refined composite multiscale dispersion entropy (RCMDE), which is a powerful nonlinear tool for irregularity measurement of vibration signals, a new diagnosis method based on IEWT, RCMDE, multi-cluster feature selection and support vector machine is proposed. Then the method is applied to analysis of bearing in this paper and the results show that the new method has higher identifying rate and better performance than that of the methods of RCMDE combining with EMD or EWT. Also, the superiority of RCMDE to dispersion entropy and multiscale dispersion entropy is investigated, together with the superiority of MCFS for feature selection.https://ieeexplore.ieee.org/document/8830419/Fault diagnosisimproved empirical wavelet transformrefined composite multiscale dispersion entropyfeature extractionrolling bearing
spellingShingle Jinde Zheng
Siqi Huang
Haiyang Pan
Kuosheng Jiang
An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing
IEEE Access
Fault diagnosis
improved empirical wavelet transform
refined composite multiscale dispersion entropy
feature extraction
rolling bearing
title An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing
title_full An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing
title_fullStr An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing
title_full_unstemmed An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing
title_short An Improved Empirical Wavelet Transform and Refined Composite Multiscale Dispersion Entropy-Based Fault Diagnosis Method for Rolling Bearing
title_sort improved empirical wavelet transform and refined composite multiscale dispersion entropy based fault diagnosis method for rolling bearing
topic Fault diagnosis
improved empirical wavelet transform
refined composite multiscale dispersion entropy
feature extraction
rolling bearing
url https://ieeexplore.ieee.org/document/8830419/
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