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Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing

1
School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243032, China
2
Anhui Key Laboratory of Mine Intelligent Equipment and Technology, Anhui University of Science & Technology, Huainan 232001, China
3
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney 2052, Australia
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(6), 621; https://doi.org/10.3390/e21060621
Received: 24 May 2019 / Revised: 14 June 2019 / Accepted: 22 June 2019 / Published: 25 June 2019
(This article belongs to the Special Issue Permutation Entropy: Theory and Applications)
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Abstract

Multi-scale permutation entropy (MPE) is an effective nonlinear dynamic approach for complexity measurement of time series and it has been widely applied to fault feature representation of rolling bearing. However, the coarse-grained time series in MPE becomes shorter and shorter with the increase of the scale factor, which causes an imprecise estimation of permutation entropy. In addition, the different amplitudes of the same patterns are not considered by the permutation entropy used in MPE. To solve these issues, the time-shift multi-scale weighted permutation entropy (TSMWPE) approach is proposed in this paper. The inadequate process of coarse-grained time series in MPE was optimized by using a time shift time series and the process of probability calculation that cannot fully consider the symbol mode is solved by introducing a weighting operation. The parameter selections of TSMWPE were studied by analyzing two different noise signals. The stability and robustness were also studied by comparing TSMWPE with TSMPE and MPE. Based on the advantages of TSMWPE, an intelligent fault diagnosis method for rolling bearing is proposed by combining it with gray wolf optimized support vector machine for fault classification. The proposed fault diagnostic method was applied to two cases of experimental data analysis of rolling bearing and the results show that it can diagnose the fault category and severity of rolling bearing accurately and the corresponding recognition rate is higher than the rate provided by the existing comparison methods. View Full-Text
Keywords: multi-scale permutation entropy; time-shift multi-scale weighted permutation entropy; rolling bearing; fault diagnosis; gray wolf optimization support vector machine multi-scale permutation entropy; time-shift multi-scale weighted permutation entropy; rolling bearing; fault diagnosis; gray wolf optimization support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Dong, Z.; Zheng, J.; Huang, S.; Pan, H.; Liu, Q. Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing. Entropy 2019, 21, 621.

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