Adaptive_Multiscale_Weighted_Permutation_Entropy_for_Rolling_Bearing_Fault_Diagnosis.pdf (16.9 MB)
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Adaptive multiscale weighted permutation entropy for rolling bearing fault diagnosis

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journal contribution
posted on 04.08.2021, 09:58 by Zhiqiang Huo, Eve ZhangEve Zhang, Gbanaibolou Jombo, Lei Shu
Fault diagnosis of rolling bearing is of great importance to ensure high reliability and safety in the industrial machinery system. Entropy measures are useful non-linear indicators for time series complexity analysis and have been widely applied in bearing fault diagnosis in the past decade. In this paper, an improved entropy measure is proposed, named Adaptive Multiscale Weighted Permutation Entropy (AMWPE). Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, an experimental bearing dataset is analyzed using the AMWPE and conventional entropy measures, and then multi-class SVM is adopted for fault type classification. Further, the robustness of different entropy measures against noise is studied by analyzing noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in bearing fault diagnosis under different fault types, severity degrees, and SNR levels.

Funding

International and Hong Kong, Macao and Taiwan Collaborative Innovation Platform and Major International Cooperation Projects of Colleges in Guangdong Province under Grant 2015KGJHZ026.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Access

Volume

8

Pages

87529 - 87540

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

22/04/2020

Publication date

2020-05-06

Copyright date

2020

eISSN

2169-3536

Language

en

Depositor

Dr Eve Zhang. Deposit date: 29 July 2021