Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals-Reference-Cited by-同舟云学术

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Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals

Published:2021-08 Issue: Volume:114 Page:470-484
ISSN:0019-0578
Container-title:ISA Transactions
language:en
Short-container-title:ISA Transactions

Author:

Wang Zhenya,Yao Ligang,Chen Gang,Ding Jiaxin

Funder

Natural Science Foundation of Fujian Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Control and Systems Engineering,Electrical and Electronic Engineering,Computer Science Applications,Instrumentation

Reference45 articles.

1. Bearing fault diagnosis under time-varying rotational speed via the fault characteristic order (FCO) index based demodulation and the stepwise resampling in the fault phase angle (FPA) domain;Wang;ISA Trans,2019

2. A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network;An;ISA Trans,2020

3. Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review;Habibi;Renew Energy,2019

4. Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance;Zhang;ISA Trans,2019

5. An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis;Cheng;ISA Trans,2019

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