A Novel Fault Diagnosis Method for Rolling Bearing Based on Hierarchical Refined Composite Multiscale Fluctuation-Based Dispersion Entropy and PSO-ELM.

Content Provider	Europe PMC
Author	Chen, Yinsheng Yuan, Zichen Chen, Jiahui Sun, Kun
Editor	Kuruoglu, Ercan
Copyright Year	2022
Abstract	This paper proposes a novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) and particle swarm optimization-based extreme learning machine (PSO-ELM). First, HRCMFDE is used to extract fault features in the vibration signal at different time scales. By introducing the hierarchical theory algorithm into the vibration signal decomposition process, the problem of missing high-frequency signals in the coarse-grained process is solved. Fluctuation-based dispersion entropy (FDE) has the characteristics of insensitivity to noise interference and high computational efficiency based on the consideration of nonlinear time series fluctuations, which makes the extracted feature vectors more effective in describing the fault information embedded in each frequency band of the vibration signal. Then, PSO is used to optimize the input weights and hidden layer neuron thresholds of the ELM model to improve the fault identification capability of the ELM classifier. Finally, the performance of the proposed rolling bearing fault diagnosis method is verified and analyzed by using the CWRU dataset and MFPT dataset as experimental cases, respectively. The results show that the proposed method has high identification accuracy for the fault diagnosis of rolling bearings with varying loads and has a good load migration effect.
Journal	Entropy (Basel, Switzerland)
Volume Number	24
PubMed Central reference number	PMC9689796
Issue Number	11
PubMed reference number	36359611
e-ISSN	10994300
DOI	10.3390/e24111517
Language	English
Publisher	MDPI
Publisher Date	2022-10-24
Access Restriction	Open
Rights License	Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). © 2022 by the authors.
Subject Keyword	rolling bearing fault diagnosis feature extraction hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) particle swarm optimization-based extreme learning machine (PSO-ELM) load migration
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy Information Systems Electrical and Electronic Engineering Mathematical Physics