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Rolling bearing intelligent fault diagnosis method based on IPSO-WCNN

Content Provider	SAGE Publishing
Author	Chen, Ronghua Gu, Yingkui Wu, Kuan Li, Cheng
Copyright Year	2022
Abstract	In the bearing fault diagnosis process using the convolution neural network (CNN), there are some problems, such as complex signal data processing and the complex network parameter setting. A rolling bearing fault diagnosis method is proposed to solve these problems based on improved particle swarm optimization and convolution neural networks with wide kernels in first-layer (IPSO-WCNN). The particle self-adaptive jump out algorithm is proposed to overcome particle swarm optimization (PSO) shortcomings. The adaptive inertia weight and the linear change acceleration coefficients are adopted for improved particle swarm optimization (IPSO). The convolution neural networks with wide kernels in first-layer (WCNN) fault diagnosis method is proposed for one-dimensional rolling bearing vibration signals, and the parameters of the WCNN is optimised by IPSO. According to the verification experiments, the proposed method can get higher accuracy than others with good adaptability.
Related Links	https://journals.sagepub.com/doi/pdf/10.1177/00202940221092109?download=true
Starting Page	681
Ending Page	693
Page Count	13
ISSN	00202940
Issue Number	3-4
Volume Number	56
Journal	Measurement and Control (MAC)
DOI	10.1177/00202940221092109
Language	English
Publisher	Sage Publications UK
Publisher Date	2022-10-28
Publisher Place	London
Access Restriction	Open
Rights Holder	© The Author(s) 2022
Subject Keyword	convolution neural network Particle swarm optimization rolling bearing fault diagnosis
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Control and Optimization Instrumentation

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