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Review of rolling bearing fault diagnosis based on convolutional neural network
Published:2024-03-06 author:LAI Rongshen, YAN Gaoqiang. Browse: 1190 Check PDF documents
Review of rolling bearing fault diagnosis based on convolutional 
neural network

LAI Rongshen, YAN Gaoqiang
(School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361021, China)

Abstract: With the rise of machine learning technology, deep learning (DL) was utilized in the field of fault diagnosis and underwent rapid development. Among these developments, the convolutional neural network was a deep learning model with excellent feature extraction ability, which had become a hot spot in fault diagnosis research because it was suitable for processing image data and high-dimensional data. Aiming at the problem of the traditional fault diagnosis methods in addressing the difficulties of feature extraction from bearing vibration signals and the contamination of signals by noise, to efficiently and accurately accomplish the fault diagnosis of rolling bearings, firstly, the structure of convolutional neural network(CNN)was briefly introduced, and the important progress of classical convolutional neural network model for rolling bearing fault diagnosis in recent years was studied. Then, from the perspectives of deep feature extraction, hyperparameter adjustment and network structure optimization, various methods for optimizing convolutional neural networks were briefly introduced, and the optimization methods of applying convolutional neural networks to rolling bearing fault diagnosis and the research progress made were discussed in detail. Finally, the advantages and disadvantages of several typical optimization methods were compared,and the ways to optimize convolutional neural networks from different angles were summarized. The results show that the rolling bearing fault diagnosis method based on convolutional neural network also needs to solve the problems of data imbalance, insufficient model feature extraction ability and weak generalization, and the follow-up research work should focus on multi-source data fusion, model performance optimization and multiparty technology combination.
Key words: rolling bearing; fault identification; convolutional neural network(CNN); deep learning(DL); deep feature extraction; hyperparameter adjustment; network structure optimization
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