Fault diagnosis method of rolling bearing based on attention mechanism

被引：0
|
作者：
Mao J. [1 ]
Guo Y. [1 ]
Zhao M. [1 ]
机构：
[1] School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2023年 / 29卷 / 07期
关键词：
attention mechanism; bearing fault diagnosis; bidirectional long short-term memory network; convolutional neural network;
D O I：
10.13196/j.cims.2023.07.009
中图分类号：
学科分类号：
摘要：
Aiming at the problems that traditional fault diagnosis methods cannot adaptively select features and are difficult to cope with load changes and noise interference, an end-to-end fault diagnosis method based on attention mechanism was proposed. The spatial features of the original vibration signal were extracted through Convolutional Neural Network (CNN), and the temporal features were extracted based on the Bidirectional Long Short-Term Memory Network (BiLSTM). The attention mechanism was used to judge the importance of the hidden layer state at each time of BiLSTM and give the corresponding weight. The hidden layer state at all times was weighted and summed, and the Softmax layer was used as the classifier for fault diagnosis. The data collected by VALENIAN-PT500 and public data were used for experimental verification. The results showed that the proposed method had high diagnostic accuracy and strong generalization, and could maintain good fault diagnosis performance under variable load and noise interference. © 2023 CIMS. All rights reserved.
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页码：2233 / 2244
页数：11
相关论文
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