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- Title
一种抗噪声的自注意力神经网络 轴承故障诊断方法.
- Authors
刘辉; 李阳
- Abstract
In order to suppress the negative influence of noise signal on fault diagnosis of bearings and solve the problem of declining accuracy of diagnosis method for bearings under noise interference in production site, a fault diagnosis method with strong robustness to noise is proposed. Firstly, the self - attention neural network is taken as main research object, the depth and width of model are analyzed, and the diagnosis effect is best when the width of model is 16 dimensions and the depth is 8 layers. Then, the necessity of position coding module in model is verified by comparative experiments. Finally, compared with CNN, LSTM, MLP, SIM and other models, the results show that the model based on self - attention neural network achieves nearly 100% diagnostic accuracy under low - intensity noise environments, and the diagnostic accuracy of the proposed model is better than that of other models under high - intensity noise environments.
- Publication
Bearing, 2023, Issue 12, p92
- ISSN
1000-3762
- Publication type
Article
- DOI
10.19533/j.issn1000-3762.2023.12.015