基于一维卷积注意力门控循环网络和迁移学习的轴承故障诊断

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Abstract This paper proposes a bearing fault diagnosis approach based on one-dimensional convolution attention gated recurrent network (1DCNN-Attention-GRU) and transfer learning to improve the accuracy and generalization capability of the fault diagnosis model with small sample size. Firstly, a fault diagnosis network based on a one-dimensional convolution network (1DCNN), gated recurrent unit (GRU), and attention mechanism is constructed to decrease the dependence of traditional fault diagnosis methods on the artificial experience. Then, transfer learning is introduced, which trains the fault diagnosis model with massive source datasets and freezes the basic structure of the model, and then obtains the final model by fine-tuning its top-level structure using less target domain data. Finally, the Softmax function is employed for fault classification. The experimental results show the fault diagnosis accuracy of the proposed approach is higher than the result of 1DCNN-GRU, GRU, and support vector machine（SVM）. In addition, when 3% of target domain data is used for fine-tuning, the fault diagnosis accuracy reaches 98% for various bearing working conditions, which indicates the proposed fault diagnosis method with transfer learning can classify various bearing working conditions with small sample size.

Key words： fault diagnosis transfer learning deep learning gated recurrent unit

Received: 14 October 2021 Published: 15 February 2023

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	SHI Jingwen
	HOU Liqun

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SHI Jingwen,HOU Liqun. Bearing fault diagnosis based on 1D CNN attention gated recurrent network and transfer learning[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(3): 159-164.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I3/159

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