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Rearing fault transfer diagnosis model based on SimAM attention mechanism

Published:2024-05-24 author:BAO Congwang, ZHU Guangyong, ZOU Wang, et al. Browse: 608 Check PDF documents

Rearing fault transfer diagnosis model based on SimAM attention mechanism

BAO Congwang1,2, ZHU Guangyong1, ZOU Wang1, GUO Hao1

(1.School of Mining and Mechanical Engineering, Liupanshui Normal University, Liupanshui 553000, China;

2.School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Abstract: Aiming at the problem that domain invariant features are difficult to extract and model overfitting is easy to occur in bearing fault migration diagnosis during cross-working condition migration diagnosis, a bearing fault migration diagnosis method based on a simple parameter-free attention module (SimAM) was proposed. Firstly, the one-dimensional convolutional neural network was used as the basic framework, and adaptive batch normalization (AdaBN) was used to normalize each output layer. After two convolutional layers and two pooling layers, the output features were deactivated by random nodes. Then, the improved parametric rectified linear unit (PReLU) activation function was used to adaptively extract the negative input weight coefficient, and the cross-entropy loss function was used to monitor the trained labeled source domain data and the mean squared logarithmic error (MSLE) was used as the loss function to train the unlabeled target data. Finally, the model was verified by the self-made experimental bench data and the open data of Case Western Reserve bearing. Different single working conditions were taken as the source domain, and the other working conditions were taken as the target domain to carry out the migration diagnosis task. The experimental results show that the proposed method has good domain invariant feature extraction performance, and the proposed features have good clustering effect. The average migration accuracy of the self-made experimental bench was above 89.1%, the highest mean was up to 97.85%, and the average migration accuracy of CWRU dataset was up to 98.68%. The results can lay a foundation for subsequent bearing fault transfer diagnosis from experimental data to industrial sites.

Key words: bearing fault diagnosis; transfer learning; simple parameterfree attention module (SimAM); adaptive batch normalization (AdaBN); parametric rectified linear unit (PReLU); mean squared logarithmic error (MSLE); convolutional neural network

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