A bearing fault diagnosis based on similarity measurement for transfer learning

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (16) : 217-223.

XU Yiyun1,MA Jian1,CHEN Liang1,SHEN Changqing2,LI Qi1,KONG Lin3

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Abstract

According to the variable working conditions of rolling bearings due to the influence of load, speed and other factors, the feature distribution deviation of fault signals will significantly reduce the generalization ability of fault diagnosis model. To solve this problem, a bearing fault diagnosis method based on similarity measurement for transfer learning was proposed. The idea of transfer learning and similarity measurement were combined. The correlation between fault features under variable conditions is calculated through correlation alignment loss, and the distribution difference between source domain and target domain features is minimized. At the same time, the similarity between the input feature and the central feature is maximized, and the fault classification information contained in the label predicted by the target domain is used to improve the accuracy of fault feature clustering, so as to increase the contribution of highly correlated features to the model and reduce the influence of non-correlated features. Finally, the features learned are used to implement fault classification. Comparison experiments on CWRU and self-built experimental platform prove that the proposed method can classify fault signals more accurately and solve the difficult problems of fault diagnosis caused by feature distribution deviation under different working conditions.
Key words: rolling bearing; fault diagnosis; transfer learning; similarity measurement; feature distribution

Key words

rolling bearing / fault diagnosis / transfer learning / similarity measurement / feature distribution

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XU Yiyun1,MA Jian1,CHEN Liang1,SHEN Changqing2,LI Qi1,KONG Lin3. A bearing fault diagnosis based on similarity measurement for transfer learning[J]. Journal of Vibration and Shock, 2022, 41(16): 217-223

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