Multi-source domain transfer diagnosis method for rolling bearings based on features disentanglement and joint domain alignment

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (1) : 113-120.

ZOU Song1, DONG Shaojiang*1,2, XIA Zongyou1, MOU Xiaoyan3

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Abstract

Aiming at the problem that the feature distribution of rolling bearing vibration data collected in variable working conditions is inconsistent and the label of the sample to be diagnosed is difficult to obtain, which leads to the difficulty of bearing fault diagnosis, this paper proposes a multi-source domain transfer diagnosis method of rolling bearing based on feature disentanglement and joint domain alignment. Firstly, in order to better extract the common features of the source domain and the target domain, the convolutional autoencoder and orthogonal constraint are used to disentangle the domain shared features and the domain private features, and the domain private features are filtered out and the domain shared features are retained for inter-domain alignment. Secondly, in order to reduce the feature distribution difference between the source domain and the target domain, the Multiple Kernel Maximum Mean Discrepancy (MK-MMD) and the Correlation Alignment method (CORAL) are used to construct the fusion metric. Finally, in order to avoid the decline of diagnostic accuracy caused by the negative impact of multi-source domain differences, the source adversarial module and the migration adversarial module are used to enhance the domain confusion between the source domain and between the source domain and the target domain, and the collaborative decision-making method is used to perform feature weighted fusion to reduce the interference of weak correlation domain features, and the final fault diagnosis recognition is realized. The proposed method is verified by experiments on rolling bearing fault data sets under two variable working conditions, and compared with the single-source domain diagnosis method and other multi-source domain diagnosis methods, which proves the effectiveness and superiority of the proposed method.

Key words

Fault diagnosis / Multi-source transfer learning / Feature disentanglement / Joint domain alignment

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ZOU Song1, DONG Shaojiang1, 2, XIA Zongyou1, MOU Xiaoyan3. Multi-source domain transfer diagnosis method for rolling bearings based on features disentanglement and joint domain alignment[J]. Journal of Vibration and Shock, 2025, 44(1): 113-120

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