Multi-domain weakly decoupled domain generalization network for fault diagnosis under unknown operating conditions

The utilization of transfer learning strategies to solve cross-domain fault diagnosis problems has achieved significant results. However, most existing multi-source domain generalization fault diagnosis methods use a single classifier or introduce auxiliary classifiers, focusing on learning domain-invariant features or global feature distribution matching. Furthermore, since the data distributions of different source domains may be significantly different, this may lose the data distribution information specific to each source domain. In addition, how to reduce the variation in risk between samples within the same domain training is also a challenging issue. Finally, it is also crucial to balance the predictive outputs of multiple classifiers to adapt them to the data distribution of the target domain. Based on the above challenges, this paper proposes a multi-domain weakly decoupled domain generalization network for fault diagnosis under unknown operating conditions. Feature weakly decoupled mechanism is achieved by employing multiple classifiers and incorporating the variance of samples within the same sample domain as a penalty term. This reduces the model’s sensitivity to changes in the extreme distribution of samples within the domain. Classifier weakly decoupled mechanism, on the other hand, reduces the inter-domain risk variance by minimizing the loss of variance in the predicted output of the source domain classifiers. This improves the robustness of the model to inter-domain distributional changes and covariate changes. Experimental results on three datasets validate the effectiveness and general applicability of the proposed approach.