Single domain generalization method based on anti-causal learning for rotating machinery fault diagnosis

Single-domain generalization (SDG) fault diagnosis methods are promising approach because they can diagnose unknown domains by training only one domain. However, there have been fewer studies on fault diagnosis for SDG. Existing studies focus on extending the distribution of source domain, but fail to consider how to estimate domain shifts between domains. Therefore, we propose an SDG fault diagnosis learning paradigm, i.e., simulation-inference-adaptation, which first establishes a pseudo-domain as the target domain to simulate the domain shifts, then reasons the causes of the domain shifts, and finally performs the domain adaptation. In this paradigm, an anti-causal learning approach is proposed, whereby the cause of the domain shifts between the pseudo-domain and the source domain is inferred during training and the learned knowledge is used to analyze the cause of the domain shifts between the target domain and the source domain during testing. Specifically, data augmentation is utilized to perform combinatorial transformation of source data to generate pseudo-domains, perform anti-causal inference to learn to discover causal factors for domain shift between pseudo-domains and source domains, and impose the inferred causality into a weighted domain adaptation network. Finally, the experimental demonstration validated the proposed method's validity and its ability to inference causality.