Dynamic feature separation domain generalization for bearing fault diagnosis

Abstract Domain adaptation is a research hotspot in the field of bearing fault diagnosis; however, extremely scarce bearing fault sample information limits the development of domain adaptation techniques. Thus, improving the generalization ability of fault diagnosis models is an urgent problem to be solved. Thus, this study proposed a dynamic feature separation domain generalization method for bearing fault diagnosis. The proposed method facilitated the dynamic adjustment of the influence of internal and mutual invariant features on the domain generalization process by learning the relationship between internal invariant features within the domain and mutual invariant features between the domains. This ensured the maximum utilization of invariant information in the features and obtainment of a domain-invariant bearing fault diagnostic model. Consequently, the fault diagnosis capability of the model was improved and the shortcomings of the poor generalization capability of existing models were alleviated. Finally, the effectiveness of the method was verified by comparing several bearing-fault datasets.