Domain generalized open-set intelligent fault diagnosis based on feature disentanglement meta-learning

Abstract

Abstract Existing domain generalization-based intelligent fault diagnosis methods mainly focus on learning domain-invariant features. However, in practical scenarios, these features are difficult to extract and effectively distinguish from class-related features. Moreover, these methods often assume identical label distributions between the source and target domain, making it challenging to handle scenarios where unknown classes exist in the target domain. To address these issues, this paper proposes a domain generalized open-set intelligent fault diagnosis method based on feature disentanglement meta-learning. A binary mask feature disentanglement module is constructed to overcome the information loss caused by feature reconstruction, enabling the separation of domain-specific and class-related features. Additionally, a meta-purification loss function is defined, incorporating a correlation loss term to remove impurity features from the class-related features, and further purifying class information through feature combination pairing. The method is trained on multiple source domains using a meta-learning strategy and generalized to target domains with unknown classes. The method is utilized for bearing fault diagnosis, designing multi-task experimental scenarios under different rotational speeds, and compared with existing domain generalization methods. Experimental results show that the proposed method exhibits excellent generalization ability and effectively addresses the issue of domain generalized open-set fault diagnosis.