Fault Diagnosis Under Unseen Conditions via Suppressing Domain-Sensitive Feature

Abstract

Cross-domain fault diagnosis plays an important role in industrial production. Current methods are able to achieve promising performance when both source and target domains are available at training time. However, when oriented toward fault diagnosis where the target domain is unseen, they often fail to effectively extract domain-invariant features and suffer severe performance degradation. To mitigate this problem, we propose a suppressing domain-sensitive feature framework for fault diagnosis under unseen conditions. A novel masking domain-sensitive feature module is constructed to emphasize the domain-invariant components in features and close the domain gaps. A tuning domain-sensitive feature module is further introduced to reduce the effect of domain-sensitive features extracted from different domains on the classifier. In addition, a consistency constraint is proposed to constrain the classifiability of features. Extensive experiments on multiple benchmark databases demonstrate that our method is more robust and efficient in dealing with both cross-working condition and cross-machine fault diagnosis under unseen conditions compared to other methods.