Multisource Domain Feature Adaptation Network for Bearing Fault Diagnosis Under Time-Varying Working Conditions

Abstract

Intelligent fault diagnosis methods based on domain adaptation (DA) have been extensively employed for tackling domain shift problems, and the basic diagnosis tasks under time-varying working conditions were well achieved. Nevertheless, the existing methods usually focus on learning diagnosis knowledge from single-source domain while ignoring abundant underlying information in multisource domain. In practical scenarios, multiple source domains are available, and there are few studies in fault diagnosis based on multisource DA. To this end, a novel multisource domain feature adaptation network (MDFAN) is proposed for bearing fault diagnosis under time-varying working conditions. The proposed network first uses a feature extractor to learn transferable features from different pairs of source and target domains, and then, a domain-specific distribution alignment module is constructed, which adopts the intra-domain alignment strategy and the inter-domain alignment strategy to reduce the shift between all domain pairs. Meanwhile, considering the classifier prediction disagreement, a classifier alignment module is further designed to relieve the classifier prediction discrepancy and enhance the prediction consistency. Case studies on two real datasets with multiple sources demonstrate the effectiveness of the proposed network, and the comparison results show its robustness and superiority.