Intelligent fault diagnosis of partial deep transfer based on multi-representation structural intraclass compact and double-aligned domain adaptation

Abstract

The main challenge of fault diagnosis models based on partial domain adaptation (PDA) is to promote positive transfer in the shared label space and avoid negative transfer caused by the mismatch between the outlier and the target label spaces. To address the above challenges and enhance the diagnosis performance in the PDA scenario, this paper proposes a partial deep transfer diagnosis model (MICDDA) based on multi-representation structure intraclass compact and double-aligned domain adaptation. First, the Gabor feature extractor and multi-representation structure network are constructed to enhance the representation ability of deep features and enrich feature information. Then, the information entropy and class diversity loss of the target domain are introduced to improve the loss of the classification network, and the source class-level weight and the target instance-level weight are used to reduce the outlier features participating in the alignment of the target features. Next, an intraclass compact constraint is proposed to enhance the agglomeration of features with the same label by minimizing the distance between the feature and the centroid. Finally, a double-aligned domain adaptation strategy is designed to enhance the alignment between the source and target domains' shared features by minimizing the inter- and intra-domain alignment. Experimental results in two case studies show that the MICDDA can effectively weaken the transfer of source outlier knowledge to the target domain, capture rich and homogeneous compact fault features in PDA diagnosis scenarios, and effectively improve diagnostic performance.