A progressive multi-source domain adaptation method for bearing fault diagnosis

Abstract

Based on massive samples collected from various working conditions, multi-source domain adaptation-based fault diagnosis methods have been a promising way to improve the generalization capability of diagnostic models. However, insufficient multi-domain alignment and category confusion issues lead to unsatisfactory diagnostic performance in practical cross-domain scenarios. This article proposes a progressive multi-source domain adaptation method, which learns domain-uninformative and category-informative features. First, all domains are divided into multiple source-source and source-target domain pairs on different feature spaces to explore rich domain-invariant features, where local maximum mean discrepancy is introduced for domain pairwise alignment. Second, category correlation is reduced to allow ambiguous samples to obtain clear category boundaries. Extensive experimental results show the outstanding performance of our method.