A dual-weighted adversarial network for partial domain fault diagnosis of machinery

Abstract

Domain adaptation provides a promising approach to cross-domain fault diagnosis of rotating machinery. While many 
current methods focus on scenarios where the source and target domains share identical label spaces, a prevalent situation in
industrial production involves the target domain being a subset of the source domain, known as partial domain adaptation (PDA). 
The main challenge in PDA is the label mismatches caused by outlier classes, making the alignment between domains particularly 
difficult. To this end, a dual-weighted adversarial network (DWAN) is proposed in this paper. Specifically, a bilateral class 
weighting strategy (BCWS) is developed, which can effectively suppress negative migration at the decision boundary while 
improving the robustness of adversarial training by applying bilateral weighting to both the source and target domains. Moreover, 
a collaborative framework is developed to facilitate positive migration. The constructed global perceptual module (GPM) is highly 
correlated with the improved adversarial loss function, which adaptively adjusts the input feature map according to the accuracy 
of the target domain classification and pays more attention to the domain-invariant features. Experimental results for two cases, 
namely across different operating conditions and across different rotating components, verify the effectiveness and superiority of 
the proposed method for the PDA problem.