Partial adversarial domain adaptation by dual-domain alignment for fault diagnosis of rotating machines

Abstract

Domain adaptation (DA) techniques have succeeded in solving domain shift problem for fault diagnosis (FD), where the research assumption is that the target domain (TD) and source domain (SD) share identical label spaces. However, when the SD label spaces subsume the TD, heterogeneity occurs, which is a partial domain adaptation (PDA) problem. In this paper, we propose a dual-domain alignment approach for partial adversarial DA (DDA-PADA) for FD, including (1) traditional domain-adversarial neural network (DANN) modules (feature extractors, feature classifiers and a domain discriminator); (2) a SD alignment (SDA) module designed based on the feature alignment of SD extracted in two stages; and (3) a cross-domain alignment (CDA) module designed based on the feature alignment of SD and TD extracted in the second stage. Specifically, SDA and CDA are implemented by a unilateral feature alignment approach, which maintains the feature consistency of the SD and attempts to mitigate cross-domain variation by correcting the feature distribution of TD, achieving feature alignment from a dual-domain perspective. Thus, DDA-PADA can effectively align the SD and TD without affecting the feature distribution of SD. Experimental results obtained on two rotating mechanical datasets show that DDA-PADA exhibits satisfactory performance in handling PDA problems. The various analysis results validate the advantages of DDA-PADA.