A Novel Weighted Adversarial Transfer Network for Partial Domain Fault Diagnosis of Machinery

Abstract

Recently, domain adaptation techniques have achieved great attention in solving domain-shift problems of mechanical fault diagnosis. However, existing methods mostly work under assumption that source domain and target domain share identical label spaces, which fail to handle those issues, where a large set of source data classes are available and target data only cover a subset of classes. To address this problem, a novel weighted adversarial transfer network (WATN) is proposed for partial domain fault diagnosis, in this article. Adversarial training is introduced to learn both class discriminative and domain invariant features, and a weighting learning strategy is adopted to weigh their contributions to both source classifier and domain discriminator. As such, the irrelevant source examples can be identified and filtered out, and the distribution discrepancy of shared classes between domains can be reduced. Experiments on two diagnosis data sets demonstrate that the proposed WATN achieves satisfactory performance and outperforms state-of-the-art methods.