DEC-NWD: an approach for open set domain adaptation in fault diagnosis

Abstract

The issue of data-driven cross-domain fault diagnosis for rolling bearings has been effectively addressed through advancements in domain adaptation (DA) methods. However, most existing approaches assume the same set of labels for training data and test data. This assumption often falls short of reality, as new fault types may emerge during the testing phase, resulting in less effective DA methods based on marginal distribution. To address this issue, this study proposes an open set DA method based on domain similarity, entropy, confidence, and nuclear-norm 1-Wasserstein discrepancy (NWD). Within this method, a sample-level transferability criterion is introduced. This criterion quantifies the transferability of target samples and assigns small weights to the unknown class. The complementary nature of entropy and confidence is exploited to improve the discriminability of the network for highly uncertain predictions and to use multiple classifiers to compensate for the possible influence of prediction errors on confidence. Additionally, the NWD is utilized in this method. It treats the classifier as a discriminator and leverages the predicted discriminative information to maximize the alignment of the common classes between the source and target domains. The proposed method has been validated through extensive experiments conducted on two publicly available bearing datasets.