An ensemble and shared selective adversarial network for partial domain fault diagnosis of machinery

Abstract

Recently, some adversarial transfer learning (TL) approaches have been developed for addressing the partial domain adaptation (DA) problems in machinery fault diagnosis. However, these existing methods generally follow the partial DA framework of multiple sub-domain discriminators, which causes the overly complex model in dealing with many source classes. Moreover, the classifier mostly based on a single intelligent model with limited generalization, it may predict the wrong class-probability for unlabeled samples, which will cause the negative transfer when used in the model training. In response to the challenges, an ensemble and shared selective adversarial network (ES-SAN) is proposed in this paper. In this network, by introducing a correlation layer to correlate both class and domain informations for each sample, a single-intelligent model based shared module is constructed, which can transform between the classifier and the discriminator capable of multi sub-domain discrimination, so as to form a simplified partial DA framework. Moreover, multiple shared modules based on different intelligent models are integrated into an ensemble module, which can output reliable probability weights to promote positive transfer. Experimental investigations on two diagnosis datasets demonstrate that the proposed ES-SAN outperforms the existing methods in the partial DA diagnosis.