Adversarial domain adaptation network with pseudo-siamese feature extractors for cross-bearing fault transfer diagnosis

Abstract

The traditional domain adaptation model just uses a single (siamese) feature extractor for mapping the source domain and target domain data to a feature space simultaneously, but it may be not well suitable for the cross-machine feature mapping. To improve the performance of the cross-bearing fault transfer diagnosis, an adversarial domain adaptation network with pseudo-siamese feature extractors (PSFEN) is proposed. The core idea is to construct a pair of feature extractors with the same structure but not sharing parameters, which form a pair of pseudo-siamese feature extractors. When the source domain data differs greatly from the target domain data in the cross-machine transfer diagnosis, a pair of pseudo-siamese feature extractors is used to extract the features of source domain and target domain respectively, thus some exclusive characteristics of two domains can be obtained except for the common characteristics. It is theoretically analyzed that the distribution discrepancy obtained by the pseudo-siamese feature extractors can be closer to its actual upper limit. By reducing the more real supremum, the domain adaptation can be better achieved, thus improving the transfer diagnosis accuracy. Then, a distance metric of maximum mean discrepancy and an unbalanced adversarial training algorithm are integrated to train the pseudo-siamese feature extractors and reduce the discrepancy between the source and target domains. The effectiveness of the proposed method is verified by experiments on six cross-bearing fault transfer diagnosis tasks. The comparative results show that the proposed method have much higher diagnostic accuracy compared to six classical models.