LAUSR

As the different distributions between the source and target domains, rolling bearing fault diagnosis based on domain adaptation (DA) has achieved a good result in unlabeled sample fault diagnosis of target domain. However, most transfer strategies focus mainly on the domain-invariant feature between the source and target domains, with little attention paid to the intrinsic feature of the target domain. This limitation causes poor knowledge transfer from the source domain due to domain confusion. Therefore, this article proposes a novel method for unlabeled rolling bearing fault diagnosis in the target domain that leverages both the intrinsic features of the target domain and the shared label space between the two domains. First, a new deep clustering network is constructed to extract the intrinsic features of the target domain, where these features are clustered several central areas based on their same attributive feature. Second, the novel adversarial DA network is designed to extract the invariant features to construct the sharing label space between the source and target domains. Third, the new pseudolabel technology is introduced to mark the target unlabeled data by fusing the invariant feature of the target domain with the sharing label space and eliminate the negative knowledge transfer. Finally, these marked labels combining the intrinsic feature of the target domain are used to train the fault diagnosis classifier, where this realizes a well-prediction model for the target domain. Several transfer tasks on two rolling bearing datasets demonstrate the efficiency of the proposed method. The proposed transfer technology realizes the accuracy prediction of rolling bearing state on target unlabeled samples. Moreover, the efficiency of the method is better than the contrasting method, and this new diagnostic technology offers a promising fault diagnosis tool under some practical industrial problem.