内对抗指导无监督多域适配网络的轴承协同故障诊断

Most of the current research on unsupervised cross-domain intelligent fault diagnosis of bearings is based on single-source domain adaptiveness, which fails to simultaneously use multiple source domains with adequate and diverse diagnostic data in practical application scenarios. The main difficulty in diagnosing bearing faults is how to more effectively extract shared characteristics of defective bearings from various source domains and combine multi-source domain knowledge for collaborative diagnosis. A proposed intra-adversarial guided unsupervised multi-domain adaptation network (IAG-MDAN) aims to address these issues. In particular, an intra-adversarial module is first constructed to determine the multi-source domain adversarial loss, and multi-sets of sources and target domain adaptive subnetworks are combined to guide the extraction of common features between multi-source and target domains, which improve the knowledge coverage. In addition, a multi-subnet collaborative decision module is designed to calculate confidence scores using the adversarial loss and distribution difference loss of multiple source-target domains, which assists the multi-subnet classifier in making better fusion decisions and improving the accuracy of collaborative fault diagnosis. Several unsupervised multi-source domain migration diagnosis tasks are performed using faulty bearing datasets under both constant and variable speed conditions, and the comparative experimental results show the superiority and robustness of the proposed method.