Guided deep subdomain adaptation network for fault diagnosis of different types of rolling bearings

The plentiful labeled data is indispensable for data-driven intelligent fault diagnosis of rolling bearings. But in the real world, it is difficult to gather sufficient vibration signals in advance when faults occur. Selecting an intelligent model trained by other datasets to diagnose the target signals is an effective strategy in response to the data scarcity. In this paper, a guided deep subdomain adaptation network (GDSAN) is proposed to align the feature distributions across different datasets efficiently by minimizing the discrepancy between the distributions of relevant subdomains. Specifically, the proposed method realizes alignment by comparing the consistency of source labels and target pseudo labels predicted by the source classifier. The guided learning reduces the misjudgment of target pseudo labels, which helps the subdomain with identical label finding the proper common subspace more accurately. To evaluate the superiority of the proposed model, this paper conducts transfer experiments on six rolling bearing datasets and selects four mainstream deep transfer learning networks to compare with GDSAN. The results show the fault recognition accuracy of GDSAN is prominently higher than other approaches, meanwhile verify the need of using guided subdomain adaptation.