A class-level matching unsupervised transfer learning network for rolling bearing fault diagnosis under various working conditions

As an effective method, deep transfer learning is used to solve the problem of unsupervised fault diagnosis of rolling bearings. In the process of obtaining domain invariant features, the feature matching at the domain-level does not consider the distribution of each category in matching the global distribution of source domain features and target domain features. To solve this problem, a class-level matching transfer learning network is proposed. In this method, source domain data and target domain data from different categories are matched first. Meanwhile, domain-level matching and class-level matching are combined based on the maximum classifier discrepancy structure. Then the transfer training process is divided into three stages: domain-level matching, class-level matching, and target domain pseudo-label direct guidance network training. The proposed method utilizes the target domain pseudo-label output by the network to extract the features of the target data by self-directing. Compared with the improved maximum classifier discrepancy on the Paderborn University dataset, the proposed method improves the fault diagnosis average accuracy by 12.95% on the six transfer tasks. It significantly improves the diagnostic accuracy of unsupervised bearing fault diagnosis under various working conditions.