A multi-source ensemble domain adaptation method for rotary machine fault diagnosis

Abstract Transfer learning has good ability to transfer knowledge for fault diagnosis under working condition, while domain mismatches or domain shift can still occur during single-source domain transfer fault diagnosis. To alleviate the problem, a multi-source ensemble domain adaptation method is proposed for rotary machinery fault diagnosis. Firstly, multi-source and target domain anchor adapters are constructed based on class-central samples from multi-source domain. Secondly, multi-source domain adaptation ensemble transfer fault diagnosis model considering the mutual difference between multi-source domain is established to obtain multiple classifiers and prediction results. Then the classifiers with good performance are integrated to achieve final diagnosis model and results by ensemble of anchor adapters. Finally, the performance of the proposed method is verified by two experiments. The results show that the proposed method has ability to learn more comprehensive and general domain invariant diagnosis knowledge, significant diagnosis performance and robustness than other transfer learning methods.