Unsupervised domain-adaptive bearing fault diagnosis method based on simulation data

Aiming at the current problem that it is difficult to obtain effective rolling bearing fault data in practical diagnostic tasks and the poor generalization ability of the current diagnostic model, a fault diagnosis method based on dynamics simulation and unsupervised domain adaptation is proposed. Firstly, a rolling bearing dynamics simulation model is established, and a large amount of simulation data is obtained to serve as the source domain. Then, an unsupervised domain-adaptive transfer learning fault diagnosis approach is used, which introduces an adversarial learning strategy that maximizes and minimizes classifier differences on basis of global domain adaptation to further reduce the conditional distribution differences between source and target domain features. Finally, the feasibility and excellence of the proposed method is verified by comparing it with other transfer learning methods.