Rolling Bearing Dynamics Simulation Information-Assisted Fault Diagnosis with Multi-Adversarial Domain Transfer Learning

To address the issues of negative transfer and reduced stability in transfer learning models for rolling bearing fault diagnosis under variable working conditions, an unsupervised multi-adversarial transfer learning fault diagnosis algorithm based on bearing dynamics simulation data is proposed. Firstly, the algorithm constructs both a global domain classifier and a subdomain classifier. In the subdomain classifier, the simulated vibration signal, which contains rich bearing fault label information, is generated by constructing dynamic equations to replace the label prediction of target domain data, thereby achieving alignment of marginal and conditional distributions. Simultaneously, an improved loss function with embedded maximum mean discrepancy is designed to reduce the feature distribution gap between source and target domain data. Finally, a weight allocation mechanism for source domain and simulation domain samples is developed to promote positive transfer and suppress negative transfer. Experiments were conducted using the Paderborn University dataset and the Huazhong University of Science and Technology dataset, achieving accuracy rates of 89.457% and 96.436%, respectively. The results show that, in comparison with existing unsupervised cross-domain fault diagnosis methods, the proposed method demonstrates significant improvements in diagnostic accuracy and stability, demonstrating its superiority in rolling bearing fault diagnosis under variable operational conditions.