Multi-source weighted source-free domain transfer method for rotating machinery fault diagnosis

The mainstream approach to addressing the issues of insufficient historical data and high annotation costs in the domain of rotating machinery is to build transfer learning models based on labeled multi-source data. However, the practical diagnosis of failure cases often relies on data privacy, thereby limiting the widespread application of current multi-source domain transfer approaches for the ‘data silos’ problem of. In view of the above problem, a multi-source weighted source-free domain transfer approach is designed for rotating machinery fault diagnosis, and the designed scheme can efficiently achieve data privacy and domain transfer. Specifically, the proposed approach achieves knowledge transfer from the source to the target during the training process of the unlabeled target data without accessing the source data. This is accomplished through the utilization of a designed reinforced information maximization strategy and improved self-training mechanism. Additionally, a weighted strategy is devised to automatically apply optimal values to all source domains based on their relevance to the target domain. The proposed framework demonstrates accuracy exceeding 96% across eight cross-domain diagnostic cases in two sets of rotating machinery data, with an average accuracy of 98.26%. These results underscore the exceptional ability of the proposed method to address cross-domain fault diagnosis in rotating machinery while ensuring privacy protection.