Source-free domain adaptation framework for fault diagnosis of rotation machinery under data privacy

Abstract

Domain adaptation-based fault diagnosis method can learn knowledge from the labeled data in the related source rotation machinery and achieve reliable performance in the unlabeled target machinery. However, due to the impact of data privacy, the labeled source data are usually unavailable, severely restricting the application of the domain adaptation-based fault diagnosis methods. In this paper, a source-free domain adaptation framework is proposed to decouple the direct usage of the source data. First, the adaptive prototype memory matrix is constructed to select reliable samples, which are employed to define the pseudo labels. Then, the confidence-based filter is designed to improve the reliability of pseudo labels. Finally, the curriculum learning strategy is utilized to balance the optimal weight between the source information and target data. The effectiveness of the proposed method was verified by datasets in the rig and real wind turbine gearboxes.