Source-free cross-machine fault diagnosis method in a two-stage pseudo-supervised framework

Domain adaptation plays a crucial role in cross-domain machinery fault diagnosis. Considering that the source domain data maybe not directly accessible due to privacy preservation and resource consumption, and it may differ from the machine source of target domain data, this paper proposes a source-free cross-machine fault diagnosis method. Firstly, a novel adaptive pseudo-labels refinement module is designed, which is composed of a semi-supervised clustered K-Nearest Neighbors (SSCKNN) algorithm and an adaptive threshold, to obtain high-quality and class-balanced pseudo-labels regardless of the high domain discrepancy caused by cross-machine. Furthermore, a two-stage training strategy is developed based on the pseudo-supervised contrastive learning to facilitate the knowledge transfer of target model and improve the training efficiency. Compared to the existing methods, the proposed approach outperforms in cross-machine fault diagnosis through a novel centroid definition and an adaptive threshold mechanism. In experiments, the proposed fault diagnosis method is compared with several state-of-the-art results on two bearing datasets and improves the accuracy by almost 40% in the cross-machine scenario, and 1 % − − 10 % in cross-domain scenario, proving its effectiveness and superiority.