Source-free domain adaptation via robust pseudo-label optimization for cross-domain wind turbine bearing diagnosis

Abstract

Newly built wind farms often face three practical barriers for bearing fault diagnosis: limited fault samples, expensive labeling, and restricted access to historical source-domain data due to privacy and security requirements. This paper presents a source-free domain adaptation framework named dual perturbation robust pseudo-label optimization (DPR-PLO) for cross-domain wind turbine bearing diagnostics using only a pre-trained source model and unlabeled target data. DPR-PLO combines noise-aware hybrid perturbation learning with dynamic prototype alignment to improve robustness against condition-induced distribution shifts. Specifically, it applies time–frequency perturbations and feature-space adversarial perturbations to enhance target diversity, constructs discriminative target representations via dynamic clustering and prototype updating, and adopts a two-stage pseudo-label refinement procedure to reduce noise propagation during self-training. Experiments on Paderborn University Dataset, Jiangnan University Dataset, and a self-collected dataset demonstrate that DPR-PLO achieves 97.56% average accuracy across 18 cross-condition tasks, surpassing benchmark methods by 6.95% and yielding more reliable pseudo-labels. These results indicate that DPR-PLO provides a feasible and robust solution for privacy-preserving bearing health management in new wind turbines.