Open-Set Black-Box Domain Adaptation for Mechanical Fault Diagnosis via Reliable Pseudolabel Learning

Abstract

Black-box domain adaptation (BBDA) technologies have been extensively studied to transfer diagnosis knowledge from the source model to the target domain without accessing source data and source model parameters. However, existing BBDA methods assume identical fault categories across domains, which rarely holds in open and uncertain industrial environments. To solve this challenge, a novel reliable pseudolabel learning (RPLL) method is proposed for open-set black-box cross-domain fault diagnosis. In RPLL, a disentangled knowledge distillation module is designed to transfer fine-grained diagnosis knowledge from the black-box source model to the target model, effectively mitigating the impact of noisy source model predictions. In addition, an entropy-oriented label division module is designed to enhance the distinction between known and unknown fault categories. These two modules operate synergistically and achieve excellent known fault recognition and unknown fault detection via entropy-based adaptive thresholds. Extensive experiments show that the proposed RPLL achieves comprehensive diagnosis accuracies of 92.09% and 88.56% in cross-condition and cross-machine transfer tasks, respectively, significantly outperforming existing methods.