Source-Free Domain Adaptation for Open-Set Cross-Domain Fault Diagnosis
Jilun Tian, Hao Luo, Shimeng Wu, Pengfei Yan, Jiusi Zhang
Abstract
Source-free domain adaptation (SFDA) has emerged as a promising and practical approach to achieve better cross-domain fault diagnosis in privacy-preserving scenarios, yet face challenges in identifying target-private faults within open-set (OS) scenarios. To address this limitation, a theoretical generalization bound error is employed to analyze the root causes, which primarily stem from domain shift and OS scenarios. Guided by this theoretical foundation, a novel SFDA-OS approach is proposed to integrate target adaptation process and OS separation using entropy-based confidence index and corresponding confidence sets. It incorporates a comprehensive loss function for adaptation, combining pseudolabel learning, clustering, and uncertainty-aware updating for high-confidence samples, alongside additional clustering for low-confidence samples. Extensive experimental results validate the effectiveness of the proposed method, demonstrating its capability to provide a potential, practical, and privacy-compliant solution for deployable fault diagnosis in actual engineering systems where unknown faults emerge and source data access is restricted.