An open-set classifier-guided dynamic joint domain adaptation for fault diagnosis of rolling bearings

Domain adaptation (DA) techniques have shown effectiveness in cross-domain fault diagnosis; however, their performance is constrained by the closed-set assumption, which is often unrealistic in complex industrial environments. The inherent uncertainty and complexity of mechanical systems in industrial big data often lead to the emergence of novel and unknown fault types. To address this challenge, a practical open-set DA approach was proposed, named open-set classifier-guided dynamic joint domain adaptation (OC-DJDA). Built upon the domain adversarial neural network, OC-DJDA incorporates two key strategies, as suggested by its name. An open-set classifier (OC) is introduced to detect unknown samples and delineate clear boundaries between known and unknown classes without requiring predefined thresholds. Meanwhile, target domain-specific features are isolated to form a shared domain, thereby mitigating the influence of unknown categories during feature alignment. A dynamic joint distribution strategy is employed to adaptively align both marginal and conditional feature distributions across domains through a dynamic weighting mechanism. Experimental results demonstrate the robustness of OC-DJDA and highlight its potential as a reliable solution for open-set fault diagnosis in complex industrial scenarios. Notably, OC-DJDA surpasses several state-of-the-art methods, achieving classification accuracies of 94.79% and 91.70% in open transfer tasks within the same machine and across different machines, respectively.