Global-focal Adaptation with Information Separation for Noise-robust Transfer Fault Diagnosis

Rotating machinery [1] is critical in industrial applications, where system reliability is essential to avoid financial losses and safety risks. Therefore, timely fault diagnosis is a crucial engineering priority. Deep learning-based fault diagnosis has achieved remarkable success due to its ability to extract features and model complex nonlinear relationships [2, 3]. However, industrial rotating machines operate under diverse conditions, leading to domain shifts that degrade the diagnostic performance of conventional deep learning methods [4]. Among the powerful artificial intelligence (AI) technologies, transfer learning [5] can address these limitations through cross-task knowledge transfer, where domain adaptation has become a widely adopted technique in fault diagnosis, primarily encompassing metric-based approaches, adversarial frameworks, and their hybrid variants [4, 6]. Currently, cross-domain fault diagnosis methods have been extended to encompass a wider range of diverse and practical application scenarios [7]. Given that source domain data are often more abundant in real-world settings, several studies have proposed multi-source transfer fault diagnosis approaches [8, 9]. For closed-set scenarios, various domain adaptation methods have been developed [10]. Since the label categories between source and target domains may not be completely identical, open-set domain adaptation and partial domain adaptation methods have been developed for fault diagnosis [11].