Imbalanced multidomain generalization fault diagnosis based on prototype-guided supervised contrastive learning with dynamic temperature modulation

Abstract

Data-driven fault diagnosis approaches have been extensively investigated for rotating machinery. However, real-world industrial environments present significant challenges: shifting operating conditions lead to distribution differences in monitoring data, and unknown target operating conditions result in a lack of target domain data for training. Additionally, monitoring data often exhibit class imbalance, with common healthy states dominating the dataset, while rare but critical fault types are underrepresented. These issues severely impair the generalization capability of diagnostic models and remain largely unaddressed. To tackle these challenges, we propose a novel learning framework, Prototype-guided Supervised Contrastive Learning with Dynamic Temperature Modulation (DTM-PSCL), designed to effectively capture imbalanced domain-invariant features. The proposed approach incorporates class prototypes to address contrastive imbalance and introduces a regularization technique to approximate cross-domain prototypes. Furthermore, we develop an exponential dynamic temperature modulation strategy in contrastive learning, which enhances training stability and enables the model to adaptively learn imbalanced domain-invariant features. Additionally, a class-balanced loss is used to prioritize underrepresented classes, reducing the model’s bias toward dominant classes. Extensive experiments on four well-known datasets consistently demonstrate the effectiveness and superiority of the proposed framework, suggesting a promising methodology for fault diagnosis of rotating machinery under unknown operating conditions.