Class-Conditional Progressive Calibration under Selective Decoupled Domain Adaptation for Long-Tailed Cross-Condition Bearing Fault Diagnosis

Rolling bearings are critical supporting components of rotating machinery and directly affect the safe and reliable operation of industrial equipment. Rolling bearing fault diagnosis remains challenging when operating-condition variations coexist with long-tailed fault distributions. Under such settings, uniform domain alignment may introduce unreliable transfer across operating conditions, while long-tailed class priors can bias target supervision toward head classes. To address these issues, we propose Selective Decoupled Domain Adaptation with Class-Conditional Progressive Calibration (SDDA-CCPC). Specifically, SDDA separates fault-discriminative and condition-related representations and performs reliability-weighted domain alignment to reduce the influence of unreliable cross-condition transfer. CCPC calibrates target predictions under long-tailed class priors and combines classifier–prototype agreement with progressive within-class selection to construct more reliable target supervision for minority classes. Across six long-tailed unsupervised cross-condition transfer tasks under different load conditions, SDDA-CCPC achieves an average Macro-F1 of 0.9178 and improves both overall diagnostic performance and tail-class recognition. For the extreme tail inner-race fault class, the average F1-score improves by 3.91%. Ablation results further demonstrate the complementary contributions of SDDA and CCPC.