TMDANN-A: Boundary-aware discriminative alignment and condition-guided multi-source at-tention for vibration-based bearing fault diagnosis under variable operating conditions

Abstract

Rolling bearing fault diagnosis is important for condition monitoring of rotating machinery. In practice, variations in operating conditions and heterogeneity among source domains of-ten cause distribution shifts in vibration signals, while labeled target-domain data are usually unavailable. To address this issue, this paper proposes TMDANN-A, a multi-source domain adaptation framework for vibration-based bearing fault diagnosis. The framework combines adversarial domain alignment, boundary-aware discriminative learning, and condition-guided source weighting to improve domain invariance and class separability. A hierarchical CNN-Transformer encoder is used to capture local impulsive features and long-range tem-poral dependencies. Experiments on the JNU and PU datasets achieve 98.51% average accu-racy over six transfer tasks. On an industrial test-rig dataset, the method further achieves 95.42% average accuracy, 95.44% Macro-F1, and 95.22% mean fault recall. The results verify its effectiveness under complex operating conditions.