Self-Distillation-Based Domain Weighted Generalization Method for Fault Diagnosis Under Actual Unseen Working Conditions

In modern industry, fault diagnosis is critical for ensuring production safety and efficiency. Intelligent fault diagnosis methods often suffer performance degradation under varying real-world operating conditions, primarily due to distribution shifts. Domain generalization (DG) techniques have been introduced to address this issue, aiming to enhance model performance under unseen working conditions. However, existing methods often overlook the significance of multi-level feature representations in learning generalizable information. Furthermore, most methods disregard the fact that not all source domains contribute equally to generalization, where certain domains provide more transferable features than others. To address these challenges, this paper proposes a self-distillation-based domain weighted generalization method for fault diagnosis. With the Vision Transformer (ViT) as the backbone, the method first incorporates a self-distillation mechanism that distills class-specific knowledge within class tokens from the final transformer block to intermediate blocks, enabling the learning of more generalized features. Second, a dynamic domain weighting strategy based on self-distillation loss is proposed, which assigns higher weights to domains with more generalized features. Finally, these weights are integrated into domain adversarial learning and fault classification process. Extensive experiments on motor fault diagnosis are conducted, covering multiple conditions including constant conditions, start-up and shut-down conditions, and the New European Driving Cycle (NEDC) conditions. Experimental results on 33 cross-condition tasks, including constant-to-constant, constant-to-variable, and variable-to-variable tasks, demonstrate that the proposed method achieves superior accuracy and robustness compared with several representative methods. Ablation studies further validate the effectiveness of each component in the proposed method.