SUN Kang, ZHANG Maoqian, WANG Hao, YANG Ming, ZHAO Laijun
Journal of Vibration and Shock. 2026, 45(11): 256-266.
Rolling bearing fault diagnosis is a crucial aspect of industrial equipment health management, and deep learning-based transfer learning methods have made significant progress in this field. However, most existing research focuses on knowledge transfer from a single source domain. Real-world industrial scenarios often involve multi-source data from multiple devices, different operating conditions, or different sensors, and the fault categories in the target domain are often unpredictable, potentially including unknown fault types not occurred beforepresent in the source domain. This poses a challenge to traditional transfer learning methods. To address this, this paper proposes a progressive transfer learning diagnostic method applicable to multiple source domains and capable of identifying unknown faults, aiming to improve the accuracy and robustness of fault diagnosis under complex operating conditions. This method First， introduces a convolutional block attention mechanism was introduced to enhance the model's ability to extract key fault features. Then, a two-stage progressive training framework is was designed, including an isolation stage and an alignment stage. In the isolation stage, known and unknown fault samples in the target domain were distinguished by adaptively learning decision boundary, and optimized by combining source domain classification loss and open set identification loss, effectively mitigating the interference problems caused by multiple source domain differences and unknown categories. The isolation stage adaptively learns the decision boundary to distinguish between known and unknown fault samples in the target domain, and optimizes this by combining source domain classification loss and open set recognition loss, effectively mitigating the interference problems caused by differences between multiple source domains and unknown categories. In the alignment stage, sample weighting strategy and adversarial training were used to promote the alignment of multiple source domains and target domains in the feature space, enhance the compactness and discriminative power of intra-class features, and thereby extract domain-invariant features with greater generalization ability. The alignment phase employs a sample weighting strategy and adversarial training to align multiple source and target domains in the feature space, enhancing the compactness and discriminative power of intra-class features, thereby extracting domain-invariant features with greater generalization ability. This method was systematically validated on a multi-condition gearbox fault dataset. Experimental results show that, in open-set transfer scenarios, the proposed method improves diagnostic accuracy by 6.2% to –14.4% compared to existing mainstream methods. Ablation experiments further confirm that the attention mechanism and adaptive weighted adversarial strategy play a crucial role in improving the model's cross-domain generalization ability. This research provides an effective and practical solution for intelligent fault diagnosis in complex industrial environments.
