多源不平衡背景下基于集成学习和双层证据融合的断路器故障诊断方法

This paper proposes a fault diagnosis method based on ensemble learning (EL) and dual-layer evidence fusion (DEF), to address the prevalent challenges of class imbalance and inter-modal conflict in circuit breaker diagnosis. In the proposed method, first, the EasyEnsemble integration strategy is employed to construct modality-specific sub-classifier ensembles for each signal source. Second, the Dempster-Shafer (D-S) evidence theory is utilized to integrate the outputs of these sub-classifier ensembles, converting the predictive results of multiple base models within the same modality into a unified, high-certainty body of evidence, thereby realizing the first-layer fusion. Finally, the independent evidence bodies outputted by each modality are combined again based on D-S theory to achieve the second-layer fusion, generating the final system decision that possesses inherent fault tolerance. A comprehensive experimental platform was established for data acquisition and validation. Results demonstrate that the proposed method achieves an F1 score of 92.85%, significantly outperforming all comparison algorithms. Crucially, the method maintains a high level of diagnostic performance even under simulated sensor failure conditions, and its ability to convert physical faults into quantifiable decision alerts via the conflict coefficient validates its effectiveness and superior robustness in complex industrial environments.