Robust fault diagnosis of a high-voltage circuit breaker via an ensemble echo state network with evidence fusion

Abstract

Reliable mechanical fault diagnosis of high-voltage circuit breakers is important to ensure the safety of electric power systems. Recent fault diagnosis approaches are mostly based on a single classifier whose performance relies heavily on expert prior knowledge. In this study, we propose an improved Dempster–Shafer evidence theory fused echo state neural network, an ensemble classifier for fault diagnosis. Evidence credibility is calculated through the evidence deviation matrix and the segmented circle function and employed as credibility weights to rectify the raw evidence. Then, an improved Dempster–Shafer evidence fusion algorithm is proposed to fuse evidence from different echo state network modules and sensors. Unlike conventional classifiers, the proposed methodology consists of multiple echo state neural network modules. It has better flexibility and stronger robustness, and its model performance is not sensitive to network parameters. Comparative analysis indicates that it can handle the paradox evidence fusion analysis and thus can achieve better diagnostic performance. The superiority of the reported fault diagnosis approaches is verified with the experimental data of a ZN12 high-voltage circuit breaker.

Fault diagnosis of induction motor in the cooling water supply system using a multi-channel data fusion transformer with limited sample conditions*

“…Feature enhancement strategy concentrates on optimizing the model structure [29] or employing data fusion techniques [30] to directly capture the robust features from limited samples. Li et al [31] proposed a hybrid classifier for the clearance joint fault diagnosis of high-voltage circuit breakers using the Dempster-Shafer evidence algorithm to fuse different aspects of data. Wang et al [32] achieved the diagnosis of bearing faults by fusing vibration and acoustic data using a convolutional neural network.…”