Multi-sensor Decision Approach for HVCB Fault Detection Based on the Vibration Information

Mechanical faults of high-voltage circuit breakers (HVCBs) always occur during long-term operation; thus, the recognition of such fault types has become a key issue. Now, most related research involves improvements to the classification method for higher precision based on a single sensor, ignoring the multi-sensor information fusion of the vibration signal for the fault diagnosis of HVCB. Classical evidence theory is a common approach for solving multi-sensor fusion. However, it fails upon encountering conflicting evidence. Therefore, this study proposes an improved multi-sensor evidence combined rule to optimize the evidence gathered from different sensors, especially for the highly conflicting and invalid evidence. A numerical simulation example indicates that the proposed method achieves a better performance. On this basis, the proposed method is applied to diagnose HVCB faults with multi-sensor. Further, the superiority of the multi-sensor fusion is proved via a comparison with the diagnostic results of five typical classifiers under a single sensor; specifically, by comparing it with traditional Dempster’s combination rules, it is verified that the proposed method is better than other multi-sensor fusion methods, which can be used in the field of HVCB fault diagnosis.