Multi-Sensor Sensitivity Assessment Strategy for High-Voltage Circuit Breaker Fault Diagnosis Using GA-SoftMax Model

Research on the mechanical fault diagnosis of high voltage circuit breakers (HVCBs) often relies on a single sensor as the data foundation, resulting in issues such as a limited variety of sensor types and insufficient depth in feature evaluation. Therefore, this paper addresses these challenges by considering the sensitivity of sensors to various faults and proposes a novel decision fusion method based on the sensitivity assessment of multiple sensor types at various measurement points. This approach involves constructing a fault sensitivity assessment function using multiple distance metrics and optimizing parameters through the Genetic Algorithm-SoftMax (GA-SoftMax) model. Finally, the SoftMax model is employed to estimate potential fault probabilities, and a novel probability-weighted decision fusion framework is introduced, adjusting diagnostic results based on the fault sensitivity of each sensor. Through a comparative analysis of four typical decision-making scenarios, the diagnostic accuracy of the method proposed in this paper reaches 91.1%, with an F1-Score of 0.98. This represents a substantial improvement over single-sensor approaches, demonstrating superior diagnostic performance compared to other decision fusion framework. This research provides a novel perspective for advancing mechanical fault diagnosis in HVCBs.