Few-Shot Mechanical Fault Diagnosis of High-Voltage Circuit Breakers Using Prior Knowledge-Constrained Wavelet-Based Multi-Frequency Feature Fusion

Mechanical faults in high-voltage circuit breakers (HVCBs) significantly affect power system reliability and require accurate diagnosis for condition-based maintenance. However, practical applications are limited by scarce fault samples, strong environmental noise, and insufficient incorporation of physical mechanisms in existing data-driven methods, which reduces robustness and interpretability. To address these challenges, this paper proposes a few-shot mechanical fault diagnosis framework for HVCBs based on prior knowledge–constrained wavelet-based multi-frequency feature fusion. First, domain-specific prior knowledge is constructed through excitation–response mechanism analysis, and signal feature extraction principles, providing physically meaningful guidance for feature learning. Second, a wavelet-based multi-frequency feature fusion network is developed to refine backbone features while integrating multi scale frequency-domain information, thereby enhancing the representation of weak and subtle fault characteristics. Third, physically motivated prior knowledge constraints are embedded into the feature fusion process, enabling the learned features to remain consistent with fault mechanisms. Furthermore, a joint loss optimization strategy is adopted to mitigate class imbalance, and metric learning is incorporated to support reliable few-shot classification. Experimental results on HVCB fault datasets demonstrate that the proposed method achieves a diagnostic accuracy of 98.67% in the 5-way 5-shot task. Compared with convolutional neural network and the wavelet multi-frequency feature fusion network without prior knowledge constraints, the proposed method improves the diagnostic accuracy by 8.91% and 8.11%, respectively. In addition, the proposed framework exhibits strong robustness and generalization capability under severe noise interference and sample imbalance, indicating its effectiveness and practical value for real-world HVCB condition monitoring.