A Novel Acoustic-Vibration Fusion-Based Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers

Abstract

Acoustic-vibration multimodal fusion technology has developed rapidly in the field of equipment fault diagnosis due to its ability to effectively suppress noise interference and enhance diagnostic reliability. However, existing acoustic-vibration multimodal fusion methods have not been adapted to the transient impact characteristics and structural features of circuit breakers, and still suffer from issues such as modal heterogeneity, feature redundancy, poor noise robustness, and insufficient real-time performance. To address these challenges, this article proposes an acoustic-vibration multimodal fusion and shared-private decoupled network method for high-voltage circuit breakers. For vibration signals, a wavelet synchronous compression transform (WSST) is employed to enhance time-frequency resolution and accurately capture transient impact characteristics. For acoustic signals, per-channel energy normalization-Mel spectrum is constructed to suppress steady-state background noise and enhance transient fault features. Based on the modal decoupling theory, a shared-private dual-branch encoder is designed to decouple cross-modal-shared information from single-modal unique features, and combined with the self-attention mechanism to achieve deep fusion of multimodal features. The experimental results show that the proposed method achieved a diagnostic accuracy of 98.02% on the test set. Compared with existing diagnostic methods, this method offers higher diagnostic accuracy, greater robustness, and better engineering practicality, providing a viable technical solution for intelligent online fault diagnosis of high-voltage circuit breakers.