Hybrid multi‐scale residual network for high‐voltage circuit breakers fault diagnosis

Abstract In this letter, a novel Hybrid Multi‐Scale Residual Network (HMSR‐Net) is proposed to improve the accuracy of fault diagnosis in the spring operating mechanisms of High‐Voltage Circuit Breakers (HVCBs). To facilitate enhanced fault classification across diverse operational conditions, five distinct fault states are simulated in addition to a baseline healthy condition, thereby capturing the nuanced and multi‐scale features of vibration signals within varying working environments. The vibration signals generated during spring energy release are collected utilizing a test platform specifically for HVCBs. These one‐dimensional time‐series signals are transformed into two‐dimensional time‐frequency images by wavelet transform, thereby offering a precise and multi‐scale representation of local signal characteristics. Subsequently, the generated time‐frequency images are processed by the proposed HMSR‐Net, extracting deep and discriminative features that are further classified with a Multi‐Layer Perceptron (MLP) classifier. Experimental evaluations on field‐measured fault vibration data reveal that HMSR‐Net achieves a fault recognition accuracy of 98.2%, which notably outperforms other machine learning and deep learning approaches.