Multi‐Information Fusion Diagnosis of High‐Voltage Circuit Breakers via Newton–Raphson Optimised Transformer and Meta‐Transfer Learning

ABSTRACT Mechanical fault diagnosis of high‐voltage circuit breakers (HVCBs) remains challenging due to the complex and nonstationary nature of vibration signals, scarcity of fault samples, and the limited feature‐extraction capacity of existing few‐shot learning models. To address these challenges, this paper proposes a multi‐information fusion diagnostic framework that integrates a Newton‐Raphson optimised Transformer with meta‐transfer learning (MTL). Specifically, entropy‐weighted fusion is introduced to suppress conflicting channels and aggregate multi‐directional vibration measurements into an informative representation. To improve training stability and reduce sensitivity to manual trial‐and‐error under limited data, a Newton–Raphson‐based optimiser is employed offline to select key Transformer hyperparameters. For data‐scarce and cross‐scenario diagnosis, a meta‐transfer learning scheme with a lightweight scale‐shift adaptation module enables fast adaptation while mitigating overfitting. The proposed framework is validated on a self‐developed multimodal vibration acquisition platform and compared with representative baselines. Experimental results show that the proposed approach achieves the highest diagnostic accuracy (98.45%) and F1‐score (98.26%) under 5‐way 5‐shot settings, outperforming conventional baselines by 2.0%–5.5%. The method exhibits strong interpretability and adaptability to variable operating conditions, providing a reliable solution for intelligent mechanical fault diagnosis of HVCBs.