Mechanical Fault Diagnosis of High-Voltage Circuit Breakers Based on Multi-Sensor Gramian Angular Field and Deep Residual Network

Abstract

The mechanical reliability of high-voltage circuit breakers (HVCBs) is crucial for power-grid stability, yet traditional diagnostic methods rely heavily on manually extracted scalar features that can discard transient information. This paper presents a mechanism-aware diagnostic pipeline that combines synchronized coil-current, contact-travel, and spring-pressure measurements; Variational Mode Decomposition (VMD); kinematics-driven Region of Interest (ROI) alignment; Gramian Angular Field (GAF) encoding; RGB channel stacking; and ResNet-18 classification. On a controlled 220 kV experimental platform covering five operating conditions and 1500 operating-cycle samples, the framework achieved an average accuracy of 96.18% (macro-precision 96.10%, recall 96.01%, and F1-score 96.05%) under five repeated stratified 2:1 holdout evaluations. Single-channel controls obtained 88.47% for current, 90.24% for travel, and 85.13% for pressure; removal of VMD and ROI reduced accuracy to 94.72% and 93.46%, respectively. The results should be interpreted as proof-of-concept evidence on controlled simulated faults; validation on temporally separated field data, other breaker types and voltage levels, and naturally imbalanced fault distributions remains necessary before broad condition-based-maintenance deployment.