A Diagnosis Method of Rail-to-Earth Insulation Based on Circuit-Field Coupling Model

A rail-to-earth insulation diagnostic method is proposed for subway systems, leveraging a computationally efficient improved field-circuit coupling model (IFCM) combined with an improved Minkowski distance (IMD) metric. The model innovatively excludes rail electric field effects and relocates leakage sources to the stray current collection network (SCCN), achieving dynamic rail-to-earth resistance (RE) simulations with deviations below 5.47% compared to experimental measurements. A multi-signal fusion approach combines rail potential (RP) and SCCN polarization potential (PP), employing time-varying filtered empirical mode decomposition (TVF-EMD) denoising and IMD-based feature extraction to quantify insulation degradation. In dynamic simulations, high computational efficiency is maintained by the IFCM, which shows a relative error of 8.09% compared to CDEGS results. Rigorous metrological validation using 5,000 defect samples demonstrates 98.09% classification accuracy, with diagnostic thresholds optimized via the Youden index. Statistical bootstrapping confirms robustness, while sensitivity analyses validate performance under noise interference, with energy feedback system (EFS) in operation, and localized insulation variations.