Spectrogram-Based Fault Detection in Covered Conductors Using ResNet50V2 with SHAP and Grad-CAM Analysis

Partial discharges (PDs) in XLPE-covered conductors are critical precursors to insulation failure in medium-voltage networks. Despite advancements in radiometric PD detection using deep learning, the classification of multiple fault types remains underexplored, and model interpretability challenges hinder practical deployment. This study presents a spectrogram-based deep learning framework for classifying 12 PD fault types and background conditions using data from a BONI-WHIP antenna. By converting time-domain signals into spectrograms and employing a ResNet50V2 classifier, the framework achieves high multi-class classification accuracy. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive Explanations (SHAP) identify the spectral features influencing predictions, aligning with known PD phenomena such as high-frequency emissions during discharges. The results demonstrate the potential for explainable AI in condition monitoring, with further validation under field conditions recommended to confirm its applicability.