Explainable AI Models for Assessing Short-Circuit Propagation in Fire-Exposed Cable Bundles

Abstract

Fire-induced short-circuit propagation in cable bundles poses significant safety risks in electrical installations, nuclear facilities, and transportation systems. Traditional fault detection methods often lack interpretability, hindering root cause analysis and preventive maintenance strategies. This paper presents novel explainable artificial intelligence (XAI) models for predicting and analyzing short-circuit propagation in fire-exposed cable bundles. We develop a hybrid framework combining gradient boosting machines with SHAP (SHapley Additive exPlanations) values to provide interpretable predictions of time-to-short-circuit and failure modes. Our approach integrates thermal imaging data, cable physical properties, and environmental conditions from controlled fire tests conducted on IEEE 383-qualified cables. The proposed XAI models achieve 94.7% accuracy in predicting short-circuit occurrence within 5-second windows while providing human-interpretable feature importance rankings. Experimental validation using the NUREG/CR-6931 dataset demonstrates that insulation temperature gradient, cable bundle density, and oxygen concentration are the three most critical factors influencing short-circuit propagation. The explainable framework enables fire safety engineers to understand model decisions, identify vulnerable cable configurations, and optimize protection strategies. Our results show a 23% improvement in early fault detection compared to conventional black-box deep learning approaches, with significantly enhanced model transparency for safety-critical applications.