Neural Network-Directed Detection and Localization of Faults in Railway Track Circuits: An Application of Dempster-Shafer Theory
Zuhair Shakor Mahmood, Ali Najdet Nasret, Abbas B. Noori, Ahmed Burhan Mohammed · Ingénierie des systèmes d information · 2023
Track circuits, integral to the security infrastructure of railway traffic systems, govern the operational status of train lines.The swift detection and rectification of faults within these circuits is critical to preserve the integrity and functionality of rail networks.In this study, an innovative approach, leveraging neural networks in tandem with Dempster-Shafer theory, is proposed for detecting and localizing faults in track circuits.The complexities of fault detection are deconstructed into more manageable, capacitor-specific pattern recognition challenges, with the resolutions amalgamated via Dempster-Shafer theory.Simulations demonstrate the efficacy of this method, yielding a detection accuracy exceeding 98% and a localization accuracy surpassing 93%.This marks a significant improvement over contemporary reference techniques, thereby setting a new benchmark in the domain of track circuit fault detection and localization.