Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data

TL;DR

This paper presents a data analytics approach using SVM to automatically detect specific component failures in AC track circuits, enhancing maintenance accuracy for the Smart Train Detection System.

Contribution

It introduces a novel failure detection method applying SVM to STDS data, capable of classifying 15 failure types across 3 categories with field validation.

Findings

All failure cases were correctly classified.

Method validated with field data from 10 track circuits.

Improves maintenance decision-making for track circuit systems.

Abstract

Track Circuits (TC) are the main signalling devices used to detect the presence of a train on a rail track. It has been used since the 19th century and nowadays there are many types depending on the technology. As a general classification, Track Circuits can be divided into 2 main groups, DC (Direct Current) and AC (Alternating Current) circuits. This work is focused on a particular AC track circuit, called "Smart Train Detection System" (STDS), designed with both high and low-frequency bands. This approach uses STDS current data applied to an SVM (support vector machine) classifier as a type of failure identifier. The main purpose of this work consists on determine automatically which is the component of the track that is failing to improve the maintenance action. Model was trained to classify 15 different failures that belong to 3 more general categories. The method was tested with field data from 10 different track circuits and validated by the STDS track circuit expert and maintainers. All use cases were correctly classified by the method.