Title: Automatic track monitoring and fault detection using vibration sensor data in railway transport system

Authors: Si Chen

Addresses: School of Computer Science and Technology, Changchun Normal University, Jilin, Changchun, China

Abstract: The development of a real-time automatic track monitoring system can detect the condition of the track without interrupting normal traffic. The objective of this study is to classify railway tracks as healthy or defective with the help of vibration readings and environmental factors such as temperature and humidity. The proposed fault detection system investigates the efficiency of several ML algorithms, which are combined with the Subtraction-Average-Based Optimiser for hyperparameter tuning to improve their performance in classifying railway tracks. According to results, the SABO-RF model provides a strong and reliable approach to real-time fault detection with 99.86% accuracy and 99.84% precision, which contributes toward preventing accidents and minimising operational disruption in railway systems. The sensitivity of the SABO-RF model outlines the influence of vibration in dimension y with a strong positive contribution of +4.24 and a strong negative contribution of −3.87 in classifying railway tracks by model.

Keywords: railway transportation system; railway track; fault detection; vibration sensors data; data-driven approach; hyperparameter tuning.

DOI: 10.1504/IJRS.2026.155218

International Journal of Reliability and Safety, 2026 Vol.20 No.3, pp.362 - 384

Received: 16 Jun 2025
Accepted: 12 Sep 2025
Published online: 29 Jul 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article