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

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  • Si Chen

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.

Suggested Citation

  • Si Chen, 2026. "Automatic track monitoring and fault detection using vibration sensor data in railway transport system," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 20(3), pages 362-384.
    • Handle: RePEc:ids:ijrsaf:v:20:y:2026:i:3:p:362-384
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