Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Data and Degradation Models via Kalman Filtering

TL;DR

This paper presents a Kalman filter-based method that combines low-cost, high-frequency on-board train sensors with degradation models to improve track geometry monitoring accuracy and reduce prediction uncertainty.

Contribution

It introduces a novel integration of on-board sensor data and degradation models using Kalman filtering for enhanced track geometry prediction.

Findings

Frequent sensor data reduces prediction uncertainty.

Noisy sensor data can still improve predictions.

Optimal sensor data frequency influences prediction interval size.

Abstract

Track geometry monitoring is essential for maintaining the safety and efficiency of railway operations. While Track Recording Cars (TRCs) provide accurate measurements of track geometry indicators, their limited availability and high operational costs restrict frequent monitoring across large rail networks. Recent advancements in on-board sensor systems installed on in-service trains offer a cost-effective alternative by enabling high-frequency, albeit less accurate, data collection. This study proposes a method to enhance the reliability of track geometry predictions by integrating low-accuracy sensor signals with degradation models through a Kalman filter framework. An experimental campaign using a low-cost sensor system mounted on a TRC evaluates the proposed approach. The results demonstrate that incorporating frequent sensor data significantly reduces prediction uncertainty, even when the data is noisy. The study also investigates how the frequency of data recording influences the size of the credible prediction interval, providing guidance on the optimal deployment of on-board sensors for effective track monitoring and maintenance planning.