Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering
For railway maintenance engineers, this method offers a cost-effective way to enhance track monitoring frequency and reliability, though it is an incremental improvement over existing sensor fusion techniques.
This study integrates low-accuracy on-board sensor vibration signals with degradation models via Kalman filtering to improve track geometry predictions. Results show that frequent sensor data reduces prediction uncertainty, even with noisy data, and provide guidance on optimal sensor deployment.
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 vibration 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.