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.

翻译：轨道几何监测对于保障铁路运营的安全与效率至关重要。尽管轨道检测车能够精确测量轨道几何指标，但其有限的可用性及高昂运营成本限制了在大型铁路网络中的频繁监测。近年来，在运营列车上安装车载传感器系统为高频数据采集提供了低成本替代方案，尽管其精度较低。本研究提出一种方法，通过卡尔曼滤波框架将低精度传感器振动信号与退化模型相集成，以提升轨道几何预测的可靠性。通过在轨道检测车上安装低成本传感器系统进行的实验评估表明，即使数据存在噪声，引入高频传感器数据也能显著降低预测不确定性。研究还探讨了数据记录频率如何影响可信预测区间的大小，为轨道监测与维护规划中车载传感器的最佳部署提供指导。