轨道几何监测的传感器融合：通过卡尔曼滤波集成车载状态监测与退化模型
Sensor Fusion for Track Geometry Monitoring: Integrating On-Board Condition Monitoring and Degradation Models via Kalman Filtering
AI总结：
本研究提出一种通过卡尔曼滤波融合低成本车载传感器振动信号与退化模型的方法，以提升轨道几何预测的可靠性，并实验验证了频繁传感器数据能显著降低预测不确定性。
摘要：
轨道几何监测对于维护铁路运营的安全性和效率至关重要。虽然轨道检测车（TRCs）能提供轨道几何指标的精确测量，但其有限的可用性和高昂的运营成本限制了在大型铁路网络中的频繁监测。近年来，安装在运营列车上的车载传感器系统提供了一种成本效益高的替代方案，能够实现高频但精度较低的数据采集。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结合，以增强轨道几何预测的可靠性。一项使用安装在TRC上的低成本传感器系统的实验活动评估了所提出的方法。结果表明，即使数据存在噪声，融入频繁的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，为有效轨道监测和维护规划中车载传感器的最优部署提供指导。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
