A Train-Borne Laser Vibrometer Solution Based on Multisignal Fusion for Self-Contained Railway Track Monitoring

IF 9.9 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Informatics Pub Date : 2024-11-25 DOI:10.1109/TII.2024.3485764
Yuanchen Zeng;Alfredo Núñez;Rolf Dollevoet;Arjen Zoeteman;Zili Li
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Abstract

This article develops and tests a self-contained railway track monitoring system that fits in existing vehicles without the need for speed and load control. Combining a train-borne laser Doppler vibrometer and axle box accelerometers enables synchronized measurements of train-track response under operational conditions. Utilizing a GPS antenna and video camera, we propose the multisignal processing method to obtain train-track vibrations with train position and speed. Then, we fuse the multiple signals to extract an impact index and a resonance index and further propose an interpretable anomaly detection strategy. We test the system on an operational line at 20–60 km/h under different working conditions and verify the detection results using information from conventional technologies. The impact index peaks near joints and welds, and the resonance index yields a good correlation with the measured track geometry. The developed solution achieves the detection, localization, and quantification of surface and support anomalies in railway tracks.
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基于多信号融合的列车载激光测振仪解决方案,用于自给式铁路轨道监测
本文开发并测试了一个独立的铁路轨道监控系统,该系统适用于现有车辆,无需速度和负载控制。结合车载激光多普勒振动计和轴箱加速度计,可以在运行条件下同步测量列车轨道响应。利用GPS天线和摄像机,我们提出了一种多信号处理方法来获取随列车位置和速度变化的列车轨道振动。然后,将多个信号融合提取冲击指数和共振指数,进一步提出一种可解释的异常检测策略。我们在一条运行线路上以20-60公里/小时的速度在不同的工作条件下测试了该系统,并使用传统技术的信息验证了检测结果。冲击指数在接头和焊缝附近达到峰值,共振指数与实测轨迹几何形状具有良好的相关性。开发的解决方案实现了铁路轨道表面和支架异常的检测、定位和量化。
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来源期刊
IEEE Transactions on Industrial Informatics
IEEE Transactions on Industrial Informatics 工程技术-工程:工业
CiteScore
24.10
自引率
8.90%
发文量
1202
审稿时长
5.1 months
期刊介绍: The IEEE Transactions on Industrial Informatics is a multidisciplinary journal dedicated to publishing technical papers that connect theory with practical applications of informatics in industrial settings. It focuses on the utilization of information in intelligent, distributed, and agile industrial automation and control systems. The scope includes topics such as knowledge-based and AI-enhanced automation, intelligent computer control systems, flexible and collaborative manufacturing, industrial informatics in software-defined vehicles and robotics, computer vision, industrial cyber-physical and industrial IoT systems, real-time and networked embedded systems, security in industrial processes, industrial communications, systems interoperability, and human-machine interaction.
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