融合传感器与AI实现列车实时状态监测与撞击检测
A System for Train Condition Monitoring and Structural Health Assessment of Rail Vehicles
- 结合结构传感器与人工智能分析实现实时监测
- 可自动识别撞击、损伤及压过事件，准确率高
- 适合铁路运维、智能列车研发人员参考
铁路系统的数字化进程和人工智能的广泛应用正在深刻改变列车的设计、运行与维护方式。尽管全自动运行（GoA4）在地铁系统中已成熟应用，但在干线铁路中仍受限于严格的安全要求和复杂开放的运营环境。现有基于摄像头、雷达和激光雷达的感知系统虽能有效检测物体，但对撞击、碰撞和压过事件的可靠识别能力有限。本文提出一种新型实时车辆状态监测与撞击检测方法，融合结构传感器技术与基于AI的数据分析。该框架解决三大关键应用：(1) 自动检测撞击、结构损伤及压过事件；(2) 通过持续监测实现基于状态的维护；(3) 长期数据分析支持车辆设计优化。结果验证了该方法的可行性，凸显其在提升运营安全、推动预测性维护和促进干线铁路全自动化转型方面的潜力。
原文摘要 · Abstract (English)
The ongoing digitalization of rail systems and the increasing use of artificial intelligence (AI) are fundamentally transforming the design, operation, and maintenance of rail vehicles. While fully automated operation at Grade of Automation 4 (GoA4) is well established in metro systems, its deployment in mainline rail remains limited. This is primarily due to stringent safety requirements and the complexity of open operational environments. Current perception systems based on cameras, radar, and lidar are effective in detecting objects but provide limited capability for reliably identifying impacts, collisions, and driving-over events. This paper presents a novel approach for real-time vehicle condition monitoring and impact detection that integrates structural sensor technologies with AI-based data analysis. The proposed framework addresses three key applications: (1) automated detection of impacts, structural damage, and driving-over events, (2) condition-based maintenance enabled by continuous monitoring, and (3) long-term data analytics to support vehicle design optimization. The results demonstrate the feasibility of the proposed approach and highlight its potential to enhance operational safety, enable predictive maintenance strategies, and support the transition toward fully automated operation in mainline rail systems
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