轨道车辆的列车状态监测与结构健康评估系统
A System for Train Condition Monitoring and Structural Health Assessment of Rail Vehicles
- DB InfraGO AG（DB InfraGO股份公司）
- DB Systemtechnik（DB系统技术公司）
- MSG Ammendorf（MSG阿门多夫公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种融合结构传感器技术与AI数据分析的系统，用于轨道车辆状态监测与撞击检测，可自动检测相关事件、支持状态维护及设计优化，助力干线铁路向全自动运行过渡。
AI中文摘要：
轨道系统的持续数字化以及人工智能（AI）的日益应用，正在从根本上改变轨道车辆的设计、运营与维护方式。尽管自动化等级4（GoA4）的全自动运行已在地铁系统中得到成熟应用，但其在干线铁路中的部署仍有限，这主要归因于严格的安全要求以及开放运营环境的复杂性。当前基于摄像头、雷达和激光雷达（lidar）的感知系统能有效检测物体，但可靠识别撞击、碰撞及碾过事件的能力有限。本文提出一种新型实时车辆状态监测与撞击检测方法，将结构传感器技术与基于AI的数据分析相融合。该框架涵盖三项关键应用：（1）自动检测撞击、结构损伤及碾过事件；（2）通过持续监测实现状态维护；（3）长期数据分析以支持车辆设计优化。结果表明，所提方法具备可行性，且其在提升运营安全性、实现预测性维护策略及助力干线铁路系统向全自动运行过渡方面具有潜力。
英文摘要：
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
