发表机构
DIETI University of Naples Federico II; DIMAI University of Florence; IDSIA USI-SUPSI University of Applied Sciences and Arts of Southern Switzerland（那不勒斯费德里科二世大学; 佛罗伦萨大学; 瑞士南部应用科学与艺术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合GPS坐标对齐定位与集体异常检测的框架，用于受电弓-接触网系统健康监测，并在意大利铁路真实数据集上验证了其性能。
AI 中文摘要
监测受电弓-接触网系统（PCS）可深入了解受电弓和铁路基础设施的健康状况。近年来的工业解决方案通过卷积神经网络进行视频监控，追踪受电弓的接触线高度和拉出值（PCS高度/拉出值）。然而，这些解决方案未考虑列车线路的地理位置。因此，本文提出了一种新颖框架，用于：1）通过与参考路线的标称GPS坐标对齐，实现PCS高度/拉出值的定位；2）进行集体异常检测，以评估PCS的健康状况。我们将该方法应用于一家铁路运输公司提供的真实工业数据集进行案例研究，评估其定位和检测性能，该数据集包含意大利铁路线路上多次列车旅程的PCS高度/拉出值。
英文摘要
Monitoring the Pantograph-Catenary System (PCS) provides insight into the health conditions of the pantograph and the railway infrastructure. Recent industrial solutions trace the pantograph's contact wire height and stagger (PCS height/stagger) using video monitoring through convolutional neural networks. However, these solutions do not account for the train route's geographic location. Therefore, in this paper we propose a novel framework for 1) localization of the PCS height/stagger by alignment with the nominal GPS coordinates of the reference route, and 2) collective anomaly detection to evaluate the health conditions of the PCS. We apply and assess the localization and detection performance of the methodology to a case-study based on a real-world industrial dataset provided by a railway transportation company, which includes the PCS height/stagger of several train journeys across Italian railway routes.
CommentsAccepted and presented at the Industry Track of the IEEE International Conference on Intelligent Transportation Systems 2026 (IEEE ITSC 2026)
