Anomaly Detection and Localization for the Pantograph-Catenary System

Abstract: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.

Machine Learning (cs.LG)

arXiv:2610.01721 [cs.LG]

(or arXiv:2610.01721v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.01721 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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