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arXiv Machine Learning By Francesco Vitale, Hangli Ge, Francesco Flammini

Anomaly Detection and Localization for the Pantograph-Catenary System

Read the original on arXiv Machine Learning →

The paper introduces a new framework for monitoring the Pantograph‑Catenary System (PCS) that localizes PCS height and stagger by aligning video‑derived measurements with nominal GPS coordinates of the reference route. It also presents a collective anomaly detection method to assess PCS health conditions. The authors evaluate both localization and detection on a real‑world dataset from an Italian railway company, covering multiple train journeys.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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