An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugal

An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugal

Abstract The public transportation sector generates large volumes of sensor data that, if analyzed adequately, can help anticipate failures and initiate maintenance actions, thereby enhancing quality and productivity. This work contributes to a real-time data-driven predictive maintenance solution f...

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Bibliographic Details
Main Authors: Silvia García-Méndez, Francisco de Arriba-Pérez, Fátima Leal, Bruno Veloso, Benedita Malheiro, Juan Carlos Burguillo-Rial
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Explainable sensor-driven computational intelligence
Intelligent transportation systems
Online supervised machine learning
Predictive maintenance
Railway sector safety and reliability
Online Access:https://doi.org/10.1038/s41598-025-08084-1
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https://doi.org/10.1038/s41598-025-08084-1

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