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

2025Journal article Open access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorGarcía-Méndez, Silvia
dc.contributor.authorArriba-Pérez, Francisco de
dc.contributor.authorLeal, Fátima
dc.contributor.authorVeloso, Bruno
dc.contributor.authorMalheiro, Benedita
dc.contributor.authorBurguillo-Rial, Juan Carlos
dc.contributor.authorBENEDITA CAMPOS NEVES MALHEIRO, MARIA
dc.date.accessioned2026-01-07T11:48:39Z
dc.date.available2026-01-07T11:48:39Z
dc.date.issued2025
dc.description.abstractThe 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 for Intelligent Transportation Systems. The proposed method implements a processing pipeline comprised of sample pre-processing, incremental classification with Machine Learning models, and outcome explanation. This novel online processing pipeline has two main highlights: (i) a dedicated sample pre-processing module, which builds statistical and frequency-related features on the fly, and (ii) an explainability module. This work is the first to perform online fault prediction with natural language and visual explainability. The experiments were performed with the MetroPT data set from the metro operator of Porto, Portugal. The results are above 98 % for F-measure and 99 % for accuracy. In the context of railway predictive maintenance, achieving these high values is crucial due to the practical and operational implications of accurate failure prediction. In the specific case of a high F-measure, this ensures that the system maintains an optimal balance between detecting the highest possible number of real faults and minimizing false alarms, which is crucial for maximizing service availability. Furthermore, the accuracy obtained enables reliability, directly impacting cost reduction and increased safety. The analysis demonstrates that the pipeline maintains high performance even in the presence of class imbalance and noise, and its explanations effectively reflect the decision-making process. These findings validate the methodological soundness of the approach and confirm its practical applicability for supporting proactive maintenance decisions in real-world railway operations. Therefore, by identifying the early signs of failure, this pipeline enables decision-makers to understand the underlying problems and act accordingly swiftly.eng
dc.description.sponsorshipXunta de Galicia grants ED481B-2022-093 ED481D 2024/014, Spain;
dc.identifier.citationGarcía-Méndez, S., de Arriba-Pérez, F., Leal, F. et al. An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugal. Sci Rep 15, 27495 (2025). https://doi.org/10.1038/s41598-025-08084-1
dc.identifier.doi10.1038/s41598-025-08084-1
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10400.22/31405
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Science and Business Media LLC
dc.relationINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
dc.relation.hasversionhttps://www.nature.com/articles/s41598-025-08084-1
dc.relation.ispartofScientific Reports
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectExplainable sensor-driven computational intelligence
dc.subjectIntelligent transportation systems
dc.subjectOnline supervised machine learning
dc.subjectPredictive maintenance
dc.subjectRailway sector safety and reliability
dc.titleAn explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugaleng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50014%2F2020/PT
oaire.citation.endPage15
oaire.citation.issue27495
oaire.citation.startPage1
oaire.citation.titleScientific Reports
oaire.citation.volume15
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBENEDITA CAMPOS NEVES MALHEIRO
person.givenNameMARIA
person.identifier.ciencia-id7A15-08FC-4430
person.identifier.orcid0000-0001-9083-4292
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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relation.isProjectOfPublication.latestForDiscovery5efdbedb-4666-4d5b-94f0-0a938b0d5ce4

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