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Data-driven predictive maintenance framework for railway systems
dc.contributor.author | Meira, Jorge | |
dc.contributor.author | Veloso, Bruno | |
dc.contributor.author | Bolón-Canedo, Verónica | |
dc.contributor.author | Marreiros, Goreti | |
dc.contributor.author | Alonso-Betanzos, Amparo | |
dc.contributor.author | Gama, João | |
dc.date.accessioned | 2024-04-01T18:55:21Z | |
dc.date.available | 2024-04-01T18:55:21Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Meira, J., Veloso, B., Bolón-Canedo, V., Marreiros, G., Alonso-Betanzos, A., & Gama, J. (2023). Data-driven predictive maintenance framework for railway systems. Intelligent Data Analysis, 27(4), 1087–1102. https://doi.org/10.3233/ida-226811 | es_ES |
dc.identifier.issn | 1088-467X | |
dc.identifier.issn | 1571-4128 | |
dc.identifier.uri | http://hdl.handle.net/2183/36034 | |
dc.description | This version of the article has been accepted for publication, after peer review. The Version of Record is available online at: https://doi.org/10.3233/ida-226811. | es_ES |
dc.description.abstract | [Abstract]: The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of sensorial and business process data. The growing and proliferation of big data and machine learning technologies enable strategic decisions based on the analyzed data. This study suggests a data-driven predictive maintenance framework for the air production unit (APU) system of a train of Metro do Porto. The proposed method assists in detecting failures and errors in machinery before they reach critical stages. We present an anomaly detection model following an unsupervised approach, combining the Half-Space-trees method with One Class K Nearest Neighbor, adapted to deal with data streams. We evaluate and compare our approach with the Half-Space-Trees method applied without the One Class K Nearest Neighbor combination. Our model produced few type-I errors, significantly increasing the value of precision when compared to the Half-Space-Trees model. Our proposal achieved high anomaly detection performance, predicting most of the catastrophic failures of the APU train system. | es_ES |
dc.description.sponsorship | This research has been financially supported in part by the Spanish Ministerio de Economia y Competitividad (research project PID2019-109238GB-C22), and by the Xunta de Galicia (Grants ED431C 2018/34 and ED431G 2019/01) with the European Union ERDF funds. CITIC, as Research Center accredited by Galician University System, is funded by Consellería de Cultura, Educación e Universidades from Xunta de Galicia, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by Secretaría Xeral de Universidades (Grant ED431G 2019/01). This work was also supported by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the Project UIDB/00760/2020 and UIDP/00760/2020. This work was supported by the CHIST-ERA grant CHIST-ERA-19-XAI-012, and project CHIST-ERA/0004/2019 funded by FCT. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2018/34 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Portugal. Fundação para a Ciência e a Tecnologia; UIDB/00760/2020 | es_ES |
dc.description.sponsorship | Portugal. Fundação para a Ciência e a Tecnologia; UIDP/00760/2020 | es_ES |
dc.description.sponsorship | Portugal. Fundação para a Ciência e a Tecnologia; CHIST-ERA/0004/2019 | es_ES |
dc.description.sponsorship | Sweden. Swedish Research Council; CHIST-ERA-19-XAI-012 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IOS Press | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLE | es_ES |
dc.relation.uri | https://doi.org/10.3233/ida-226811 | es_ES |
dc.rights | Todos os dereitos reservados. All rights reserved. | es_ES |
dc.subject | Anomaly detection | es_ES |
dc.subject | Data streams | es_ES |
dc.subject | Unsupervised learning | es_ES |
dc.subject | One class classification | es_ES |
dc.subject | Predictive maintenance | es_ES |
dc.title | Data-driven predictive maintenance framework for railway systems | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Intelligent Data Analysis | es_ES |
UDC.volume | 27 | es_ES |
UDC.issue | 4 | es_ES |
UDC.startPage | 1087 | es_ES |
UDC.endPage | 1102 | es_ES |
dc.identifier.doi | 10.3233/ida-226811 |
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