{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T19:26:34Z","timestamp":1776021994808,"version":"3.50.1"},"reference-count":142,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,26]],"date-time":"2021-08-26T00:00:00Z","timestamp":1629936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["DSAIPA\/DS\/0086\/2018"],"award-info":[{"award-number":["DSAIPA\/DS\/0086\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events\u2014anomaly detection in time-series\u2014can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.<\/jats:p>","DOI":"10.3390\/s21175739","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"5739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":120,"title":["A Survey on Data-Driven Predictive Maintenance for the Railway Industry"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3636-6146","authenticated-orcid":false,"given":"Narjes","family":"Davari","sequence":"first","affiliation":[{"name":"Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7980-0972","authenticated-orcid":false,"given":"Bruno","family":"Veloso","sequence":"additional","affiliation":[{"name":"Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"Faculty of Science and Technology, University Portucalense, 4200-072 Porto, Portugal"},{"name":"School of Economics, University of Porto, 4099-002 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9921-5065","authenticated-orcid":false,"given":"Gustavo de Assis","family":"Costa","sequence":"additional","affiliation":[{"name":"Federal Institute of Goi\u00e1s, Campus Jata\u00ed, Unity Flamboyant, Jata\u00ed 75801-326, Brazil"}]},{"given":"Pedro Mota","family":"Pereira","sequence":"additional","affiliation":[{"name":"Metro of Porto, 4350-158 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6852-8077","authenticated-orcid":false,"given":"Rita P.","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3357-1195","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Gama","sequence":"additional","affiliation":[{"name":"Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"School of Economics, University of Porto, 4099-002 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1109\/JSYST.2019.2905565","article-title":"Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey","volume":"13","author":"Zhang","year":"2019","journal-title":"IEEE Syst. 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