{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:28:44Z","timestamp":1773329324523,"version":"3.50.1"},"reference-count":10,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The paper describes the MetroPT data set, an outcome of a Predictive Maintenance project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 to develop machine learning methods for online anomaly detection and failure prediction. Several analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) provide a framework that can be easily used and help the development of new machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.<\/jats:p>","DOI":"10.1038\/s41597-022-01877-3","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T06:02:44Z","timestamp":1670911364000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["The MetroPT dataset for predictive maintenance"],"prefix":"10.1038","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7980-0972","authenticated-orcid":false,"given":"Bruno","family":"Veloso","sequence":"first","affiliation":[]},{"given":"Rita P.","family":"Ribeiro","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Gama","sequence":"additional","affiliation":[]},{"given":"Pedro Mota","family":"Pereira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"1877_CR1","doi-asserted-by":"publisher","unstructured":"Esteban, A., Zafra, A. & Ventura, S. Data Mining in Predictive Maintenance Systems: A Taxonomy and Systematic Review. WIREs Data Mining and Knowledge Discovery 1\u201345, https:\/\/doi.org\/10.1002\/widm.1471 (2022).","DOI":"10.1002\/widm.1471"},{"key":"1877_CR2","doi-asserted-by":"publisher","first-page":"5739","DOI":"10.3390\/s21175739","volume":"21","author":"N Davari","year":"2021","unstructured":"Davari, N. et al. A survey on data-driven predictive maintenance for the railway industry. Sensors 21, 5739 (2021).","journal-title":"Sensors"},{"key":"1877_CR3","doi-asserted-by":"crossref","unstructured":"Gama, J., Ribeiro, R. P. & Veloso, B. Data-driven predictive maintenance. IEEE Intelligent Systems 1\u20132 (2022).","DOI":"10.1109\/MIS.2022.3167561"},{"key":"1877_CR4","unstructured":"IFM. Pressure transmitter pt5414. Accessed on 7th July 2022."},{"key":"1877_CR5","unstructured":"WIKA. Thermocouple tc12-m. Accessed on 7th July 2022."},{"key":"1877_CR6","unstructured":"IFM. Flowmeter sd6500. Accessed on 7th July 2022."},{"key":"1877_CR7","unstructured":"LEM. Ac current transducer at-b420l. Accessed on 7th July 2022."},{"key":"1877_CR8","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.6854240","author":"B Veloso","year":"2022","unstructured":"Veloso, B., Gama, J., Ribeiro, R. & Pereira, P. MetroPT: A Benchmark dataset for predictive maintenance Zenodo https:\/\/doi.org\/10.5281\/zenodo.6854240 (2022)."},{"key":"1877_CR9","doi-asserted-by":"crossref","unstructured":"Barros, M., Veloso, B., Pereira, P. M., Ribeiro, R. P. & Gama, J. Failure detection of an air production unit in operational context. In IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, 61\u201374 (Springer, 2020).","DOI":"10.1007\/978-3-030-66770-2_5"},{"key":"1877_CR10","doi-asserted-by":"crossref","unstructured":"Davari, N., Veloso, B., Ribeiro, R. P., Pereira, P. M. & Gama, J. Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 1\u201310 (IEEE, 2021).","DOI":"10.1109\/DSAA53316.2021.9564181"}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41597-022-01877-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-022-01877-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-022-01877-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T06:02:56Z","timestamp":1670911376000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41597-022-01877-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"references-count":10,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["1877"],"URL":"https:\/\/doi.org\/10.1038\/s41597-022-01877-3","relation":{"references":[{"id-type":"doi","id":"10.5281\/zenodo.6854240","asserted-by":"subject"}]},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]},"assertion":[{"value":"20 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"764"}}