{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:37:38Z","timestamp":1773329858752,"version":"3.50.1"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,6]]},"DOI":"10.1109\/dsaa53316.2021.9564181","type":"proceedings-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T22:54:28Z","timestamp":1634770468000},"page":"1-10","source":"Crossref","is-referenced-by-count":47,"title":["Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry"],"prefix":"10.1109","author":[{"given":"Narjes","family":"Davari","sequence":"first","affiliation":[]},{"given":"Bruno","family":"Veloso","sequence":"additional","affiliation":[]},{"given":"Rita P.","family":"Ribeiro","sequence":"additional","affiliation":[]},{"given":"Pedro Mota","family":"Pereira","sequence":"additional","affiliation":[]},{"given":"Joao","family":"Gama","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Deep learning for anomaly detection: A survey","author":"chalapathy","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.07.321"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.07.020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-016-5584-6"},{"key":"ref14","article-title":"Anomaly detection and severity prediction of air leakage in train braking pipes","volume":"21","author":"lee","year":"2017","journal-title":"International Journal of Prognostics and Health Management"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30241-2_50"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66770-2_5"},{"key":"ref17","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"vincent","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"ref18","first-page":"96","article-title":"Structured denoising autoencoder for fault detection and analysis","author":"tagawa","year":"0","journal-title":"Asian Conference on Machine Learning"},{"key":"ref19","first-page":"1","article-title":"Sparse autoencoder","volume":"72","author":"ng","year":"2011","journal-title":"Cs294a lecture notes"},{"key":"ref28","first-page":"1","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","volume":"2","author":"an","year":"2015","journal-title":"Special Lecture on IE"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2019.2905565"},{"key":"ref27","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2765544"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.05.050"},{"key":"ref5","first-page":"259","article-title":"Intelligent predictive maintenance (ipdm) system-industry 4.0 scenario","volume":"113","author":"wang","year":"2016","journal-title":"WIT Transactions on Engineering Sciences"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2752709"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3390\/s19122750"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1080\/15732479.2015.1032983"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.jors.2602085"},{"key":"ref20","article-title":"On optimization methods for deep learning","author":"le","year":"0","journal-title":"ICML"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2017.2754287"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2012.05.003"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2016.04.007"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126419"},{"key":"ref26","volume":"1","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.10.025"}],"event":{"name":"2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)","location":"Porto, Portugal","start":{"date-parts":[[2021,10,6]]},"end":{"date-parts":[[2021,10,9]]}},"container-title":["2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9564091\/9564109\/09564181.pdf?arnumber=9564181","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:47:23Z","timestamp":1652197643000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9564181\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,6]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/dsaa53316.2021.9564181","relation":{},"subject":[],"published":{"date-parts":[[2021,10,6]]}}}