{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T16:03:50Z","timestamp":1769184230681,"version":"3.49.0"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3650780","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:39:33Z","timestamp":1767638373000},"page":"7975-8005","source":"Crossref","is-referenced-by-count":0,"title":["Contextually-Enhanced Deep Learning Prognostic Modeling for Predicting the Remaining Useful Life of Machines"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3920-576X","authenticated-orcid":false,"given":"K. Aditya","family":"Shastry","sequence":"first","affiliation":[{"name":"Nitte Meenakshi Institute of Technology (NMIT), Nitte (Deemed to be University), Bengaluru, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9626-7006","authenticated-orcid":false,"given":"K.","family":"Deepthi","sequence":"additional","affiliation":[{"name":"Nitte Meenakshi Institute of Technology (NMIT), Nitte (Deemed to be University), Bengaluru, India"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106612"},{"issue":"1","key":"ref2","first-page":"1","article-title":"Prognostics and RUL prediction of machinery: Advances, opportunities and challenges","volume":"2","author":"Gebraeel","year":"2023","journal-title":"J. Dyn., Monit. Diagnostics"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2023.109566"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/su152115283"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/3742912"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/1830694"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3390\/s23198124"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2017.05.030"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/math10162921"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8815241"},{"key":"ref11","first-page":"329","article-title":"RUL prediction using ML algorithms","volume-title":"Proc. 3rd Int. Conf. Commun., Comput. Electron. Syst.","volume":"844","author":"Madeira"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-37154-5"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/s22124549"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.17531\/ein.2021.4.17"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/tr.2016.2570568"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/icphm.2017.7998311"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2025.3577829"},{"key":"ref18","article-title":"Predictive maintenance for continuous production line based on equipment failure prediction using MLP","volume":"156","author":"Yang","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref19","article-title":"An improved LSTM network for RUL prediction with piecewise linear degradation modeling","volume":"176","author":"Xu","year":"2021","journal-title":"Measurement"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3390\/machines10111067"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.11.021"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582798"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106113"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2018.03.262"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/sym13101861"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11071125"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2022.05.010"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3390\/s21030932"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9937846"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s43684-022-00034-2"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3203406"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3390\/s20226626"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3390\/s21020418"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.10.064"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace10080715"},{"key":"ref37","first-page":"1","article-title":"ML based data driven diagnostics & prognostics framework for aircraft predictive maintenance","volume-title":"Proc. 10th Int. Symp. NDT Aerosp.","author":"Adhikari"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.3390\/app132111893"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3226780"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace9120839"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2018.11.027"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107690"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/9601389"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-40315-1"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00328-3"},{"key":"ref46","first-page":"259","volume-title":"Source Separation and ML","author":"Chien","year":"2019"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2022.114772"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/cmmno53328.2021.9467643"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2902129"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11328104.pdf?arnumber=11328104","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:03:01Z","timestamp":1769115781000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11328104\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3650780","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}