{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:02:38Z","timestamp":1773248558472,"version":"3.50.1"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003593","name":"National Council for Scientific and Technological Development","doi-asserted-by":"crossref","award":["303527\/2021-8"],"award-info":[{"award-number":["303527\/2021-8"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Brazilian Federal Agency for Support and Evaluation of Graduate Education","award":["ROR: 00x0ma614"],"award-info":[{"award-number":["ROR: 00x0ma614"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3543600","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T18:57:49Z","timestamp":1739991469000},"page":"33799-33812","source":"Crossref","is-referenced-by-count":3,"title":["Methodology for Commercial Vehicle Mechanical Systems Maintenance: Data-Driven and Deep-Learning-Based Prediction"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9878-0081","authenticated-orcid":false,"given":"Romulo Gon\u00e7alves","family":"Lins","sequence":"first","affiliation":[{"name":"Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, Santo André, Brazil"}]},{"given":"Tiago","family":"Nascimento de Freitas","sequence":"additional","affiliation":[{"name":"Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, Santo André, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7336-6308","authenticated-orcid":false,"given":"Ricardo","family":"Gaspar","sequence":"additional","affiliation":[{"name":"Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, Santo André, Brazil"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Machine learning methods for vehicle predictive maintenance using off-board and on-board data","author":"Prytz","year":"2014"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103554"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1080\/25726668.2020.1720371"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105878"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104552"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2018.01.011"},{"key":"ref7","volume-title":"The rewards and challenges of predictive maintenance","author":"Lavi","year":"2018"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.4271\/2019-01-1048"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2019.06.003"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3200694"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3235151"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.36001\/ijphm.2017.v8i3.2667"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2014.11.007"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2015.02.009"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/INDUSCON58041.2023.10374966"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2019.106024"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30241-2_50"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106031"},{"key":"ref20","article-title":"A transformer-based framework for multi-variate time series: A remaining useful life prediction use case","author":"Ogunfowora","year":"2023","journal-title":"arXiv:2308.09884"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3130183"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3350777"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3269693"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIS53314.2022.9742716"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/SBESC56799.2022.9965059"},{"key":"ref26","volume-title":"Bosch Automotive Electrics and Automotive Electronics: Systems and Components, Networking and Hybrid Drive","author":"Bosch","year":"2014"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2016.09.032"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx659"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2016.2618756"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10892108.pdf?arnumber=10892108","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T19:08:07Z","timestamp":1740596887000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10892108\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3543600","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}