{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T15:53:01Z","timestamp":1785858781537,"version":"3.56.0"},"reference-count":90,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"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":["IEEE Trans. Automat. Sci. Eng."],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1109\/tase.2023.3267860","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T18:48:38Z","timestamp":1682362118000},"page":"2742-2762","source":"Crossref","is-referenced-by-count":24,"title":["GAN-Based Multi-Task Learning Approach for Prognostics and Health Management of IIoT"],"prefix":"10.1109","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2882-9115","authenticated-orcid":false,"given":"Sourajit","family":"Behera","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, Bihar, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4910-5749","authenticated-orcid":false,"given":"Rajiv","family":"Misra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, Bihar, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Sillitti","sequence":"additional","affiliation":[{"name":"Centre for Applied Software Engineering, Genova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2919153"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2740434"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2697718"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.08.028"},{"key":"ref5","article-title":"Dynamic adaptation in industrial IoT systems","author":"Lam","year":"2021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2018.04.015"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s40684-016-0015-5"},{"key":"ref8","article-title":"Deep learning for automobile predictive maintenance under industry 4.0","author":"Chen","year":"2020"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2016.01.129"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.2991\/iwama-16.2016.7"},{"issue":"1","key":"ref11","first-page":"9","article-title":"Reinforcement learning based predictive maintenance for a machine with multiple deteriorating yield levels","volume":"10","author":"Wang","year":"2014","journal-title":"J. Comput. Inf. Syst."},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2018.06.021"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CoDIT.2018.8394832"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2921912"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.11.021"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114301"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref19","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Odena"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref21","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford","year":"2015","journal-title":"arXiv:1511.06434"},{"key":"ref22","volume-title":"Issues of Fault Diagnosis for Dynamic Systems","author":"Patton","year":"2013"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007327622663"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref27","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Lundberg"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3005965"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3297280.3297363"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2019.07.004"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2900295"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-020-01630-w"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3017900"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/11538059_91"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2008.2002909"},{"key":"ref37","article-title":"Conditional image generation with PixelCNN decoders","author":"van den Oord","year":"2016","journal-title":"arXiv:1606.05328"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2915536"},{"key":"ref39","article-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014","journal-title":"arXiv:1411.1784"},{"key":"ref40","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114582"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/IRI.2018.00018"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3009343"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2019.01.001"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.applthermaleng.2019.113933"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2021.02.042"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00414-0"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3046566"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2020.2972468"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3390\/su12114776"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108029"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.03.004"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1117\/12.2617310"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2021.107519"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2021.12.006"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2022.02.223"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106689"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3154786"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3149097"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.108139"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2022.3170630"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3178431"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107195"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2983409"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1002\/er.7548"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378139"},{"key":"ref69","article-title":"C-RNN-GAN: Continuous recurrent neural networks with adversarial training","author":"Mogren","year":"2016","journal-title":"arXiv:1611.09904"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2019.8916999"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240383"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2022.104520"},{"key":"ref73","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref74","first-page":"1","article-title":"The marginal value of adaptive gradient methods in machine learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Wilson"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-32373-0_3"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8014979"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.12.032"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref79","volume-title":"Trajectory Similarity Based Prediction for Remaining Useful Life Estimation","author":"Wang","year":"2010"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.36001\/phmconf.2014.v6i1.2513"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711421"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-32025-0_14"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2891463"},{"key":"ref84","article-title":"Using explainable artificial intelligence to interpret remaininguseful life estimation with gated recurrent unit","author":"Baptista","year":"2020"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/ICKII50300.2020.9318912"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2020.2972443"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1002\/qre.2651"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2017.7998311"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2018.8448804"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.107961"}],"container-title":["IEEE Transactions on Automation Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8856\/10631719\/10107629.pdf?arnumber=10107629","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T17:25:59Z","timestamp":1729531559000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10107629\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":90,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tase.2023.3267860","relation":{},"ISSN":["1545-5955","1558-3783"],"issn-type":[{"value":"1545-5955","type":"print"},{"value":"1558-3783","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7]]}}}