{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:09:07Z","timestamp":1782839347701,"version":"3.54.5"},"reference-count":41,"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 Trans. Automat. Sci. Eng."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tase.2025.3528469","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T15:26:07Z","timestamp":1736781967000},"page":"3316-3328","source":"Crossref","is-referenced-by-count":3,"title":["From Inconsistency to Unity: Benchmarking Deep Learning-Based Unsupervised Domain Adaptation for RUL"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5512-9712","authenticated-orcid":false,"given":"Tilman","family":"Krokotsch","sequence":"first","affiliation":[{"name":"Chair of Electronic Measurement and Diagnostic Technology, Technische Universität Berlin, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2138-4395","authenticated-orcid":false,"given":"Mohamed","family":"Ragab","sequence":"additional","affiliation":[{"name":"Technology and Research (A*STAR), Institute for Infocomm Research and the Centre for Frontier AI Research, Agency for Science, Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-3600","authenticated-orcid":false,"given":"Min","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research and the Centre for Frontier AI Research, A*STAR, Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0762-6562","authenticated-orcid":false,"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research and the Centre for Frontier AI Research, A*STAR, Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1719-0328","authenticated-orcid":false,"given":"Zhenghua","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research and the Centre for Frontier AI Research, A*STAR, Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2865-0078","authenticated-orcid":false,"given":"Clemens","family":"G\u00fchmann","sequence":"additional","affiliation":[{"name":"Chair of Electronic Measurement and Diagnostic Technology, Technische Universität Berlin, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1201\/9781003097242"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2009.2038170"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/tase.2024.3366288"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2013.2250282"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2020.2979166"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106889"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2881543"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM49022.2020.9187058"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3172704"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3028503"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.083"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26221"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3444904"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29500"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2019.106682"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3032690"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3162283"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-021-01814-y"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3223387"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.107556"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM49022.2020.9187053"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref23","first-page":"1","article-title":"PRONOSTIA: An experimental platform for bearings accelerated degradation tests","volume-title":"Proc. IEEE Int. Conf. Prognostics Health Manage. (PHM)","author":"Nectoux"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2018.2882682"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3400066"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_10"},{"key":"ref27","first-page":"3470","article-title":"Deep transfer learning with joint adaptation networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","author":"Long"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/11494683_1"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.11.021"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2018.11.011"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3200667"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110391"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102413"},{"key":"ref34","first-page":"1","article-title":"TSLANet: Rethinking transformers for time series representation learning","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Eldele"},{"key":"ref35","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.5555\/1248547.1248548"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2022.106004"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref39","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.3390\/data6010005"},{"key":"ref41","first-page":"1","article-title":"Can we evaluate domain adaptation models without target-domain labels","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Yang"}],"container-title":["IEEE Transactions on Automation Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8856\/11323516\/10839091.pdf?arnumber=10839091","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:53:07Z","timestamp":1774554787000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10839091\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/tase.2025.3528469","relation":{},"ISSN":["1545-5955","1558-3783"],"issn-type":[{"value":"1545-5955","type":"print"},{"value":"1558-3783","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}