{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:26:01Z","timestamp":1770917161187,"version":"3.50.1"},"reference-count":33,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"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":[[2020,6]]},"DOI":"10.1109\/icphm49022.2020.9187058","type":"proceedings-article","created":{"date-parts":[[2020,9,7]],"date-time":"2020-09-07T22:32:50Z","timestamp":1599517970000},"page":"1-8","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Evaluation Framework for Unsupervised Domain Adaption on Remaining Useful Lifetime Estimation"],"prefix":"10.1109","author":[{"given":"Tilman","family":"Krokotsch","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirko","family":"Knaak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Clemens","family":"Guhmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","first-page":"1014","article-title":"Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data","volume":"83","author":"lindstrom","year":"1988","journal-title":"Journal of the American Statistical Association"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711422"},{"key":"ref31","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014"},{"key":"ref30","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics"},{"key":"ref10","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume":"2","author":"ganin","year":"2015","journal-title":"International Conference on Machine Learning"},{"key":"ref11","article-title":"User’s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPPS)","author":"fredrick","year":"2007","journal-title":"NASA Technical Memorandum"},{"key":"ref12","first-page":"3470","article-title":"Deep transfer learning with joint adaptation networks","volume":"5","author":"long","year":"2017","journal-title":"International Conference on Machine Learning"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2004.824875"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2017.7998311"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.05.063"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1007\/978-3-319-32025-0_14","article-title":"Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life","volume":"9642","author":"sateesh babu","year":"2016","journal-title":"Database Systems for Advanced Applications"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.11.021"},{"key":"ref18","article-title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","author":"bai","year":"2018"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref28","article-title":"Variational Recurrent Adversarial Deep Domain Adaptation","author":"purushotham","year":"2017","journal-title":"International Conference on Learning Representations"},{"key":"ref4","first-page":"4171","article-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","volume":"1","author":"devlin","year":"2019","journal-title":"Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"ref3","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"International Conference on Learning Representations"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2018.12.005"},{"key":"ref29","article-title":"Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis","author":"cheng","year":"2019"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2016.7542845"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3390\/app8122416"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/AERO.2000.877920"},{"key":"ref9","article-title":"Remaining useful lifetime prediction via deep domain adaptation","volume":"195","author":"de oliveira da costa","year":"2020","journal-title":"Reliability Engineering & System Safety"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-009-2482-0"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0718-4"},{"key":"ref22","article-title":"Unsupervised Domain Adaptation with Residual Transfer Networks","author":"long","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref21","first-page":"97","article-title":"Learning Transferable Features with Deep Adaptation Networks","volume":"37","author":"long","year":"2015","journal-title":"International Conference on Machine Learning"},{"key":"ref24","first-page":"1640","article-title":"Conditional Adversarial Domain Adaptation","author":"long","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref23","article-title":"Domain-Adversarial Training of Neural Networks","volume":"17","author":"ganin","year":"2016","journal-title":"Journal of Machine Learning Research"},{"key":"ref26","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"tzeng","year":"2014"},{"key":"ref25","first-page":"3320","article-title":"How transferable are features in deep neural networksƒ","author":"yosinski","year":"2014","journal-title":"Advances in neural information processing systems"}],"event":{"name":"2020 IEEE International Conference on Prognostics and Health Management (ICPHM)","location":"Detroit, MI, USA","start":{"date-parts":[[2020,6,8]]},"end":{"date-parts":[[2020,6,10]]}},"container-title":["2020 IEEE International Conference on Prognostics and Health Management (ICPHM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9184066\/9187021\/09187058.pdf?arnumber=9187058","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:27:48Z","timestamp":1656376068000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9187058\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/icphm49022.2020.9187058","relation":{},"subject":[],"published":{"date-parts":[[2020,6]]}}}