{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T03:31:35Z","timestamp":1788233495436,"version":"build-2803163510"},"reference-count":70,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100023758","name":"Fachhochschule S\u00fcdwestfalen","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100023758","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.eswa.2026.132600","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:52:26Z","timestamp":1777571546000},"page":"132600","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["GRADA: Gradient regularized adversarial domain adaptation for remaining useful lifetime estimation"],"prefix":"10.1016","volume":"325","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0157-1991","authenticated-orcid":false,"given":"Sayed","family":"Rafay Bin Shah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8405-0977","authenticated-orcid":false,"given":"Andreas","family":"Schwung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132600_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113721","article-title":"Continual learning by gradient monitoring for remaining useful lifetime estimation","volume":"323","author":"Shah","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.132600_bib0002","series-title":"Proceedings of the 34th international conference on machine learning","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume":"Vol. 70","author":"Arjovsky","year":"2017"},{"key":"10.1016\/j.eswa.2026.132600_bib0003","series-title":"2009\u202fIEEE international conference on acoustics, speech and signal processing","first-page":"693","article-title":"A fast iterative shrinkage-thresholding algorithm with application to wavelet-based image deblurring","author":"Beck","year":"2009"},{"key":"10.1016\/j.eswa.2026.132600_bib0004","series-title":"2024\u202fIEEE conference on artificial intelligence (CAI)","first-page":"1202","article-title":"Representation learning and knowledge distillation for lightweight domain adaptation","author":"Bin Shah","year":"2024"},{"key":"10.1016\/j.eswa.2026.132600_bib0005","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511804441","article-title":"Convex optimization","author":"Boyd","year":"2004"},{"key":"10.1016\/j.eswa.2026.132600_bib0006","doi-asserted-by":"crossref","first-page":"74244","DOI":"10.1109\/ACCESS.2022.3187702","article-title":"Shared temporal attention transformer for remaining useful lifetime estimation","volume":"10","author":"Chadha","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.132600_bib0007","doi-asserted-by":"crossref","first-page":"19621","DOI":"10.1109\/ACCESS.2022.3151975","article-title":"Transformer network for remaining useful life prediction of lithium-ion batteries","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.eswa.2026.132600_bib0008","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1109\/TIE.2020.2972443","article-title":"Machine remaining useful life prediction via an attention-based deep learning approach","volume":"68","author":"Chen","year":"2021","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"10.1016\/j.eswa.2026.132600_bib0009","unstructured":"Cortild, D., Ketels, L., Peypouquet, J., & Garrigos, G. (2025). New tight bounds for SGD without variance assumption: A computer-aided lyapunov analysis. arXiv: 2505.17965. https:\/\/doi.org\/10.48550\/arXiv.2505.17965."},{"issue":"11","key":"10.1016\/j.eswa.2026.132600_bib0010","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1002\/cpa.20042","article-title":"An iterative thresholding algorithm for linear inverse problems with a sparsity constraint","volume":"57","author":"Daubechies","year":"2004","journal-title":"Communications on Pure and Applied Mathematics"},{"key":"10.1016\/j.eswa.2026.132600_bib0011","series-title":"2019\u202fIEEE\/CVF international conference on computer vision (ICCV)","first-page":"9943","article-title":"Cluster alignment with a teacher for unsupervised domain adaptation","author":"Deng","year":"2019"},{"key":"10.1016\/j.eswa.2026.132600_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.110199","article-title":"Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions","volume":"261","author":"Ding","year":"2023","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.132600_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.108012","article-title":"Deep residual LSTM with domain-invariance for remaining useful life prediction across domains","volume":"216","author":"Fu","year":"2021","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.132600_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111783","article-title":"Mixup domain adaptations for dynamic remaining useful life predictions","volume":"295","author":"Furqon","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.132600_bib0015","series-title":"Proceedings of the 32nd international conference on machine learning","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume":"Vol. 37","author":"Ganin","year":"2015"},{"issue":"59","key":"10.1016\/j.eswa.2026.132600_bib0016","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"Journal of Machine Learning Research"},{"issue":"4","key":"10.1016\/j.eswa.2026.132600_bib0017","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1137\/120880811","article-title":"Stochastic first- and zeroth-order methods for nonconvex stochastic programming","volume":"23","author":"Ghadimi","year":"2013","journal-title":"SIAM Journal on Optimization"},{"issue":"2","key":"10.1016\/j.eswa.2026.132600_bib0018","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1137\/080725891","article-title":"The split bregman method for l1-regularized problems","volume":"2","author":"Goldstein","year":"2009","journal-title":"SIAM Journal on Imaging Sciences"},{"key":"10.1016\/j.eswa.2026.132600_bib0019","series-title":"Proceedings of the 31st international conference on neural information processing systems","first-page":"5769-5779","article-title":"Improved training of wasserstein GANs","author":"Gulrajani","year":"2017"},{"key":"10.1016\/j.eswa.2026.132600_bib0020","series-title":"2022\u202fIEEE 20th international conference on industrial informatics (INDIN)","first-page":"353","article-title":"Masked self-supervision for remaining useful lifetime prediction in machine tools","author":"Guo","year":"2022"},{"key":"10.1016\/j.eswa.2026.132600_bib0021","series-title":"Advances in neural information processing systems","article-title":"Learning both weights and connections for efficient neural network","volume":"Vol. 28","author":"Han","year":"2015"},{"key":"10.1016\/j.eswa.2026.132600_bib0022","series-title":"Proceedings of the 35th international conference on machine learning","first-page":"1989","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","volume":"Vol. 80","author":"Hoffman","year":"2018"},{"key":"10.1016\/j.eswa.2026.132600_bib0023","series-title":"Advances in Neural Information Processing Systems","first-page":"10827","article-title":"On convergence of adam for stochastic optimization under relaxed assumptions","volume":"37","author":"Hong","year":"2024"},{"key":"10.1016\/j.eswa.2026.132600_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108526","article-title":"Remaining useful life estimation of bearings under different working conditions via wasserstein distance-based weighted domain adaptation","volume":"224","author":"Hu","year":"2022","journal-title":"Reliability Engineering & System Safety"},{"issue":"11","key":"10.1016\/j.eswa.2026.132600_bib0025","doi-asserted-by":"crossref","first-page":"8792","DOI":"10.1109\/TIE.2019.2891463","article-title":"A bidirectional LSTM prognostics method under multiple operational conditions","volume":"66","author":"Huang","year":"2019","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"10.1016\/j.eswa.2026.132600_bib0026","series-title":"Advances in neural information processing systems","article-title":"Correcting sample selection bias by unlabeled data","volume":"Vol. 19","author":"Huang","year":"2006"},{"key":"10.1016\/j.eswa.2026.132600_bib0027","series-title":"Nonlinear systems","author":"Khalil","year":"2002"},{"issue":"19","key":"10.1016\/j.eswa.2026.132600_bib0028","doi-asserted-by":"crossref","first-page":"4156","DOI":"10.3390\/app9194156","article-title":"Convolution and long short-term memory hybrid deep neural networks for remaining useful life prognostics","volume":"9","author":"Kong","year":"2019","journal-title":"Applied Sciences"},{"key":"10.1016\/j.eswa.2026.132600_bib0029","series-title":"2022 8th international conference on optimization and applications (ICOA)","first-page":"1","article-title":"Mode collapse in generative adversarial networks: An overview","author":"Kossale","year":"2022"},{"key":"10.1016\/j.eswa.2026.132600_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110296","article-title":"Self-supervised domain adaptation for machinery remaining useful life prediction","volume":"250","author":"Le Xuan","year":"2024","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.132600_bib0031","series-title":"2019\u202fIEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"10277","article-title":"Sliced Wasserstein discrepancy for unsupervised domain adaptation","author":"Lee","year":"2019"},{"issue":"11","key":"10.1016\/j.eswa.2026.132600_bib0032","doi-asserted-by":"crossref","first-page":"3918","DOI":"10.1109\/TPAMI.2020.2991050","article-title":"Maximum density divergence for domain adaptation","volume":"43","author":"Li","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"12","key":"10.1016\/j.eswa.2026.132600_bib0033","doi-asserted-by":"crossref","first-page":"6103","DOI":"10.1109\/TIP.2019.2924174","article-title":"Locality preserving joint transfer for domain adaptation","volume":"28","author":"Li","year":"2019","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.eswa.2026.132600_bib0034","doi-asserted-by":"crossref","first-page":"75464","DOI":"10.1109\/ACCESS.2019.2919566","article-title":"A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.eswa.2026.132600_bib0035","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1109\/TNNLS.2018.2868854","article-title":"Heterogeneous domain adaptation through progressive alignment","volume":"30","author":"Li","year":"2019","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2026.132600_bib0036","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.ress.2018.11.027","article-title":"Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture","volume":"183","author":"Listou Ellefsen","year":"2019","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.132600_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108330","article-title":"Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture","volume":"221","author":"Liu","year":"2022","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.132600_bib0038","series-title":"Proceedings of the 32nd international conference on machine learning","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume":"Vol. 37","author":"Long","year":"2015"},{"key":"10.1016\/j.eswa.2026.132600_bib0039","series-title":"Advances in neural information processing systems","article-title":"Unsupervised domain adaptation with residual transfer networks","volume":"Vol. 29","author":"Long","year":"2016"},{"key":"10.1016\/j.eswa.2026.132600_bib0040","series-title":"Proceedings of the 35th international conference on machine learning","first-page":"3481","article-title":"Which training methods for GANs do actually converge?","volume":"Vol. 80","author":"Mescheder","year":"2018"},{"key":"10.1016\/j.eswa.2026.132600_bib0041","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Spectral normalization for generative adversarial networks","author":"Miyato","year":"2018"},{"key":"10.1016\/j.eswa.2026.132600_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109718","article-title":"Domain adaptation via alignment of operation profile for remaining useful lifetime prediction","volume":"242","author":"Nejjar","year":"2024","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.132600_bib0043","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"},{"issue":"3","key":"10.1016\/j.eswa.2026.132600_bib0044","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1561\/2400000003","article-title":"Proximal algorithms","volume":"1","author":"Parikh","year":"2014","journal-title":"Foundations and Trends in Optimization"},{"key":"10.1016\/j.eswa.2026.132600_bib0045","series-title":"Proceedings of the 30th international conference on machine learning","first-page":"1310","article-title":"On the difficulty of training recurrent neural networks","volume":"Vol. 28","author":"Pascanu","year":"2013"},{"issue":"8","key":"10.1016\/j.eswa.2026.132600_bib0046","doi-asserted-by":"crossref","first-page":"5239","DOI":"10.1109\/TII.2020.3032690","article-title":"Contrastive adversarial domain adaptation for machine remaining useful life prediction","volume":"17","author":"Ragab","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.eswa.2026.132600_bib0047","series-title":"2020\u202fIEEE international conference on prognostics and health management (ICPHM)","first-page":"1","article-title":"Adversarial transfer learning for machine remaining useful life prediction","author":"Ragab","year":"2020"},{"key":"10.1016\/j.eswa.2026.132600_bib0048","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neucom.2021.09.022","article-title":"Attention-based sequence to sequence model for machine remaining useful life prediction","volume":"466","author":"Ragab","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.132600_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.107929","article-title":"Deep learning for prognostics and health management: State of the art, challenges, and opportunities","volume":"163","author":"Rezaeianjouybari","year":"2020","journal-title":"Measurement"},{"key":"10.1016\/j.eswa.2026.132600_bib0050","series-title":"2018\u202fIEEE\/CVF conference on computer vision and pattern recognition","first-page":"8099","article-title":"From source to target and back: Symmetric bi-directional adaptive GAN","author":"Russo","year":"2018"},{"key":"10.1016\/j.eswa.2026.132600_bib0051","series-title":"2018\u202fIEEE\/CVF conference on computer vision and pattern recognition","first-page":"3723","article-title":"Maximum classifier discrepancy for unsupervised domain adaptation","author":"Saito","year":"2018"},{"key":"10.1016\/j.eswa.2026.132600_bib0052","series-title":"2008 international conference on prognostics and health management","first-page":"1","article-title":"Damage propagation modeling for aircraft engine run-to-failure simulation","author":"Saxena","year":"2008"},{"key":"10.1016\/j.eswa.2026.132600_bib0053","article-title":"A sequence-to-sequence approach for remaining useful lifetime estimation using attention-augmented bidirectional LSTM","volume":"10\u201311","author":"Shah","year":"2021","journal-title":"Intelligent Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132600_bib0054","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113721","article-title":"Continual learning by gradient monitoring for remaining useful lifetime estimation","volume":"323","author":"Shah","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.132600_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109821","article-title":"A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction","volume":"243","author":"Shi","year":"2024","journal-title":"Reliability Engineering & System Safety"},{"issue":"1","key":"10.1016\/j.eswa.2026.132600_bib0056","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2010.11.018","article-title":"Remaining useful life estimation - a review on the statistical data driven approaches","volume":"213","author":"Si","year":"2011","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"10.1016\/j.eswa.2026.132600_bib0057","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v30i1.10306","article-title":"Return of frustratingly easy domain adaptation","volume":"30","author":"Sun","year":"2016","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132600_bib0058","first-page":"153","article-title":"Correlation alignment for unsupervised domain adaptation","author":"Sun","year":"2017"},{"key":"10.1016\/j.eswa.2026.132600_bib0059","series-title":"2017\u202fIEEE conference on computer vision and pattern recognition (CVPR)","first-page":"2962","article-title":"Adversarial discriminative domain adaptation","author":"Tzeng","year":"2017"},{"key":"10.1016\/j.eswa.2026.132600_bib0060","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014). Deep domain confusion: Maximizing for domain invariance. arXiv: 1412.3474. https:\/\/doi.org\/10.48550\/arXiv.1412.3474."},{"key":"10.1016\/j.eswa.2026.132600_bib0061","series-title":"Proceedings of the 36th international conference on machine learning","first-page":"6392","article-title":"Characterization of convex objective functions and optimal expected convergence rates for SGD","volume":"Vol. 97","author":"Van Dijk","year":"2019"},{"key":"10.1016\/j.eswa.2026.132600_bib0062","doi-asserted-by":"crossref","first-page":"10253","DOI":"10.1609\/aaai.v37i8.26221","article-title":"Sensor alignment for multivariate time-series unsupervised domain adaptation","volume":"37","author":"Wang","year":"2023","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132600_bib0063","doi-asserted-by":"crossref","first-page":"162415","DOI":"10.1109\/ACCESS.2019.2950985","article-title":"A review on deep learning applications in prognostics and health management","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.132600_bib0064","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107556","article-title":"Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions","volume":"211","author":"Zhang","year":"2021","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.132600_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109350","article-title":"Multi-task learning boosted predictions of the remaining useful life of aero-engines under scenarios of working-condition shift","volume":"237","author":"Zhang","year":"2023","journal-title":"Reliability Engineering & System Safety"},{"issue":"9","key":"10.1016\/j.eswa.2026.132600_bib0066","doi-asserted-by":"crossref","DOI":"10.3390\/app9091890","article-title":"A hybrid method for remaining useful life estimation of lithium-ion battery with regeneration phenomena","volume":"9","author":"Zhao","year":"2019","journal-title":"Applied Sciences"},{"key":"10.1016\/j.eswa.2026.132600_bib0067","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107946","article-title":"Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors","volume":"216","author":"Zhuang","year":"2021","journal-title":"Reliability Engineering & System Safety"},{"issue":"6","key":"10.1016\/j.eswa.2026.132600_bib0068","doi-asserted-by":"crossref","first-page":"4675","DOI":"10.1109\/TMECH.2022.3163289","article-title":"Health assessment of rotating equipment with unseen conditions using adversarial domain generalization toward self-supervised regularization learning","volume":"27","author":"Zhuang","year":"2022","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"key":"10.1016\/j.eswa.2026.132600_bib0069","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108599","article-title":"An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions","volume":"225","author":"Zhuang","year":"2022","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.132600_bib0070","first-page":"6298","article-title":"Comparing deep learning methods to predict the remaining useful life of lithium-ion batteries","volume":"62","author":"Zraibi","year":"2022","journal-title":"Materials Today: Proceedings"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426015137?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426015137?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T15:07:19Z","timestamp":1786806439000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426015137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":70,"alternative-id":["S0957417426015137"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132600","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"GRADA: Gradient regularized adversarial domain adaptation for remaining useful lifetime estimation","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132600","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"132600"}}