{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T21:59:18Z","timestamp":1780955958714,"version":"3.54.1"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100032734","name":"Centre for Advances in Reliability and Safety","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100032734","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Reliability Engineering & System Safety"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.ress.2026.112786","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T23:06:24Z","timestamp":1776899184000},"page":"112786","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["Zero-shot cross-domain transfer for remaining useful life prediction of aero-engines via iterative diffusion refinement"],"prefix":"10.1016","volume":"275","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1788-460X","authenticated-orcid":false,"given":"Ka-Chun","family":"Fung","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsun-Hin","family":"Cheung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9356-0717","authenticated-orcid":false,"given":"Songjiang","family":"Lai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8221-5328","authenticated-orcid":false,"given":"Kaiwen","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kin-Man","family":"Lam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ress.2026.112786_bib0001","first-page":"1551","article-title":"Turbofan engine degradation simulation data set","author":"Saxena","year":"2008","journal-title":"NASA Ames Progn Data Repos"},{"issue":"1","key":"10.1016\/j.ress.2026.112786_bib0002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2010.11.018","article-title":"Remaining useful life estimation\u2013a review on the statistical data driven approaches","volume":"213","author":"Si","year":"2011","journal-title":"Eur J Oper Res"},{"issue":"3","key":"10.1016\/j.ress.2026.112786_bib0003","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1109\/TR.2019.2957965","article-title":"A review on prognostics methods for engineering systems","volume":"69","author":"Guo","year":"2019","journal-title":"IEEE Trans Reliab"},{"key":"10.1016\/j.ress.2026.112786_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108651","article-title":"A long short-term memory neural network based Wiener process model for remaining useful life prediction","volume":"226","author":"Chen","year":"2022","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0005","series-title":"2017 IEEE international conference on prognostics and health management (ICPHM)","first-page":"88","article-title":"Long short-term memory network for remaining useful life estimation","author":"Zheng","year":"2017"},{"key":"10.1016\/j.ress.2026.112786_bib0006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ress.2017.11.021","article-title":"Remaining useful life estimation in prognostics using deep convolution neural networks","volume":"172","author":"Li","year":"2018","journal-title":"Reliab Eng Syst Saf"},{"issue":"1","key":"10.1016\/j.ress.2026.112786_bib0007","article-title":"Transformer model for remaining useful life prediction of aeroengine","volume":"2171","author":"Li","year":"2022","journal-title":"J Phys: Conf Ser"},{"key":"10.1016\/j.ress.2026.112786_bib0008","first-page":"1","article-title":"Domain adaptive remaining useful life prediction with transformer","volume":"71","author":"Li","year":"2022","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2026.112786_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2023.103888","article-title":"Trans-lighter: a light-weight federated learning-based architecture for remaining useful lifetime prediction","volume":"148","author":"Du","year":"2023","journal-title":"Comput Ind"},{"key":"10.1016\/j.ress.2026.112786_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.111071","article-title":"A stacked ensemble method based on TCN and convolutional bi-directional GRU with multiple time windows for remaining useful life estimation","volume":"150","author":"Guo","year":"2024","journal-title":"Appl Soft Comput"},{"key":"10.1016\/j.ress.2026.112786_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121859","article-title":"Bayesian gated-transformer model for risk-aware prediction of aero-engine remaining useful life","volume":"238","author":"Xiang","year":"2024","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.ress.2026.112786_bib0012","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.ress.2026.112786_bib0013","series-title":"International conference on database systems for advanced applications","first-page":"214","article-title":"Deep convolutional neural network based regression approach for estimation of remaining useful life","author":"Sateesh Babu","year":"2016"},{"key":"10.1016\/j.ress.2026.112786_bib0014","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.ymssp.2019.05.005","article-title":"Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme","volume":"129","author":"Yu","year":"2019","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112786_bib0015","series-title":"2022 6th international conference on system reliability and safety (ICSRS)","first-page":"494","article-title":"Remaining useful life prediction of aero-engine based on deep convolutional LSTM network","author":"Wang","year":"2022"},{"key":"10.1016\/j.ress.2026.112786_bib0016","series-title":"Joint european conference on machine learning and knowledge discovery in databases","first-page":"461","article-title":"Automatic remaining useful life estimation framework with embedded convolutional LSTM as the backbone","author":"Zhou","year":"2020"},{"key":"10.1016\/j.ress.2026.112786_bib0017","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":"Ellefsen","year":"2019","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0018","article-title":"A physics-constrained bayesian neural network for machinery remaining useful life prediction and uncertainty quantification","author":"Wang","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110668","article-title":"Image-based remaining useful life prediction through adaptation from simulation to experimental domain","volume":"255","author":"Wang","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.110902","article-title":"Remaining useful life prediction based on graph feature attention networks with missing multi-sensor features","volume":"258","author":"Wang","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111121","article-title":"A soft actor-critic reinforcement learning-based method for remaining useful life prediction","volume":"261","author":"Ding","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111420","article-title":"Spatial-temporal multi-sensor information fusion network with prior knowledge embedding for equipment remaining useful life prediction","author":"Qin","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0023","first-page":"1","article-title":"Convolutional transformer: an enhanced attention mechanism architecture for remaining useful life estimation of bearings","volume":"71","author":"Ding","year":"2022","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2026.112786_bib0024","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":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0025","article-title":"Evidential domain adaptation for remaining useful life prediction with incomplete degradation","author":"Hou","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"issue":"24","key":"10.1016\/j.ress.2026.112786_bib0026","doi-asserted-by":"crossref","first-page":"4647","DOI":"10.3390\/math10244647","article-title":"Remaining useful life prediction based on multi-representation domain adaptation","volume":"10","author":"Lyu","year":"2022","journal-title":"Mathematics"},{"key":"10.1016\/j.ress.2026.112786_bib0027","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":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0028","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"2206","article-title":"Your diffusion model is secretly a zero-shot classifier","author":"Li","year":"2023"},{"key":"10.1016\/j.ress.2026.112786_bib0029","unstructured":"M. Raman, R. Shah, A. Kannan, and P. Chawla, \"Turn down the noise: leveraging diffusion models for test-time adaptation via pseudo-label ensembling,\" arXiv preprint arXiv:2311.18071, 2023."},{"key":"10.1016\/j.ress.2026.112786_bib0030","series-title":"European conference on computer vision","first-page":"151","article-title":"ZoDi: zero-shot domain adaptation with diffusion-based image transfer","author":"Azuma","year":"2024"},{"key":"10.1016\/j.ress.2026.112786_bib0031","series-title":"International conference on machine learning","first-page":"8857","article-title":"Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting","author":"Rasul","year":"2021"},{"key":"10.1016\/j.ress.2026.112786_bib0032","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"4195","article-title":"Scalable diffusion models with transformers","author":"Peebles","year":"2023"},{"key":"10.1016\/j.ress.2026.112786_bib0033","unstructured":"J. Song, C. Meng, and S. Ermon, \"Denoising diffusion implicit models,\" arXiv preprint arXiv:2010.02502, 2020."},{"key":"10.1016\/j.ress.2026.112786_bib0034","article-title":"Predict, refine, synthesize: self-guiding diffusion models for probabilistic time series forecasting","volume":"36","author":"Kollovieh","year":"2024","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.ress.2026.112786_bib0035","unstructured":"X. Yuan and Y. Qiao, \"Diffusion-ts: interpretable diffusion for general time series generation,\" arXiv preprint arXiv:2403.01742, 2024."},{"key":"10.1016\/j.ress.2026.112786_bib0036","series-title":"The twelfth international conference on learning representations","article-title":"Multi-resolution diffusion models for time series forecasting","author":"Shen","year":"2024"},{"key":"10.1016\/j.ress.2026.112786_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110394","article-title":"Data augmentation based on diffusion probabilistic model for remaining useful life estimation of aero-engines","volume":"252","author":"Wang","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"issue":"1","key":"10.1016\/j.ress.2026.112786_bib0038","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1038\/s41598-024-52151-y","article-title":"A novel gear RUL prediction method by diffusion model generation health index and attention guided multi-hierarchy LSTM","volume":"14","author":"Chen","year":"2024","journal-title":"Sci Rep"},{"key":"10.1016\/j.ress.2026.112786_bib0039","unstructured":"H. Eivazi et al., \"DiffBatt: a diffusion model for battery degradation prediction and synthesis\", arXiv preprint arXiv:2410.23893, 2024."},{"issue":"1","key":"10.1016\/j.ress.2026.112786_bib0040","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3390\/data6010005","article-title":"Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics","volume":"6","author":"Arias Chao","year":"2021","journal-title":"Data"},{"key":"10.1016\/j.ress.2026.112786_bib0041","unstructured":"X. Chen et al., \"Symbolic discovery of optimization algorithms. ArXiv 2023\u2033, arXiv preprint arXiv:2302.06675, 2023."},{"key":"10.1016\/j.ress.2026.112786_bib0042","series-title":"Artificial intelligence and machine learning for multi-domain operations applications","first-page":"369","article-title":"Super-convergence: very fast training of neural networks using large learning rates","volume":"11006","author":"Smith","year":"2019"},{"issue":"2","key":"10.1016\/j.ress.2026.112786_bib0043","article-title":"Dynamical variational autoencoders for estimating the remaining useful life of machinery","volume":"16","author":"Star","year":"2025","journal-title":"Int J Progn Health Manag"},{"key":"10.1016\/j.ress.2026.112786_bib0044","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111726","article-title":"Joint learning strategy of multi-scale multi-task convolutional neural network for aero-engine prognosis","volume":"160","author":"Zhou","year":"2024","journal-title":"Appl Soft Comput"},{"key":"10.1016\/j.ress.2026.112786_bib0045","series-title":"Proceedings of the AAAI conference on artificial intelligence","article-title":"Return of frustratingly easy domain adaptation","volume":"30","author":"Sun","year":"2016"},{"key":"10.1016\/j.ress.2026.112786_bib0046","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2019.106682","article-title":"Remaining useful lifetime prediction via deep domain adaptation","volume":"195","author":"da Costa","year":"2020","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0047","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":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112786_bib0048","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.ress.2026.112786_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112718","article-title":"Source-free calibrated uncertainty for RUL adaptation with incomplete degradation","volume":"233","author":"Hou","year":"2025","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112786_bib0050","unstructured":"A. Sabour, S. Fidler, and K. Kreis, \"Align your steps: optimizing sampling schedules in diffusion models,\" arXiv preprint arXiv:2404.14507, 2024."}],"container-title":["Reliability Engineering & System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026005971?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026005971?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T21:18:05Z","timestamp":1780953485000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832026005971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":50,"alternative-id":["S0951832026005971"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2026.112786","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Zero-shot cross-domain transfer for remaining useful life prediction of aero-engines via iterative diffusion refinement","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2026.112786","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112786"}}