{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:48:15Z","timestamp":1758311295766,"version":"3.44.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 51875459"],"award-info":[{"award-number":["No. 51875459"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10489-025-06705-w","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T10:11:08Z","timestamp":1751969468000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep multi-scale multi-head attention network for aero-engine remaining useful life prediction"],"prefix":"10.1007","volume":"55","author":[{"given":"Lianbing","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6180-4641","authenticated-orcid":false,"given":"Hongkai","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutong","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunpeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"6705_CR1","doi-asserted-by":"publisher","first-page":"108119","DOI":"10.1016\/j.ress.2021.108119","volume":"218","author":"E Zio","year":"2022","unstructured":"Zio E, Prognostics, Health Management (PHM) (2022) Where are we and where do we (need to) go in theory and practice. Reliab Eng Syst Saf 218:108119. https:\/\/doi.org\/10.1016\/j.ress.2021.108119","journal-title":"Reliab Eng Syst Saf"},{"key":"6705_CR2","doi-asserted-by":"publisher","first-page":"111507","DOI":"10.1016\/j.ymssp.2024.111507","volume":"216","author":"Y Liu","year":"2024","unstructured":"Liu Y, Jiang H, Yao R, Zeng T (2024) Counterfactual-augmented few-shot contrastive learning for machinery intelligent fault diagnosis with limited samples. Mech Syst Signal Process 216:111507. https:\/\/doi.org\/10.1016\/j.ymssp.2024.111507","journal-title":"Mech Syst Signal Process"},{"key":"6705_CR3","doi-asserted-by":"publisher","first-page":"107257","DOI":"10.1016\/j.ress.2020.107257","volume":"205","author":"Z Shi","year":"2021","unstructured":"Shi Z, Chehade A (2021) A dual-LSTM framework combining change point detection and remaining useful life prediction. Reliab Eng Syst Saf 205:107257. https:\/\/doi.org\/10.1016\/j.ress.2020.107257","journal-title":"Reliab Eng Syst Saf"},{"key":"6705_CR4","doi-asserted-by":"publisher","first-page":"107788","DOI":"10.1016\/j.measurement.2020.107788","volume":"159","author":"M Hou","year":"2020","unstructured":"Hou M, Pi D, Li B (2020) Similarity-based deep learning approach for remaining useful life prediction. Measurement 159:107788. https:\/\/doi.org\/10.1016\/j.measurement.2020.107788","journal-title":"Measurement"},{"key":"6705_CR5","doi-asserted-by":"publisher","first-page":"10181","DOI":"10.1007\/s10489-021-03034-6","volume":"52","author":"B Xue","year":"2022","unstructured":"Xue B, Xu F, Huang X, Xu Z, Zhang X (2022) Improved similarity based prognostics method for turbine engine degradation with degradation consistency test. Appl Intell 52:10181\u201310201. https:\/\/doi.org\/10.1007\/s10489-021-03034-6","journal-title":"Appl Intell"},{"key":"6705_CR6","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1109\/TCYB.2019.2938244","volume":"51","author":"C Wang","year":"2021","unstructured":"Wang C, Lu N, Cheng Y, Jiang B (2021) A Data-Driven Aero-Engine degradation prognostic strategy. IEEE Trans Cybernetics 51:1531\u20131541. https:\/\/doi.org\/10.1109\/TCYB.2019.2938244","journal-title":"IEEE Trans Cybernetics"},{"key":"6705_CR7","doi-asserted-by":"publisher","first-page":"3622","DOI":"10.1007\/s10489-022-03670-6","volume":"53","author":"H Shi","year":"2023","unstructured":"Shi H, Huang C, Zhang X, Zhao J, Li S (2023) Wasserstein distance based multi-scale adversarial domain adaptation method for remaining useful life prediction. Appl Intell 53:3622\u20133637. https:\/\/doi.org\/10.1007\/s10489-022-03670-6","journal-title":"Appl Intell"},{"key":"6705_CR8","doi-asserted-by":"publisher","first-page":"107471","DOI":"10.1016\/j.ymssp.2020.107471","volume":"153","author":"H Liu","year":"2021","unstructured":"Liu H, Song W, Niu Y, Zio E (2021) A generalized cauchy method for remaining useful life prediction of wind turbine gearboxes. Mech Syst Signal Process 153:107471. https:\/\/doi.org\/10.1016\/j.ymssp.2020.107471","journal-title":"Mech Syst Signal Process"},{"key":"6705_CR9","doi-asserted-by":"publisher","first-page":"106899","DOI":"10.1016\/j.ymssp.2020.106899","volume":"144","author":"Z Pan","year":"2020","unstructured":"Pan Z, Meng Z, Chen Z, Gao W, Shi Y (2020) A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings. Mech Syst Signal Process 144:106899. https:\/\/doi.org\/10.1016\/j.ymssp.2020.106899","journal-title":"Mech Syst Signal Process"},{"key":"6705_CR10","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.1109\/TIM.2019.2917735","volume":"69","author":"W Mao","year":"2020","unstructured":"Mao W, He J, Zuo MJ (2020) Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning. IEEE Trans Instrum Meas 69:1594\u20131608. https:\/\/doi.org\/10.1109\/TIM.2019.2917735","journal-title":"IEEE Trans Instrum Meas"},{"key":"6705_CR11","doi-asserted-by":"publisher","first-page":"22682","DOI":"10.1007\/s10489-023-04777-0","volume":"53","author":"Y Wang","year":"2023","unstructured":"Wang Y, Wang Y (2023) A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation. Appl Intell 53:22682\u201322699. https:\/\/doi.org\/10.1007\/s10489-023-04777-0","journal-title":"Appl Intell"},{"key":"6705_CR12","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1007\/s12065-022-00805-z","volume":"17","author":"S Abdelghafar","year":"2024","unstructured":"Abdelghafar S, Khater A, Wagdy A, Darwish A, Hassanien AE (2024) Aero engines remaining useful life prediction based on enhanced adaptive guided differential evolution. Evol Intel 17:1209\u20131220. https:\/\/doi.org\/10.1007\/s12065-022-00805-z","journal-title":"Evol Intel"},{"key":"6705_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3221142","volume":"72","author":"L Zeng","year":"2023","unstructured":"Zeng L, Zheng J, Yao L, Ge Z (2023) Dynamic bayesian networks for feature learning and transfer applications in remaining useful life Estimation. IEEE Trans Instrum Meas 72:1\u201312. https:\/\/doi.org\/10.1109\/TIM.2022.3221142","journal-title":"IEEE Trans Instrum Meas"},{"key":"6705_CR14","doi-asserted-by":"publisher","first-page":"103936","DOI":"10.1016\/j.engappai.2020.103936","volume":"96","author":"T Berghout","year":"2020","unstructured":"Berghout T, Mouss L-H, Kadri O, Sa\u00efdi L, Benbouzid M (2020) Aircraft engines remaining useful life prediction with an adaptive denoising online sequential extreme learning machine. Eng Appl Artif Intell 96:103936. https:\/\/doi.org\/10.1016\/j.engappai.2020.103936","journal-title":"Eng Appl Artif Intell"},{"key":"6705_CR15","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.cam.2018.07.008","volume":"346","author":"C Ord\u00f3\u00f1ez","year":"2019","unstructured":"Ord\u00f3\u00f1ez C, S\u00e1nchez Lasheras F, Roca-Pardi\u00f1as J, de Juez FJ (2019) A hybrid ARIMA\u2013SVM model for the study of the remaining useful life of aircraft engines. J Comput Appl Math 346:184\u2013191. https:\/\/doi.org\/10.1016\/j.cam.2018.07.008","journal-title":"J Comput Appl Math"},{"key":"6705_CR16","doi-asserted-by":"publisher","first-page":"101405","DOI":"10.1016\/j.aei.2021.101405","volume":"50","author":"JC Chen","year":"2021","unstructured":"Chen JC, Chen T-L, Liu W-J, Cheng CC, Li M-G (2021) Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery. Adv Eng Inform 50:101405. https:\/\/doi.org\/10.1016\/j.aei.2021.101405","journal-title":"Adv Eng Inform"},{"key":"6705_CR17","doi-asserted-by":"publisher","first-page":"107892","DOI":"10.1016\/j.knosys.2021.107892","volume":"238","author":"K Zhao","year":"2022","unstructured":"Zhao K, Jiang H, Liu C, Wang Y, Zhu K (2022) A new data generation approach with modified Wasserstein auto-encoder for rotating machinery fault diagnosis with limited fault data. Knowl Based Syst 238:107892. https:\/\/doi.org\/10.1016\/j.knosys.2021.107892","journal-title":"Knowl Based Syst"},{"key":"6705_CR18","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.isatra.2019.07.004","volume":"97","author":"J Wu","year":"2020","unstructured":"Wu J, Hu K, Cheng Y, Zhu H, Shao X, Wang Y (2020) Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network. ISA Trans 97:241\u2013250. https:\/\/doi.org\/10.1016\/j.isatra.2019.07.004","journal-title":"ISA Trans"},{"key":"6705_CR19","doi-asserted-by":"publisher","first-page":"23498","DOI":"10.1109\/JSEN.2021.3109623","volume":"21","author":"C Zhao","year":"2021","unstructured":"Zhao C, Huang X, Li Y, Li S, Novel Cap A (2021) -LSTM model for remaining useful life prediction. IEEE Sens J 21:23498\u201323509. https:\/\/doi.org\/10.1109\/JSEN.2021.3109623","journal-title":"IEEE Sens J"},{"key":"6705_CR20","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.compind.2019.02.004","volume":"108","author":"A Al-Dulaimi","year":"2019","unstructured":"Al-Dulaimi A, Zabihi S, Asif A, Mohammadi A (2019) A multimodal and hybrid deep neural network model for remaining useful life Estimation. Comput Ind 108:186\u2013196. https:\/\/doi.org\/10.1016\/j.compind.2019.02.004","journal-title":"Comput Ind"},{"key":"6705_CR21","doi-asserted-by":"publisher","first-page":"107807","DOI":"10.1016\/j.ress.2021.107807","volume":"214","author":"J Liu","year":"2021","unstructured":"Liu J, Lei F, Pan C, Hu D, Zuo H (2021) Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion. Reliab Eng Syst Saf 214:107807. https:\/\/doi.org\/10.1016\/j.ress.2021.107807","journal-title":"Reliab Eng Syst Saf"},{"key":"6705_CR22","doi-asserted-by":"publisher","unstructured":"Chen J, Jing H, Chang Y, Liu Q (2019) Reliab Eng Syst Saf 185:372\u2013382. https:\/\/doi.org\/10.1016\/j.ress.2019.01.006. Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process","DOI":"10.1016\/j.ress.2019.01.006"},{"key":"6705_CR23","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1109\/TNNLS.2016.2582798","volume":"28","author":"C Zhang","year":"2017","unstructured":"Zhang C, Lim P, Qin AK, Tan KC (2017) Multiobjective deep belief networks ensemble for remaining useful life Estimation in prognostics. IEEE Trans Neural Networks Learn Syst 28:2306\u20132318. https:\/\/doi.org\/10.1109\/TNNLS.2016.2582798","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"6705_CR24","doi-asserted-by":"publisher","first-page":"8792","DOI":"10.1109\/TIE.2019.2891463","volume":"66","author":"C-G Huang","year":"2019","unstructured":"Huang C-G, Huang H-Z, Li Y-F, Bidirectional A (2019) Prognostics method under multiple operational conditions. IEEE Trans Industr Electron 66:8792\u20138802. https:\/\/doi.org\/10.1109\/TIE.2019.2891463","journal-title":"IEEE Trans Industr Electron"},{"key":"6705_CR25","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.neucom.2022.02.032","volume":"487","author":"T Song","year":"2022","unstructured":"Song T, Liu C, Wu R, Jin Y, Jiang D (2022) A hierarchical scheme for remaining useful life prediction with long short-term memory networks. Neurocomputing 487:22\u201333. https:\/\/doi.org\/10.1016\/j.neucom.2022.02.032","journal-title":"Neurocomputing"},{"key":"6705_CR26","doi-asserted-by":"publisher","unstructured":"Mo H, Lucca F, Malacarne J, Iacca G, Multi-Head (2020) CNN-LSTM with prediction error analysis for remaining useful life prediction. In: 2020 27th conference of open innovations association (FRUCT), pp. 164\u2013171. https:\/\/doi.org\/10.23919\/FRUCT49677.2020.9211058","DOI":"10.23919\/FRUCT49677.2020.9211058"},{"key":"6705_CR27","doi-asserted-by":"publisher","first-page":"108341","DOI":"10.1016\/j.ress.2022.108341","volume":"221","author":"I de Pater","year":"2022","unstructured":"de Pater I, Reijns A, Mitici M (2022) Alarm-based predictive maintenance scheduling for aircraft engines with imperfect remaining useful life prognostics. Reliab Eng Syst Saf 221:108341. https:\/\/doi.org\/10.1016\/j.ress.2022.108341","journal-title":"Reliab Eng Syst Saf"},{"key":"6705_CR28","doi-asserted-by":"publisher","first-page":"1997","DOI":"10.1007\/s10845-021-01750-x","volume":"32","author":"Y Mo","year":"2021","unstructured":"Mo Y, Wu Q, Li X, Huang B (2021) Remaining useful life Estimation via transformer encoder enhanced by a gated convolutional unit. J Intell Manuf 32:1997\u20132006. https:\/\/doi.org\/10.1007\/s10845-021-01750-x","journal-title":"J Intell Manuf"},{"key":"6705_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ress.2017.11.021","volume":"172","author":"X Li","year":"2018","unstructured":"Li X, Ding Q, Sun J-Q (2018) Remaining useful life Estimation in prognostics using deep Convolution neural networks. Reliab Eng Syst Saf 172:1\u201311. https:\/\/doi.org\/10.1016\/j.ress.2017.11.021","journal-title":"Reliab Eng Syst Saf"},{"key":"6705_CR30","doi-asserted-by":"publisher","first-page":"106113","DOI":"10.1016\/j.asoc.2020.106113","volume":"89","author":"H Li","year":"2020","unstructured":"Li H, Zhao W, Zhang Y, Zio E (2020) Remaining useful life prediction using multi-scale deep convolutional neural network. Appl Soft Comput 89:106113. https:\/\/doi.org\/10.1016\/j.asoc.2020.106113","journal-title":"Appl Soft Comput"},{"key":"6705_CR31","doi-asserted-by":"publisher","first-page":"12721","DOI":"10.1109\/TPAMI.2023.3306164","volume":"45","author":"S Khan","year":"2023","unstructured":"Khan S, Khan FS, Vaswani A, Parmar N, Yang M-H, Shah M (2023) Guest editorial introduction to the special section on transformer models in vision. IEEE Trans Pattern Anal Mach Intell 45:12721\u201312725. https:\/\/doi.org\/10.1109\/TPAMI.2023.3306164","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6705_CR32","doi-asserted-by":"publisher","first-page":"108098","DOI":"10.1016\/j.engappai.2024.108098","volume":"133","author":"Y Dong","year":"2024","unstructured":"Dong Y, Jiang H, Jiang W, Xie L (2024) Dynamic normalization supervised contrastive network with multiscale compound attention mechanism for gearbox imbalanced fault diagnosis. Eng Appl Artif Intell 133:108098. https:\/\/doi.org\/10.1016\/j.engappai.2024.108098","journal-title":"Eng Appl Artif Intell"},{"key":"6705_CR33","doi-asserted-by":"publisher","first-page":"101682","DOI":"10.1016\/j.aei.2022.101682","volume":"53","author":"J Zhou","year":"2022","unstructured":"Zhou J, Qin Y, Chen D, Liu F, Qian Q (2022) Remaining useful life prediction of bearings by a new reinforced memory GRU network. Adv Eng Inform 53:101682. https:\/\/doi.org\/10.1016\/j.aei.2022.101682","journal-title":"Adv Eng Inform"},{"key":"6705_CR34","unstructured":"Saxena A, Goebel K (2008) Turbofan engine degradation simulation data set. NASA AMES prognostics data repository. Moffett Field, C: NASA Ames Research Center. http:\/\/ti.arc.nasa.gov\/project\/prognostic-data-repository"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06705-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06705-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06705-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T15:57:10Z","timestamp":1758297430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06705-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,8]]},"references-count":34,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["6705"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06705-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,7,8]]},"assertion":[{"value":"8 June 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"The authors affirm that there are no known financial conflicts of interest or personal relationships that might have influenced the work presented in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"852"}}