{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:11:03Z","timestamp":1772932263231,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:00:00Z","timestamp":1772841600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:00:00Z","timestamp":1772841600000},"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":"crossref","award":["No.62203193"],"award-info":[{"award-number":["No.62203193"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Jiangsu Province Higher Education Institutions Basic Disciplines","award":["21KJB510016"],"award-info":[{"award-number":["21KJB510016"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s13042-025-02882-9","type":"journal-article","created":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T06:04:30Z","timestamp":1772863470000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An improved data-model interactive approach for remaining useful life prediction of stochastic degrading devices by using independent threshold"],"prefix":"10.1007","volume":"17","author":[{"given":"Li","family":"Sun","sequence":"first","affiliation":[]},{"given":"Yinfei","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jinjun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Sizhao","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Honggen","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Guochao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,7]]},"reference":[{"key":"2882_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.108063","volume":"217","author":"Y Hu","year":"2022","unstructured":"Hu Y, Miao X, Si Y, Pan E, Zio E (2022) Prognostics and health management: a review from the perspectives of design, development and decision. Reliab Eng Syst Saf 217:108063. https:\/\/doi.org\/10.1016\/j.ress.2021.108063","journal-title":"Reliab Eng Syst Saf"},{"key":"2882_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.triboint.2023.109231","volume":"192","author":"TN Sindhu","year":"2024","unstructured":"Sindhu TN, \u00c7olak AB, Lone SA, Shafiq A, Abushal TA (2024) A decreasing failure rate model with a novel approach to enhance the artificial neural network\u2019s structure for engineering and disease data analysis. Tribol Int 192:109231. https:\/\/doi.org\/10.1016\/j.triboint.2023.109231","journal-title":"Tribol Int"},{"key":"2882_CR3","doi-asserted-by":"publisher","first-page":"3616","DOI":"10.1002\/qre.3155","volume":"38","author":"A Shafiq","year":"2022","unstructured":"Shafiq A, \u00c7olak AB, Sindhu TN (2022) Reliability investigation of exponentiated weibull distribution using IPL through numerical and artificial neural network modeling. Qual Reliab Eng Int 38:3616\u20133631. https:\/\/doi.org\/10.1002\/qre.3155","journal-title":"Qual Reliab Eng Int"},{"key":"2882_CR4","doi-asserted-by":"publisher","first-page":"2398","DOI":"10.1002\/qre.3352","volume":"39","author":"TN Sindhu","year":"2023","unstructured":"Sindhu TN, \u00c7olak AB, Lone SA, Shafiq A (2023) Reliability study of generalized exponential distribution based on inverse power law using artificial neural network with bayesian regularization. Qual Reliab Eng Int 39:2398\u20132421. https:\/\/doi.org\/10.1002\/qre.3352","journal-title":"Qual Reliab Eng Int"},{"key":"2882_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10407790.2023.2273512","volume":"85","author":"A Shafiq","year":"2024","unstructured":"Shafiq A, \u00c7olak AB, Sindhu TN (2024) Development of an intelligent computing system using neural networks for modeling bioconvection flow of second-grade nanofluid with gyrotactic microorganisms. Numer Heat Transf Part B Fundam 85:1\u201318. https:\/\/doi.org\/10.1080\/10407790.2023.2273512","journal-title":"Numer Heat Transf Part B Fundam"},{"key":"2882_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124582","volume":"255","author":"G Nain","year":"2024","unstructured":"Nain G, Pattanaik KK, Sharma GK, Gauttam H (2024) PackMASNet: an information integration approach for quality inspection in industry 5.0. Expert Syst Appl 255:124582. https:\/\/doi.org\/10.1016\/j.eswa.2024.124582","journal-title":"Expert Syst Appl"},{"key":"2882_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-024-02968-1","volume":"5","author":"G Nain","year":"2024","unstructured":"Nain G, Pattanaik KK, Sharma GK (2024) A novel semi-supervised learning for industrial edge computing platforms in quality prediction. SN Comput Sci 5:637. https:\/\/doi.org\/10.1007\/s42979-024-02968-1","journal-title":"SN Comput Sci"},{"key":"2882_CR8","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TNNLS.2020.2977132","volume":"32","author":"Y Gao","year":"2021","unstructured":"Gao Y, Wen Y, Wu J (2021) A neural network-based joint prognostic model for data fusion and remaining useful life prediction. IEEE Trans Neural Netw Learn Syst 32:117\u2013127. https:\/\/doi.org\/10.1109\/TNNLS.2020.2977132","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2882_CR9","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.ress.2018.11.027","volume":"183","author":"A Listou Ellefsen","year":"2019","unstructured":"Listou Ellefsen A, Bj\u00f8rlykhaug E, \u00c6s\u00f8y V, Ushakov S, Zhang H (2019) Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture. Reliab Eng Syst Saf 183:240\u2013251. https:\/\/doi.org\/10.1016\/j.ress.2018.11.027","journal-title":"Reliab Eng Syst Saf"},{"key":"2882_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.109006","volume":"231","author":"Y Wang","year":"2023","unstructured":"Wang Y, Lei Y, Li N, Yan T, Si X (2023) Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery. Reliab Eng Syst Saf 231:109006. https:\/\/doi.org\/10.1016\/j.ress.2022.109006","journal-title":"Reliab Eng Syst Saf"},{"key":"2882_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.110637","volume":"189","author":"C Zhao","year":"2022","unstructured":"Zhao C, Huang X, Li Y, Li S (2022) A novel remaining useful life prediction method based on gated attention mechanism capsule neural network. Measurement 189:110637. https:\/\/doi.org\/10.1016\/j.measurement.2021.110637","journal-title":"Measurement"},{"key":"2882_CR12","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.jmsy.2023.02.019","volume":"68","author":"B Qiang","year":"2023","unstructured":"Qiang B, Shi K, Liu N, Ren J, Shi Y (2023) Integrating physics-informed recurrent gaussian process regression into instance transfer for predicting tool wear in milling process. J Manuf Syst 68:42\u201355. https:\/\/doi.org\/10.1016\/j.jmsy.2023.02.019","journal-title":"J Manuf Syst"},{"key":"2882_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-54030-5","volume-title":"Data-driven remaining useful life prognosis techniques","author":"X Si","year":"2017","unstructured":"Si X, Zhang Z, Hu C (2017) Data-driven remaining useful life prognosis techniques. Springer Berlin Heidelberg, Berlin, Heidelberg"},{"key":"2882_CR14","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1016\/j.futures.2006.11.011","volume":"39","author":"MBA van Asselt","year":"2007","unstructured":"van Asselt MBA, Mesman J, van\u2018t Klooster SA (2007) Dealing with prognostic uncertainty. Futures 39:669\u2013684. https:\/\/doi.org\/10.1016\/j.futures.2006.11.011","journal-title":"Futures"},{"key":"2882_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.130555","volume":"293","author":"H Pang","year":"2024","unstructured":"Pang H, Chen K, Geng Y, Wu L, Wang F, Liu J (2024) Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter. Energy 293:130555. https:\/\/doi.org\/10.1016\/j.energy.2024.130555","journal-title":"Energy"},{"key":"2882_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.109347","volume":"179","author":"J Shi","year":"2022","unstructured":"Shi J, Rivera A, Wu D (2022) Battery health management using physics-informed machine learning: online degradation modeling and remaining useful life prediction. Mech Syst Signal Process 179:109347. https:\/\/doi.org\/10.1016\/j.ymssp.2022.109347","journal-title":"Mech Syst Signal Process"},{"key":"2882_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/3203959","volume":"2019","author":"Y Zheng","year":"2019","unstructured":"Zheng Y (2019) Predicting remaining useful life based on Hilbert-Huang entropy with degradation model. J Electr Comput Eng 2019:1\u201311. https:\/\/doi.org\/10.1155\/2019\/3203959","journal-title":"J Electr Comput Eng"},{"key":"2882_CR18","doi-asserted-by":"publisher","DOI":"10.1002\/qre.3646","author":"K Song","year":"2024","unstructured":"Song K (2024) A new multivariate gamma process model for degradation analysis. Qual Reliab Eng Int. https:\/\/doi.org\/10.1002\/qre.3646","journal-title":"Qual Reliab Eng Int"},{"key":"2882_CR19","doi-asserted-by":"publisher","DOI":"10.1080\/24725854.2024.2389538","author":"A Xu","year":"2024","unstructured":"Xu A, Fang G, Zhuang L, Gu C (2024) A multivariate student-t process model for dependent tail-weighted degradation data. IISE Trans. https:\/\/doi.org\/10.1080\/24725854.2024.2389538","journal-title":"IISE Trans"},{"key":"2882_CR20","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.isatra.2021.07.002","volume":"125","author":"H Liu","year":"2022","unstructured":"Liu H, Song W, Zio E (2022) Fractional l\u00e9vy stable motion with LRD for RUL and reliability analysis of li-ion battery. ISA Trans 125:360\u2013370. https:\/\/doi.org\/10.1016\/j.isatra.2021.07.002","journal-title":"ISA Trans"},{"key":"2882_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TASE.2014.2349733","volume":"13","author":"K Liu","year":"2014","unstructured":"Liu K, Huang S (2014) Integration of data fusion methodology and degradation modeling process to improve prognostics. IEEE Trans Autom Sci Eng 13:1\u201311. https:\/\/doi.org\/10.1109\/TASE.2014.2349733","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"2882_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TASE.2015.2446752","volume":"14","author":"K Liu","year":"2015","unstructured":"Liu K, Chehade A, Song C (2015) Optimize the signal quality of the composite health index via data fusion for degradation modeling and prognostic analysis. IEEE Trans Autom Sci Eng 14:1\u201311. https:\/\/doi.org\/10.1109\/TASE.2015.2446752","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"2882_CR23","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1080\/00224065.2018.1436829","volume":"50","author":"A Chehade","year":"2018","unstructured":"Chehade A, Song C, Liu K, Saxena A, Zhang X (2018) A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes. J Qual Technol 50:150\u2013165. https:\/\/doi.org\/10.1080\/00224065.2018.1436829","journal-title":"J Qual Technol"},{"key":"2882_CR24","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1109\/TASE.2018.2890608","volume":"16","author":"M Kim","year":"2019","unstructured":"Kim M, Song C, Liu K (2019) A generic health index approach for multisensor degradation modeling and sensor selection. IEEE Trans Autom Sci Eng 16:1426\u20131437. https:\/\/doi.org\/10.1109\/TASE.2018.2890608","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"2882_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.108526","volume":"167","author":"T Li","year":"2022","unstructured":"Li T, Si X, Pei H, Sun L (2022) Data-model interactive prognosis for multi-sensor monitored stochastic degrading devices. Mech Syst Signal Process 167:108526. https:\/\/doi.org\/10.1016\/j.ymssp.2021.108526","journal-title":"Mech Syst Signal Process"},{"key":"2882_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109344","volume":"237","author":"T Li","year":"2023","unstructured":"Li T, Pei H, Si X, Lei Y (2023) Prognosis for stochastic degrading systems with massive data: a data-model interactive perspective. Reliab Eng Syst Saf 237:109344. https:\/\/doi.org\/10.1016\/j.ress.2023.109344","journal-title":"Reliab Eng Syst Saf"},{"key":"2882_CR27","doi-asserted-by":"publisher","first-page":"7571","DOI":"10.1007\/s13369-023-08486-1","volume":"49","author":"U Samal","year":"2024","unstructured":"Samal U, Kumar A (2024) Enhancing software reliability forecasting through a hybrid ARIMA-ANN model. Arab J Sci Eng 49:7571\u20137584. https:\/\/doi.org\/10.1007\/s13369-023-08486-1","journal-title":"Arab J Sci Eng"},{"key":"2882_CR28","doi-asserted-by":"publisher","DOI":"10.1142\/S0218539324500098","author":"U Samal","year":"2024","unstructured":"Samal U, Kumar A (2024) A neural network approach for software reliability prediction. Int J Reliab Qual Saf Eng. https:\/\/doi.org\/10.1142\/S0218539324500098","journal-title":"Int J Reliab Qual Saf Eng"},{"key":"2882_CR29","doi-asserted-by":"publisher","first-page":"2230","DOI":"10.1007\/s11771-016-3281-z","volume":"23","author":"S Tang","year":"2016","unstructured":"Tang S, Yu C, Feng Y, Xie J, Gao Q, Si X (2016) Remaining useful life estimation based on wiener degradation processes with random failure threshold. J Cent South Univ 23:2230\u20132241. https:\/\/doi.org\/10.1007\/s11771-016-3281-z","journal-title":"J Cent South Univ"},{"key":"2882_CR30","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.1049\/pel2.12611","volume":"17","author":"Q Wu","year":"2024","unstructured":"Wu Q, Xu B, Xiao L, Wang Q (2024) A remaining useful life prediction method of SiC MOSFET considering failure threshold uncertainty. IET Power Electron 17:1594\u20131606. https:\/\/doi.org\/10.1049\/pel2.12611","journal-title":"IET Power Electron"},{"key":"2882_CR31","doi-asserted-by":"publisher","first-page":"415","DOI":"10.23919\/JSEE.2020.000018","volume":"31","author":"Z Wang","year":"2020","unstructured":"Wang Z, Chen Y, Cai Z, Gao Y, Wang L (2020) Methods for predicting the remaining useful life of equipment in consideration of the random failure threshold. J Syst Eng Electron 31:415\u2013431. https:\/\/doi.org\/10.23919\/JSEE.2020.000018","journal-title":"J Syst Eng Electron"},{"key":"2882_CR32","doi-asserted-by":"publisher","first-page":"530","DOI":"10.23919\/JSEE.2023.000042","volume":"34","author":"F Wang","year":"2023","unstructured":"Wang F, Tang S, Li L, Sun X, Yu C, Si X (2023) Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold. J Syst Eng Electron 34:530\u2013542. https:\/\/doi.org\/10.23919\/JSEE.2023.000042","journal-title":"J Syst Eng Electron"},{"key":"2882_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2024.109961","volume":"244","author":"B Wu","year":"2024","unstructured":"Wu B, Zhang X, Shi H, Zeng J (2024) Failure mode division and remaining useful life prognostics of multi-indicator systems with multi-fault. Reliab Eng Syst Saf 244:109961. https:\/\/doi.org\/10.1016\/j.ress.2024.109961","journal-title":"Reliab Eng Syst Saf"},{"key":"2882_CR34","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TASE.2013.2250282","volume":"10","author":"K Liu","year":"2013","unstructured":"Liu K, Gebraeel NZ, Shi J (2013) A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Trans Autom Sci Eng 10:652\u2013664. https:\/\/doi.org\/10.1109\/TASE.2013.2250282","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"2882_CR35","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":"2882_CR36","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. http:\/\/arxiv.org\/abs\/1412.3555. Accessed 27 March 2023"},{"key":"2882_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ailsci.2023.100082","volume":"4","author":"A Shafiq","year":"2023","unstructured":"Shafiq A, \u00c7olak AB, Sindhu TN, Lone SA, Abushal TA (2023) Modeling and survival exploration of breast carcinoma: a statistical, maximum likelihood estimation, and artificial neural network perspective. Artif Intell Life Sci 4:100082. https:\/\/doi.org\/10.1016\/j.ailsci.2023.100082","journal-title":"Artif Intell Life Sci"},{"key":"2882_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm J. R Stat Soc Series B Stat Methodol 39:1\u201322. https:\/\/doi.org\/10.1111\/j.2517-6161.1977.tb01600.x","journal-title":"R Stat Soc Series B Stat Methodol"},{"key":"2882_CR39","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1214\/aos\/1176346060","volume":"11","author":"CFJ Wu","year":"1983","unstructured":"Wu CFJ (1983) On the convergence properties of the EM algorithm. Ann Stat 11:95\u2013103","journal-title":"Ann Stat"},{"key":"2882_CR40","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.ejor.2012.10.030","volume":"226","author":"X Si","year":"2013","unstructured":"Si X, Wang W, Chen M-Y, hu C, Zhou D-H (2013) A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution. Eur J Oper Res 226:53\u201366. https:\/\/doi.org\/10.1016\/j.ejor.2012.10.030","journal-title":"Eur J Oper Res"},{"key":"2882_CR41","doi-asserted-by":"publisher","unstructured":"Saxena A, Goebel K, Simon D, Eklund N (2008) Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 international conference on prognostics and health management, IEEE, Denver, CO, USA. p 1\u20139. https:\/\/doi.org\/10.1109\/PHM.2008.4711414.","DOI":"10.1109\/PHM.2008.4711414"},{"key":"2882_CR42","unstructured":"Chen Y-C (2021) 2010 PHM society conference data challenge. https:\/\/ieee-dataport.org\/documents\/2010-phm-society-conference-data-challenge. Accessed 13 Nov 2023"},{"key":"2882_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.112739","volume":"213","author":"X Zhang","year":"2023","unstructured":"Zhang X, Shi B, Feng B, Liu L, Gao Z (2023) A hybrid method for cutting tool RUL prediction based on CNN and multistage wiener process using small sample data. Measurement 213:112739. https:\/\/doi.org\/10.1016\/j.measurement.2023.112739","journal-title":"Measurement"},{"key":"2882_CR44","doi-asserted-by":"publisher","first-page":"5023","DOI":"10.1109\/TII.2019.2900295","volume":"15","author":"H Miao","year":"2019","unstructured":"Miao H, Li B, Sun C, Liu J (2019) Joint learning of degradation assessment and RUL prediction for aeroengines via dual-task deep LSTM networks. IEEE Trans Ind Inform 15:5023\u20135032. https:\/\/doi.org\/10.1109\/TII.2019.2900295","journal-title":"IEEE Trans Ind Inform"},{"key":"2882_CR45","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 Netw Learn Syst 28:2306\u20132318. https:\/\/doi.org\/10.1109\/TNNLS.2016.2582798","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2882_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108685","volume":"226","author":"J Zhuang","year":"2022","unstructured":"Zhuang J, Jia M, Cao Y, Zhao X (2022) Semi-supervised double attention guided assessment approach for remaining useful life of rotating machinery. Reliab Eng Syst Saf 226:108685. https:\/\/doi.org\/10.1016\/j.ress.2022.108685","journal-title":"Reliab Eng Syst Saf"},{"key":"2882_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121859","volume":"238","author":"F Xiang","year":"2024","unstructured":"Xiang F, Zhang Y, Zhang S, Wang Z, Qiu L, Choi J-H (2024) Bayesian gated-transformer model for risk-aware prediction of aero-engine remaining useful life. Expert Syst Appl 238:121859. https:\/\/doi.org\/10.1016\/j.eswa.2023.121859","journal-title":"Expert Syst Appl"},{"key":"2882_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.108297","volume":"221","author":"J Zhang","year":"2022","unstructured":"Zhang J, Jiang Y, Wu S, Li X, Luo H, Yin S (2022) Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism. Reliab Eng Syst Saf 221:108297. https:\/\/doi.org\/10.1016\/j.ress.2021.108297","journal-title":"Reliab Eng Syst Saf"},{"key":"2882_CR49","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":"2882_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105860","volume":"120","author":"K Zhao","year":"2023","unstructured":"Zhao K, Jia Z, Jia F, Shao H (2023) Multi-scale integrated deep self-attention network for predicting remaining useful life of aero-engine. Eng Appl Artif Intell 120:105860. https:\/\/doi.org\/10.1016\/j.engappai.2023.105860","journal-title":"Eng Appl Artif Intell"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02882-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02882-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02882-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T06:04:32Z","timestamp":1772863472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02882-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,7]]},"references-count":50,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["2882"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02882-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,7]]},"assertion":[{"value":"17 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"182"}}