{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T12:39:11Z","timestamp":1779021551206,"version":"3.51.4"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-024-06889-x","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T20:29:22Z","timestamp":1741033762000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Leveraging working-condition-related features for enhanced cross-domain remaining useful life prediction of aircraft engines"],"prefix":"10.1007","volume":"81","author":[{"given":"Zhiyao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiting","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengpeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrico","family":"Zio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"6889_CR1","doi-asserted-by":"publisher","unstructured":"Yang J, Tang S, Fang P, Wang F, Sun X, Si X (2024) Remaining useful life prediction of implicit linear wiener degradation process based on multi-source information. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 238(1):93\u2013111. https:\/\/doi.org\/10.1177\/1748006X22113260","DOI":"10.1177\/1748006X22113260"},{"key":"6889_CR2","doi-asserted-by":"publisher","unstructured":"Dash BM, Prakash O, Samantaray AK (2023) Failure prognosis of the components with unlike degradation trends: A data-driven approach. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 237(6):1132\u20131149. https:\/\/doi.org\/10.1177\/1748006X22111930","DOI":"10.1177\/1748006X22111930"},{"key":"6889_CR3","doi-asserted-by":"publisher","first-page":"4660","DOI":"10.1007\/s11227-022-04833-5","volume":"79","author":"AL Lima","year":"2023","unstructured":"Lima AL, Aranha VM, Nascimento EGS (2023) Predictive maintenance applied to mission critical supercomputing environments: remaining useful life estimation of a hydraulic cooling system using deep learning. J Supercomput 79:4660\u20134684. https:\/\/doi.org\/10.1007\/s11227-022-04833-5","journal-title":"J Supercomput"},{"issue":"1","key":"6889_CR4","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s10479-023-05312-7","volume":"342","author":"X Wen","year":"2024","unstructured":"Wen X, Chung S-H, Ma H-L, Khan WA (2024) Airline crew scheduling with sustainability enhancement by data analytics under circular economy. Ann Operations Res 342(1):959\u2013985. https:\/\/doi.org\/10.1007\/s10479-023-05312-7","journal-title":"Ann Operations Res"},{"key":"6889_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jairtraman.2023.102488","volume":"114","author":"WA Khan","year":"2024","unstructured":"Khan WA, Chung S-H, Eltoukhy AE, Khurshid F (2024) A novel parallel series data-driven model for IATA-coded flight delays prediction and features analysis. J Air Transp Manag 114:102488. https:\/\/doi.org\/10.1016\/j.jairtraman.2023.102488","journal-title":"J Air Transp Manag"},{"issue":"11","key":"6889_CR6","doi-asserted-by":"publisher","first-page":"12018","DOI":"10.1109\/JSEN.2023.3269030","volume":"23","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Chen P, Xing C, Liu B, Wang R, Li L, Chen X, Zio E (2023) A data augmentation boosted dual informer framework for the performance degradation prediction of aero-engines. IEEE Sens J 23(11):12018\u201312030. https:\/\/doi.org\/10.1109\/JSEN.2023.3269030","journal-title":"IEEE Sens J"},{"key":"6889_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-023-02303-0","author":"WA Khan","year":"2024","unstructured":"Khan WA, Masoud M, Eltoukhy AE, Ullah M (2024) Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time. J Intell Manuf. https:\/\/doi.org\/10.1007\/s10845-023-02303-0","journal-title":"J Intell Manuf"},{"key":"6889_CR8","doi-asserted-by":"publisher","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180\u20131189. https:\/\/doi.org\/10.48550\/arXiv.1409.7495","DOI":"10.48550\/arXiv.1409.7495"},{"key":"6889_CR9","doi-asserted-by":"publisher","unstructured":"Jiang J, Shu Y, Wang J, Long M (2022) Transferability in deep learning: a survey. arXiv e-prints https:\/\/doi.org\/10.48550\/arXiv.2201.05867","DOI":"10.48550\/arXiv.2201.05867"},{"key":"6889_CR10","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.jmsy.2022.11.003","volume":"66","author":"G Liu","year":"2023","unstructured":"Liu G, Shen W, Gao L, Kusiak A (2023) Automated broad transfer learning for cross-domain fault diagnosis. J Manuf Syst 66:27\u201341. https:\/\/doi.org\/10.1016\/j.jmsy.2022.11.003","journal-title":"J Manuf Syst"},{"issue":"1","key":"6889_CR11","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1093\/jcde\/qwae018","volume":"11","author":"Q Liu","year":"2024","unstructured":"Liu Q, Zhang Z, Guo P, Wang Y, Liang J (2024) Enhancing aircraft engine remaining useful life prediction via multiscale deep transfer learning with limited data. J Comput Des Eng 11(1):343\u2013355. https:\/\/doi.org\/10.1093\/jcde\/qwae018","journal-title":"J Comput Des Eng"},{"issue":"1561\/116","key":"6889_CR12","first-page":"00000192","volume":"10","author":"X Liu","year":"2022","unstructured":"Liu X, Yoo C, Xing F, Oh H, El Fakhri G, Kang J-W, Woo J et al (2022) Deep unsupervised domain adaptation: a review of recent advances and perspectives. APSIPA Trans Signal Inf Process 10(1561\/116):00000192","journal-title":"APSIPA Trans Signal Inf Process"},{"key":"6889_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.109608","volume":"183","author":"R He","year":"2023","unstructured":"He R, Tian Z, Zuo M (2023) A transferable neural network method for remaining useful life prediction. Mech Syst Signal Process 183:109608. https:\/\/doi.org\/10.1016\/j.ymssp.2022.109608","journal-title":"Mech Syst Signal Process"},{"key":"6889_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108722","volume":"226","author":"W Li","year":"2022","unstructured":"Li W, Shang Z, Gao M, Qian S, Feng Z (2022) Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions. Reliab Eng Syst Saf 226:108722. https:\/\/doi.org\/10.1016\/j.ress.2022.108722","journal-title":"Reliab Eng Syst Saf"},{"key":"6889_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106122","volume":"203","author":"Y Jin","year":"2020","unstructured":"Jin Y, Qin C, Liu J, Lin K, Liu C (2020) A novel domain adaptive residual network for automatic atrial fibrillation detection. Knowl-Based Syst 203:106122. https:\/\/doi.org\/10.1016\/j.knosys.2020.106122","journal-title":"Knowl-Based Syst"},{"key":"6889_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106974","volume":"222","author":"K Zhao","year":"2021","unstructured":"Zhao K, Jiang H, Wang K, Pei Z (2021) Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis. Knowl-Based Syst 222:106974. https:\/\/doi.org\/10.1016\/j.knosys.2021.106974","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"6889_CR17","doi-asserted-by":"publisher","first-page":"5254","DOI":"10.1109\/TMECH.2022.3177174","volume":"27","author":"Y Xiao","year":"2022","unstructured":"Xiao Y, Shao H, Han S, Huo Z, Wan J (2022) Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain. IEEE Trans Mech 27(6):5254\u20135263. https:\/\/doi.org\/10.1109\/TMECH.2022.3177174","journal-title":"IEEE Trans Mech"},{"key":"6889_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108986","volume":"231","author":"J Zhang","year":"2023","unstructured":"Zhang J, Li X, Tian J, Jiang Y, Luo H, Yin S (2023) A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition. Reliab Eng Syst Saf 231:108986. https:\/\/doi.org\/10.1016\/j.ress.2022.108986","journal-title":"Reliab Eng Syst Saf"},{"key":"6889_CR19","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.jmsy.2021.11.016","volume":"62","author":"H Cao","year":"2022","unstructured":"Cao H, Shao H, Zhong X, Deng Q, Yang X, Xuan J (2022) Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds. J Manuf Syst 62:186\u2013198. https:\/\/doi.org\/10.1016\/j.jmsy.2021.11.016","journal-title":"J Manuf Syst"},{"key":"6889_CR20","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.jmsy.2022.06.009","volume":"64","author":"K Su","year":"2022","unstructured":"Su K, Liu J, Xiong H (2022) A multi-level adaptation scheme for hierarchical bearing fault diagnosis under variable working conditions. J Manuf Syst 64:251\u2013260. https:\/\/doi.org\/10.1016\/j.jmsy.2022.06.009","journal-title":"J Manuf Syst"},{"key":"6889_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.109598","volume":"182","author":"R He","year":"2023","unstructured":"He R, Tian Z, Zuo M (2023) Machine prognostics under varying operating conditions based on state-space and neural network modeling. Mech Syst Signal Process 182:109598. https:\/\/doi.org\/10.1016\/j.ymssp.2022.109598","journal-title":"Mech Syst Signal Process"},{"key":"6889_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120276","volume":"227","author":"J Zhuang","year":"2023","unstructured":"Zhuang J, Cao Y, Jia M, Zhao X, Peng Q (2023) Remaining useful life prediction of bearings using multi-source adversarial online regression under online unknown conditions. Expert Syst Appl 227:120276. https:\/\/doi.org\/10.1016\/j.eswa.2023.120276","journal-title":"Expert Syst Appl"},{"key":"6889_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2023.109795","volume":"187","author":"K Li","year":"2024","unstructured":"Li K, Li Z, Jia X, Liu L, Chen M (2024) A domain adversarial graph convolutional network for intelligent monitoring of tool wear in machine tools. Comput Ind Eng 187:109795. https:\/\/doi.org\/10.1016\/j.cie.2023.109795","journal-title":"Comput Ind Eng"},{"key":"6889_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.108012","volume":"216","author":"S Fu","year":"2021","unstructured":"Fu S, Zhang Y, Lin L, Zhao M, Zhong S-s (2021) Deep residual LSTM with domain-invariance for remaining useful life prediction across domains. Reliab Eng Syst Saf 216:108012. https:\/\/doi.org\/10.1016\/j.ress.2021.108012","journal-title":"Reliab Eng Syst Saf"},{"key":"6889_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2022.3183134","author":"Z Ye","year":"2022","unstructured":"Ye Z, Yu J (2022) A selective adversarial adaptation network for remaining useful life prediction of machines under different working conditions. IEEE Syst J. https:\/\/doi.org\/10.1109\/JSYST.2022.3183134","journal-title":"IEEE Syst J"},{"issue":"3","key":"6889_CR26","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(3):3622\u20133637. https:\/\/doi.org\/10.1007\/s10489-022-03670-6","journal-title":"Appl Intell"},{"key":"6889_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110199","volume":"261","author":"Y Ding","year":"2023","unstructured":"Ding Y, Jia M, Cao Y, Ding P, Zhao X, Lee C-G (2023) Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions. Knowl-Based Syst 261:110199. https:\/\/doi.org\/10.1016\/j.knosys.2022.110199","journal-title":"Knowl-Based Syst"},{"issue":"8","key":"6889_CR28","doi-asserted-by":"publisher","first-page":"5239","DOI":"10.1109\/TII.2020.3032690","volume":"17","author":"M Ragab","year":"2021","unstructured":"Ragab M, Chen Z, Wu M, Foo CS, Kwoh CK, Yan R, Li X (2021) Contrastive adversarial domain adaptation for machine remaining useful life prediction. IEEE Trans Ind Inf 17(8):5239\u20135249. https:\/\/doi.org\/10.1109\/TII.2020.3032690","journal-title":"IEEE Trans Ind Inf"},{"key":"6889_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108599","volume":"225","author":"J Zhuang","year":"2022","unstructured":"Zhuang J, Jia M, Zhao X (2022) An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions. Reliab Eng Syst Saf 225:108599. https:\/\/doi.org\/10.1016\/j.ress.2022.108599","journal-title":"Reliab Eng Syst Saf"},{"key":"6889_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110139","volume":"191","author":"H Zhou","year":"2023","unstructured":"Zhou H, Lei Z, Zio E, Wen G, Liu Z, Su Y, Chen X (2023) Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions. Mech Syst Signal Process 191:110139. https:\/\/doi.org\/10.1016\/j.ymssp.2023.110139","journal-title":"Mech Syst Signal Process"},{"key":"6889_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.108265","volume":"219","author":"T Hu","year":"2022","unstructured":"Hu T, Guo Y, Gu L, Zhou Y, Zhang Z, Zhou Z (2022) Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method. Reliab Eng Syst Saf 219:108265. https:\/\/doi.org\/10.1016\/j.ress.2021.108265","journal-title":"Reliab Eng Syst Saf"},{"key":"6889_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2019.106682","volume":"195","author":"PRdO Costa","year":"2020","unstructured":"Costa PRdO, Ak\u00e7ay A, Zhang Y, Kaymak U (2020) Remaining useful lifetime prediction via deep domain adaptation. Reliab Eng Syst Saf 195:106682. https:\/\/doi.org\/10.1016\/j.ress.2019.106682","journal-title":"Reliab Eng Syst Saf"},{"key":"6889_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jmsy.2021.10.014","volume":"62","author":"K Zhang","year":"2022","unstructured":"Zhang K, Chen J, He S, Li F, Feng Y, Zhou Z (2022) Triplet metric driven multi-head GNN augmented with decoupling adversarial learning for intelligent fault diagnosis of machines under varying working condition. J Manuf Syst 62:1\u201316. https:\/\/doi.org\/10.1016\/j.jmsy.2021.10.014","journal-title":"J Manuf Syst"},{"key":"6889_CR34","doi-asserted-by":"publisher","unstructured":"Forouzandeh Shahraki A, Al-Dahidi S, Rahim Taleqani A, Yadav OP (2023) Using lstm neural network to predict remaining useful life of electrolytic capacitors in dynamic operating conditions. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 237(1):16\u201328. https:\/\/doi.org\/10.1177\/1748006X2210875","DOI":"10.1177\/1748006X2210875"},{"key":"6889_CR35","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":"6889_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108914","volume":"230","author":"S Dong","year":"2023","unstructured":"Dong S, Xiao J, Hu X, Fang N, Liu L, Yao J (2023) Deep transfer learning based on Bi-LSTM and attention for remaining useful life prediction of rolling bearing. Reliab Eng Syst Saf 230:108914. https:\/\/doi.org\/10.1016\/j.ress.2022.108914","journal-title":"Reliab Eng Syst Saf"},{"issue":"5","key":"6889_CR37","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1093\/jcde\/qwad081","volume":"10","author":"Z Wang","year":"2023","unstructured":"Wang Z, Liu T, Wu X, Liu C (2023) A diagnosis method for imbalanced bearing data based on improved smote model combined with CNN-AM. J Comput Des Eng 10(5):1930\u20131940. https:\/\/doi.org\/10.1093\/jcde\/qwad081","journal-title":"J Comput Des Eng"},{"key":"6889_CR38","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in Neural Information Processing Systems, pp. 5998\u20136008. https:\/\/doi.org\/10.48550\/arXiv.1706.03762"},{"key":"6889_CR39","doi-asserted-by":"publisher","unstructured":"Jose S, Ngouna RH, Nguyen KT, Medjaher K (2022) Solving time alignment issue of multimodal data for accurate prognostics with CNN-Transformer-LSTM network. In: 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), vol. 1, pp. 280\u2013285. https:\/\/doi.org\/10.1109\/CoDIT55151.2022.9804090 . IEEE","DOI":"10.1109\/CoDIT55151.2022.9804090"},{"key":"6889_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3160561","volume":"71","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Song W, Li Q (2022) Dual-aspect self-attention based on transformer for remaining useful life prediction. IEEE Trans Instrum Meas 71:1\u201311. https:\/\/doi.org\/10.1109\/TIM.2022.3160561","journal-title":"IEEE Trans Instrum Meas"},{"key":"6889_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108886","volume":"229","author":"D Xu","year":"2023","unstructured":"Xu D, Xiao X, Liu J, Sui S (2023) Spatio-temporal degradation modeling and remaining useful life prediction under multiple operating conditions based on attention mechanism and deep learning. Reliab Eng Syst Saf 229:108886. https:\/\/doi.org\/10.1016\/j.ress.2022.108886","journal-title":"Reliab Eng Syst Saf"},{"key":"6889_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109096","volume":"233","author":"J Zhang","year":"2023","unstructured":"Zhang J, Li X, Tian J, Luo H, Yin S (2023) An integrated multi-head dual sparse self-attention network for remaining useful life prediction. Reliab Eng Syst Saf 233:109096. https:\/\/doi.org\/10.1016\/j.ress.2023.109096","journal-title":"Reliab Eng Syst Saf"},{"key":"6889_CR43","doi-asserted-by":"publisher","first-page":"11558","DOI":"10.1007\/s11227-023-05126-1","volume":"79","author":"G Peng","year":"2023","unstructured":"Peng G, Qi L, Shui Y, Jianyu X, Xiang T, Chao G (2023) A transformer with layer-cross decoding for remaining useful life prediction. J Supercomput 79:11558\u201311584. https:\/\/doi.org\/10.1007\/s11227-023-05126-1","journal-title":"J Supercomput"},{"key":"6889_CR44","doi-asserted-by":"crossref","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021) Informer: Beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of AAAI, pp. 11106\u201311115. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17325","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"6889_CR45","doi-asserted-by":"publisher","unstructured":"Liu P, Qiu X, Huang X (2017) Adversarial multi-task learning for text classification. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1\u201310. ACL, https:\/\/doi.org\/10.18653\/v1\/P17-1001","DOI":"10.18653\/v1\/P17-1001"},{"key":"6889_CR46","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1608.06019","author":"K Bousmalis","year":"2016","unstructured":"Bousmalis K, Trigeorgis G, Silberman N, Krishnan D, Erhan D (2016) Domain separation networks. Advan Neural Inf Process Syst. https:\/\/doi.org\/10.48550\/arXiv.1608.06019","journal-title":"Advan Neural Inf Process Syst"},{"key":"6889_CR47","doi-asserted-by":"publisher","first-page":"6980","DOI":"10.48550\/arXiv.1412.6980","volume":"1412","author":"DP Kingma","year":"2014","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. CoRR 1412:6980. https:\/\/doi.org\/10.48550\/arXiv.1412.6980","journal-title":"CoRR"},{"key":"6889_CR48","unstructured":"Frederick DK, Decastro JA, Litt JS (2007) User\u2019s guide for the commercial modular aero-propulsion system simulation (C-MAPSS)."},{"key":"6889_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109164","volume":"126","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Chen X, Zio E (2022) A framework for predicting the remaining useful life of machinery working under time-varying operational conditions. Appl Soft Comput 126:109164. https:\/\/doi.org\/10.1016\/j.asoc.2022.109164","journal-title":"Appl Soft Comput"},{"key":"6889_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109350","volume":"237","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Chen X, Zio E, Longxiao L (2023) Multi-task learning boosted predictions of the remaining useful life of aero-engines under scenarios of working-condition shift. Reliab Eng Syst Saf 237:109350. https:\/\/doi.org\/10.1016\/j.ress.2023.109350","journal-title":"Reliab Eng Syst Saf"},{"issue":"5","key":"6889_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109287","volume":"178","author":"Y Cao","year":"2021","unstructured":"Cao Y, Jia M, Ding P, Ding Y (2021) Transfer learning for remaining useful life prediction of multi-conditions bearings based on bidirectional-GRU network. Measurement 178(5):109287. https:\/\/doi.org\/10.1016\/j.measurement.2021.109287","journal-title":"Measurement"},{"key":"6889_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105843","volume":"197","author":"X Li","year":"2020","unstructured":"Li X, Zhang W, Ma H, Luo Z, Li X (2020) Data alignments in machinery remaining useful life prediction using deep adversarial neural networks. Knowl-Based Syst 197:105843. https:\/\/doi.org\/10.1016\/j.knosys.2020.105843","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"6889_CR53","doi-asserted-by":"publisher","first-page":"438","DOI":"10.3390\/machines10060438","volume":"10","author":"Y Duan","year":"2022","unstructured":"Duan Y, Xiao J, Li H, Zhang J (2022) Cross-domain remaining useful life prediction based on adversarial training. Machines 10(6):438. https:\/\/doi.org\/10.3390\/machines10060438","journal-title":"Machines"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06889-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06889-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06889-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T20:29:26Z","timestamp":1741033766000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06889-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,3]]},"references-count":53,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["6889"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06889-x","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,3]]},"assertion":[{"value":"23 December 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 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":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"573"}}