{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:01:06Z","timestamp":1780765266182,"version":"3.54.1"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.aei.2026.104808","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T16:37:02Z","timestamp":1779899822000},"page":"104808","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["LKG-STNet: A large language model-assisted knowledge graph-guided spatiotemporal network for aero-engine remaining useful life prediction"],"prefix":"10.1016","volume":"75","author":[{"given":"Panpan","family":"Qiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9116-4769","authenticated-orcid":false,"given":"Jianzhuo","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxia","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongchuan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104808_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113098","article-title":"Global attention mechanism based deep learning for remaining useful life prediction of aero-engine","volume":"217","author":"Xu","year":"2023","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104808_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.116345","article-title":"Ctnet: Improving the non-stationary predictive ability of remaining useful life of aero-engine under multiple time-varying operating conditions","volume":"243","author":"Liu","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104808_b3","first-page":"1","article-title":"Remaining useful life prediction methodologies with health indicator dependence for rotating machinery: A comprehensive review","volume":"74","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"6","key":"10.1016\/j.aei.2026.104808_b4","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ad2bcc","article-title":"A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges","volume":"35","author":"Zhou","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.aei.2026.104808_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2020.103936","article-title":"Aircraft engines remaining useful life prediction with an adaptive denoising online sequential extreme learning machine","volume":"96","author":"Berghout","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"3","key":"10.1016\/j.aei.2026.104808_b6","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/TIE.2016.2623260","article-title":"Direct remaining useful life estimation based on support vector regression","volume":"64","author":"Khelif","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.aei.2026.104808_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119767","article-title":"LSTM-based failure prediction for railway rolling stock equipment","volume":"222","author":"De Simone","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104808_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103231","article-title":"Enhanced deep learning framework for accurate near-failure rul prediction of bearings in varying operating conditions","volume":"65","author":"Kumar","year":"2025","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104808_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111663","article-title":"GRU-AE-wiener: A generative adversarial network assisted hybrid gated recurrent unit with Wiener model for bearing remaining useful life estimation","volume":"220","author":"Wen","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.aei.2026.104808_b10","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.neucom.2021.12.035","article-title":"Ensembles of probabilistic LSTM predictors and correctors for bearing prognostics using industrial standards","volume":"491","author":"Nemani","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104808_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108528","article-title":"Bearing remaining useful life prediction with convolutional long short-term memory fusion networks","volume":"224","author":"Wan","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104808_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107652","article-title":"An integrated deep multiscale feature fusion network for aeroengine remaining useful life prediction with multisensor data","volume":"235","author":"Li","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104808_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103292","article-title":"Lithium-ion batteries remaining useful life prediction via Fourier-mixed window attention enhanced informer with decomposition and adaptive error correction strategy","volume":"65","author":"Cheng","year":"2025","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104808_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107519","article-title":"MHT: A multiscale hourglass-transformer for remaining useful life prediction of aircraft engine","volume":"128","author":"Guo","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104808_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125995","article-title":"Pstformer: A novel parallel spatial-temporal transformer for remaining useful life prediction of aeroengine","volume":"265","author":"Fu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104808_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103616","article-title":"An adaptive fused domain-cycling variational generative adversarial network for machine fault diagnosis under data scarcity","volume":"126","author":"Wang","year":"2026","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.aei.2026.104808_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.107968","article-title":"Global wavelet-integrated residual frequency attention regularized network for hypersonic flight vehicle fault diagnosis with imbalanced data","volume":"132","author":"Dong","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104808_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126669","article-title":"A convolutional-transformer reinforcement learning agent for rotating machinery fault diagnosis","volume":"271","author":"Li","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104808_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.110906","article-title":"Physics-informed neural network supported wiener process for degradation modeling and reliability prediction","volume":"258","author":"He","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104808_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102958","article-title":"Spatio-temporal attention-based hidden physics-informed neural network for remaining useful life prediction","volume":"63","author":"Jiang","year":"2025","journal-title":"Adv. Eng. Informatics"},{"issue":"3","key":"10.1016\/j.aei.2026.104808_b21","article-title":"A physics-informed neural network-based method for predicting degradation trajectories and remaining useful life of supercapacitors","volume":"4","author":"Lixin","year":"2025","journal-title":"Green Energy Intell. Transp."},{"key":"10.1016\/j.aei.2026.104808_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.109926","article-title":"RUL prediction for two-phase degrading systems considering physical damage observations","volume":"244","author":"Cai","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104808_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107878","article-title":"Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction","volume":"215","author":"Li","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104808_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122041","article-title":"State of health and remaining useful life prediction of lithium-ion batteries with conditional graph convolutional network","volume":"238","author":"Wei","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104808_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.102120","article-title":"Spatial-temporal dual-channel adaptive graph convolutional network for remaining useful life prediction with multi-sensor information fusion","volume":"57","author":"Zhang","year":"2023","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104808_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111738","article-title":"An adaptive model with dual-dimensional attention for remaining useful life prediction of aero-engine","volume":"293","author":"Gan","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104808_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110685","article-title":"Physics-informed spatio-temporal hybrid neural networks for predicting remaining useful life in aircraft engine","volume":"256","author":"Zhou","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104808_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109514","article-title":"A novel dual attention mechanism combined with knowledge for remaining useful life prediction based on gated recurrent units","volume":"239","author":"Li","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104808_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.110928","article-title":"Knowledge embedded spatial\u2013temporal graph convolutional networks for remaining useful life prediction","volume":"259","author":"Cai","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104808_b30","series-title":"Are Transformers Effective for Time Series Forecasting?","first-page":"11121","author":"Zeng","year":"2023"},{"key":"10.1016\/j.aei.2026.104808_b31","first-page":"459","article-title":"Tsmixer: Lightweight MLP-mixer model for multivariate time series forecasting","author":"Ekambaram","year":"2023"},{"key":"10.1016\/j.aei.2026.104808_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.113079","article-title":"Common distribution discrepancy knowledge distilling: A new out-of-distribution generalization framework for machinery RUL prediction","volume":"237","author":"Qian","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"issue":"8","key":"10.1016\/j.aei.2026.104808_b33","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1109\/JAS.2025.125126","article-title":"Dkamformer: Domain knowledge-augmented multiscale transformer for remaining useful life prediction of aeroengine","volume":"12","author":"Fu","year":"2025","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"10.1016\/j.aei.2026.104808_b34","unstructured":"J. Zhou, J. Luo, J. Qi, Y. Qin, Knowledge Library Network for Non-Exemplar Incremental Remaining Useful Life Prediction, IEEE\/ASME Trans. Mechatronics."},{"key":"10.1016\/j.aei.2026.104808_b35","series-title":"MLP-mixer: An all-MLP architecture for vision","author":"Tolstikhin","year":"2021"},{"key":"10.1016\/j.aei.2026.104808_b36","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.aei.2026.104808_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103553","article-title":"Sequential multi-objective multi-agent reinforcement learning approach for system predictive maintenance of turbofan engine","volume":"67","author":"Chen","year":"2025","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.aei.2026.104808_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129487","article-title":"Dual-attention enhanced variational encoding for interpretable remaining useful life prediction","volume":"624","author":"Liu","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104808_b39","series-title":"2018 Prognostics and System Health Management Conference (PHM-Chongqing)","first-page":"1037","article-title":"Remaining useful life estimation in prognostics using deep bidirectional LSTM neural network","author":"Wang","year":"2018"},{"key":"10.1016\/j.aei.2026.104808_b40","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."},{"key":"10.1016\/j.aei.2026.104808_b41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.psep.2019.08.019","article-title":"A new method for determining coal seam permeability redistribution induced by roadway excavation and its applications","volume":"131","author":"Liu","year":"2019","journal-title":"Process. Saf. Environ. Prot."},{"issue":"12","key":"10.1016\/j.aei.2026.104808_b42","doi-asserted-by":"crossref","first-page":"9594","DOI":"10.1109\/JIOT.2020.3004452","article-title":"Distributed attention-based temporal convolutional network for remaining useful life prediction","volume":"8","author":"Song","year":"2021","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"10.1016\/j.aei.2026.104808_b43","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1109\/TII.2020.2983760","article-title":"Remaining useful life prediction using a novel feature-attention-based end-to-end approach","volume":"17","author":"Liu","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.aei.2026.104808_b44","first-page":"11106","article-title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","volume":"vol. 35","author":"Zhou","year":"2021"},{"key":"10.1016\/j.aei.2026.104808_b45","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.aei.2026.104808_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108653","article-title":"The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study","volume":"168","author":"Li","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.aei.2026.104808_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108590","article-title":"Trend attention fully convolutional network for remaining useful life estimation","volume":"225","author":"Fan","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104808_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108353","article-title":"Variational encoding approach for interpretable assessment of remaining useful life estimation","volume":"222","author":"Costa","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104808_b49","series-title":"The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","author":"Zhang","year":"2023"},{"key":"10.1016\/j.aei.2026.104808_b50","first-page":"11121","article-title":"Are transformers effective for time series forecasting?","volume":"vol. 37","author":"Zeng","year":"2023"},{"key":"10.1016\/j.aei.2026.104808_b51","series-title":"The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","author":"Nie","year":"2023"},{"key":"10.1016\/j.aei.2026.104808_b52","series-title":"The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","author":"Wu","year":"2023"},{"key":"10.1016\/j.aei.2026.104808_b53","series-title":"Tsmixer: An all-mlp architecture for time series forecasting","author":"Chen","year":"2023"},{"key":"10.1016\/j.aei.2026.104808_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109096","article-title":"An integrated multi-head dual sparse self-attention network for remaining useful life prediction","volume":"233","author":"Zhang","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104808_b55","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107241","article-title":"An attention-based temporal convolutional network method for predicting remaining useful life of aero-engine","volume":"127","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104808_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107365","article-title":"MachNet, a general deep learning architecture for predictive maintenance within the industry 4.0 paradigm","volume":"127","author":"Jaenal","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104808_b57","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2025.137253","article-title":"A spatial\u2013temporal graph structure automatic feedback learning system with tensor fusion and its application on engine rul prediction","volume":"334","author":"Liu","year":"2025","journal-title":"Energy"},{"key":"10.1016\/j.aei.2026.104808_b58","series-title":"BatteryLife: A comprehensive dataset and benchmark for battery life prediction","author":"Tan","year":"2025"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626005008?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626005008?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:33:28Z","timestamp":1780763608000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626005008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":58,"alternative-id":["S1474034626005008"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104808","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"LKG-STNet: A large language model-assisted knowledge graph-guided spatiotemporal network for aero-engine remaining useful life prediction","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104808","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"104808"}}