{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T07:14:03Z","timestamp":1774077243084,"version":"3.50.1"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2025\\u202F J01311"],"award-info":[{"award-number":["2025\\u202F J01311"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2026C01034"],"award-info":[{"award-number":["2026C01034"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.aei.2026.104507","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T08:20:56Z","timestamp":1772526056000},"page":"104507","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A heterogeneous graph neural network with spatial\u2013temporal and operating condition-aware message passing mechanism for RUL prediction of aero-engines"],"prefix":"10.1016","volume":"73","author":[{"given":"Ruoyao","family":"Tian","sequence":"first","affiliation":[]},{"given":"Haorui","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Biao","family":"Mei","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Weidong","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104507_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124538","article-title":"Residual-based adversarial feature decoupling for remaining useful life prediction of aero-engines under variable operating conditions","volume":"255","author":"Wen","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104507_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.109610","article-title":"Time-varying trajectory modeling via dynamic governing network for remaining useful life prediction","volume":"182","author":"Zhou","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.aei.2026.104507_b0015","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. Inform."},{"key":"10.1016\/j.aei.2026.104507_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103907","article-title":"Bayesian physics-informed neural networks with iterative ensemble kalman inversion for RUL prediction and uncertainty quantification","volume":"69","author":"Xie","year":"2026","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104507_b0025","article-title":"Cross-attention multi-scale state space model for remaining useful life prediction of aircraft engines","volume":"69","author":"Zhang","year":"2026","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104507_b0030","doi-asserted-by":"crossref","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","article-title":"Deep Learning Algorithms for Bearing Fault Diagnostics\u2014A Comprehensive Review","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104507_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110154","article-title":"Least squares smoothed k-nearest neighbors online prediction of the remaining useful life of a NASA turbofan","volume":"190","author":"Viale","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.aei.2026.104507_b0040","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.apenergy.2015.08.119","article-title":"A novel multistage support Vector Machine based approach for Li ion battery remaining useful life estimation","volume":"159","author":"Patil","year":"2015","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104507_b0045","doi-asserted-by":"crossref","first-page":"7771","DOI":"10.1109\/TII.2022.3206339","article-title":"Optimized Random Forest Model for remaining Useful Life Prediction of Experimental Bearings","volume":"19","author":"Alfarizi","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.aei.2026.104507_b0050","article-title":"Remaining useful life estimation of bearing using spatio-temporal convolutional transformer","volume":"35","author":"Zhu","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.aei.2026.104507_b0055","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1109\/TVT.2023.3319377","article-title":"Multi-Resolution LSTM-Based Prediction Model for remaining Useful Life of Aero-Engine","volume":"73","author":"Xu","year":"2024","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.aei.2026.104507_b0060","first-page":"1","article-title":"Prediction of remaining Useful Life of Rolling Bearings based on Multiscale Efficient Channel attention CNN and Bidirectional GRU","volume":"73","author":"Ma","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104507_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.123622","article-title":"Remaining useful life prediction of lithium-ion batteries using a hybrid model","volume":"248","author":"Yao","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.aei.2026.104507_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.112739","article-title":"A hybrid method for cutting tool RUL prediction based on CNN and multistage Wiener process using small sample data","volume":"213","author":"Zhang","year":"2023","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104507_b0075","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1007\/s10115-022-01756-8","article-title":"Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond","volume":"64","author":"Li","year":"2022","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.aei.2026.104507_b0080","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.2026.104507_b0085","first-page":"1","article-title":"Predicting the remaining Useful Life of Aircraft Engines using Spatial and Temporal attention Mechanisms","volume":"74","author":"Shan","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104507_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2025.110964","article-title":"A dual-path architecture based on time series decomposition and degradation correction for remaining useful life prediction of aero-engine","volume":"203","author":"Wu","year":"2025","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.aei.2026.104507_b0095","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The Graph Neural Network Model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.aei.2026.104507_b0100","article-title":"A Survey on Graph Neural Networks for remaining Useful Life Prediction: Methodologies","author":"Wang","year":"2024","journal-title":"Evaluation and Future Trends"},{"key":"10.1016\/j.aei.2026.104507_b0105","doi-asserted-by":"crossref","first-page":"21694","DOI":"10.1109\/JSEN.2024.3400249","article-title":"Digital twin-assisted multiview reconstruction enhanced domain adaptation graph networks for aero-engine gas path fault diagnosis","volume":"24","author":"Xu","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.aei.2026.104507_b0110","first-page":"1","article-title":"Dual-channel degradation monitoring based on graph neural network for aero-engine remaining useful life prediction","volume":"74","author":"Cao","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104507_b0115","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40649-019-0069-y","article-title":"Graph convolutional networks: a comprehensive review","volume":"6","author":"Zhang","year":"2019","journal-title":"Comput. Soc. Netw."},{"key":"10.1016\/j.aei.2026.104507_b0120","doi-asserted-by":"crossref","first-page":"32468","DOI":"10.1109\/JSEN.2024.3404072","article-title":"Spatio-Temporal Propagation: an Extended Message-Passing Graph Neural Network for remaining Useful Life Prediction","volume":"24","author":"Kong","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.aei.2026.104507_b0125","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1109\/TNNLS.2023.3330487","article-title":"Local\u2013Global Correlation Fusion-based Graph Neural Network for remaining Useful Life Prediction","volume":"36","author":"Wang","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.aei.2026.104507_b0130","first-page":"1","article-title":"Comprehensive Dynamic Structure Graph Neural Network for Aero-Engine remaining Useful Life Prediction","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104507_b0135","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.isatra.2025.05.010","article-title":"Aeroengine remaining useful life prediction via integrating enhanced inverted transformer and spatiotemporal graph learning","volume":"163","author":"Sun","year":"2025","journal-title":"ISA Trans."},{"key":"10.1016\/j.aei.2026.104507_b0140","series-title":"User\u2019s Guide for the Commercial Modular Aero-Propulsion System simulation","author":"Frederick","year":"2007"},{"key":"10.1016\/j.aei.2026.104507_b0145","first-page":"1114","article-title":"Exploratory Data Analysis of the N-CMAPSS Dataset for Prognostics, in","author":"Chatterjee","year":"2021","journal-title":"IEEE"},{"key":"10.1016\/j.aei.2026.104507_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.113308","article-title":"An interpretable multiscale graph wavelet neural network for aeroengine fault diagnosis under time-varying speeds","volume":"240","author":"Jia","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.aei.2026.104507_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103765","article-title":"Spatial-temporal adaptive causality graph-based fault root cause location method for time-varying industrial process","volume":"68","author":"Liu","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104507_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103119","article-title":"MGTN-DSI: a multi-sensor graph transfer network considering dual structural information for fault diagnosis under varying working conditions","volume":"65","author":"Liu","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104507_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2025.139306","article-title":"Multi-scale spatiotemporal feature-assisted physical information graph temporal convolutional network for aero-engine degradation trend prediction","volume":"340","author":"Feng","year":"2025","journal-title":"Energy"},{"key":"10.1016\/j.aei.2026.104507_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110162","article-title":"Nonlinear slow-varying dynamics-assisted temporal graph transformer network for remaining useful life prediction","volume":"248","author":"Gao","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104507_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111152","article-title":"An adaptive multi-scale spatial-temporal graph attention ensemble network with physical guidance for remaining useful life prediction of multi-sensor equipment","volume":"262","author":"Zhou","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104507_b0180","author":"Wang","year":"2021","journal-title":"Heterogeneous Graph Attention Network"},{"key":"10.1016\/j.aei.2026.104507_b0185","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1109\/TETCI.2024.3420692","article-title":"Efficient Message Passing Algorithm and Architecture Co-Design for Graph Neural Networks","volume":"9","author":"Zou","year":"2025","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"10.1016\/j.aei.2026.104507_b0190","author":"Bahuleyan","year":"2018","journal-title":"Variational Attention for Sequence-to-Sequence Models"},{"key":"10.1016\/j.aei.2026.104507_b0195","unstructured":"D.K. Frederick, J.A. DeCastro, J.S. Litt, User\u2019s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS), N. Y. (2007)."},{"key":"10.1016\/j.aei.2026.104507_b0200","doi-asserted-by":"crossref","first-page":"175","DOI":"10.54254\/2755-2721\/98\/2024FMCEAU0109","article-title":"Analysis of Principle and applications for Different Engines","volume":"98","author":"Wu","year":"2024","journal-title":"Appl. Comput. Eng."},{"key":"10.1016\/j.aei.2026.104507_b0205","unstructured":"J. Hubinka, B. Paradiso, C. Santner, H.P. Pirker, E. G\u00f6ttlich, DESIGN AND OPERATION OF A TWO SPOOL HIGH PRESSURE TEST TURBINE FACILITY, (n.d.)."},{"key":"10.1016\/j.aei.2026.104507_b0210","doi-asserted-by":"crossref","unstructured":"J.D. Mattingly, W.H. Heiser, D.T. Pratt, Aircraft engine design, second edition, American Institute of Aeronautics and Astronautics, Reston ,VA, 2002. DOI: 10.2514\/4.861444.","DOI":"10.2514\/4.861444"},{"key":"10.1016\/j.aei.2026.104507_b0215","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/j.ijhydene.2023.11.252","article-title":"Thermodynamic, economic, and environmental analysis of a hydrogen-powered turbofan engine at varying altitudes","volume":"55","author":"O\u011fur","year":"2024","journal-title":"Int. J. Hydrog. Energy"},{"key":"10.1016\/j.aei.2026.104507_b0220","doi-asserted-by":"crossref","unstructured":"J.W. Connolly, J. Csank, A. Chicatelli, Advanced Control Considerations for Turbofan Engine Design, in: 52nd AIAASAEASEE Jt. Propuls. Conf., American Institute of Aeronautics and Astronautics, Salt Lake City, UT, 2016. DOI: 10.2514\/6.2016-4653.","DOI":"10.2514\/6.2016-4653"},{"key":"10.1016\/j.aei.2026.104507_b0225","doi-asserted-by":"crossref","first-page":"6825","DOI":"10.1109\/JSEN.2024.3523176","article-title":"Multiscale Spatiotemporal attention Network for remaining Useful Life Prediction of Mechanical Systems","volume":"25","author":"Gao","year":"2025","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.aei.2026.104507_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110288","article-title":"An attention-based multi-scale temporal convolutional network for remaining useful life prediction","volume":"250","author":"Xu","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104507_b0235","first-page":"253","article-title":"Towards distribution Clustering-based Deep LSTM Models for RUL Prediction","author":"Sayah","year":"2020","journal-title":"IEEE"},{"key":"10.1016\/j.aei.2026.104507_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.fraope.2024.100083","article-title":"An aero-engine remaining useful life prediction model based on feature selection and the improved TCN","volume":"6","author":"Zha","year":"2024","journal-title":"Frankl. Open"},{"key":"10.1016\/j.aei.2026.104507_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109846","article-title":"An integrated feature extraction framework of linear multi-layer perceptron to reduce computation complexity for remaining useful life prediction","volume":"141","author":"Gao","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104507_b0250","first-page":"1","article-title":"Heterogeneous dynamic-aware GNN for RUL prediction of aeroengine","volume":"74","author":"Jin","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104507_b0255","first-page":"1","article-title":"A multi-scale pooling attention-based graph attention network for remaining useful life prediction","author":"Tang","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104507_b0260","first-page":"1","article-title":"Remaining Useful Life Prediction Method based on the Spatiotemporal Graph and GCN Nested Parallel Route Model","volume":"73","author":"Song","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104507_b0265","first-page":"1114","article-title":"IEEE Int. Conf. Ind. Eng. Eng","volume":"2021","author":"S. Chatterjee, A. Keprate, Exploratory Data Analysis of the N-CMAPSS Dataset for Prognostics, in","year":"2021","journal-title":"Manag. IEEM, IEEE, Singapore, Singapore"},{"key":"10.1016\/j.aei.2026.104507_b0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.ast.2025.110292","article-title":"Numerical study on the effects of different total temperature inlet distortions on the aerodynamic performance and stability of the centrifugal compressor","volume":"163","author":"Zhang","year":"2025","journal-title":"Aerosp. Sci. Technol."},{"key":"10.1016\/j.aei.2026.104507_b0275","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neucom.2022.02.032","article-title":"A hierarchical scheme for remaining useful life prediction with long short-term memory networks","volume":"487","author":"Song","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104507_b0280","first-page":"159","article-title":"GPT-based equipment remaining useful life prediction, in","author":"Wang","year":"2024","journal-title":"ACM"},{"key":"10.1016\/j.aei.2026.104507_b0285","first-page":"1","article-title":"Data-Driven RUL Prediction using Performance Metrics (Short Paper)","volume":"125","author":"Diaz-Gonzalez","year":"2024","journal-title":"Oasics"},{"key":"10.1016\/j.aei.2026.104507_b0290","unstructured":"L. Ren, H. Wang, T. Mo, A Lightweight Group Transformer-Based Time Series Reduction Network for Edge Intelligence and Its Application in Industrial RUL Prediction, IEEE Trans. NEURAL Netw. Learn. Syst. (n.d.)."},{"key":"10.1016\/j.aei.2026.104507_b0295","article-title":"Multi-head attention-based variational autoencoders ensemble for remaining useful life prediction of aero-engines","author":"Wang","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.aei.2026.104507_b0300","doi-asserted-by":"crossref","DOI":"10.1109\/TII.2023.3333933","article-title":"An Interpretable Neuro-Dynamic Scheme with Feature-Temporal attention for remaining Useful Life Estimation","volume":"20","author":"Qin","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.aei.2026.104507_b0305","article-title":"An interpretable RUL prediction method of aircraft engines under complex operating conditions using spatio-temporal features","author":"Gao","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.aei.2026.104507_b0310","doi-asserted-by":"crossref","DOI":"10.1109\/TNNLS.2023.3257038","article-title":"DLformer: a Dynamic Length Transformer-based Network for Efficient Feature Representation in remaining Useful Life Prediction","volume":"35","author":"Ren","year":"2024","journal-title":"IEEE Trans. NEURAL Netw. Learn. Syst."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626001990?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626001990?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T03:53:41Z","timestamp":1774065221000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626001990"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":62,"alternative-id":["S1474034626001990"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104507","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A heterogeneous graph neural network with spatial\u2013temporal and operating condition-aware message passing mechanism for RUL prediction of aero-engines","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104507","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":"104507"}}