{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:37:26Z","timestamp":1776328646026,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873014"],"award-info":[{"award-number":["61873014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L212033"],"award-info":[{"award-number":["L212033"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10845-023-02125-0","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T17:07:25Z","timestamp":1682442445000},"page":"1571-1583","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A framework and method for equipment digital twin dynamic evolution based on IExATCN"],"prefix":"10.1007","volume":"35","author":[{"given":"Kunyu","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1989-6102","authenticated-orcid":false,"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zidi","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Hongbo","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Han","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Jin","family":"Cui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,25]]},"reference":[{"issue":"11","key":"2125_CR1","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1080\/0951192X.2019.1686173","volume":"32","author":"P Aivaliotis","year":"2019","unstructured":"Aivaliotis, P., Georgoulias, K., & Chryssolouris, G. (2019). The use of digital twin for predictive maintenance in manufacturing. International Journal of Computer Integrated Manufacturing, 32(11), 1067\u20131080.","journal-title":"International Journal of Computer Integrated Manufacturing"},{"key":"2125_CR2","doi-asserted-by":"crossref","unstructured":"Arag\u00f3n, G., Puri, H., Grass, A., Chala, S., & Beecks, C. (2019). Incremental deep-learning for continuous load prediction in energy management systems. In 2019 IEEE Milan PowerTech (pp. 1\u20136). IEEE.","DOI":"10.1109\/PTC.2019.8810793"},{"key":"2125_CR3","unstructured":"Bai, S., Kolter, J.Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271."},{"key":"2125_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-74568-4","volume-title":"Handbook of dynamic data driven applications systems","author":"EP Blasch","year":"2022","unstructured":"Blasch, E. P., Darema, F., Ravela, S., & Aved, A. J. (2022). Handbook of dynamic data driven applications systems (Vol. 1). Springer."},{"key":"2125_CR5","doi-asserted-by":"publisher","first-page":"106612","DOI":"10.1016\/j.ymssp.2019.106612","volume":"140","author":"W Booyse","year":"2020","unstructured":"Booyse, W., Wilke, D. N., & Heyns, S. (2020). Deep digital twins for detection, diagnostics and prognostics. Mechanical Systems and Signal Processing, 140, 106612\u2013110661225.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2125_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2020.106410","volume":"243","author":"S Chakraborty","year":"2021","unstructured":"Chakraborty, S., & Adhikari, S. (2021). Machine learning based digital twin for dynamical systems with multiple time-scales. Computers & Structures, 243, 106410.","journal-title":"Computers & Structures"},{"key":"2125_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.107961","volume":"217","author":"MA Chao","year":"2022","unstructured":"Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2022). Fusing physics-based and deep learning models for prognostics. Reliability Engineering & System Safety, 217, 107961.","journal-title":"Reliability Engineering & System Safety"},{"key":"2125_CR8","unstructured":"Chen, H., Li, L., Shang, C., & Huang, B. (2022). Fault detection for nonlinear dynamic systems with consideration of modeling errors: A data-driven approach. IEEE Transactions on Cybernetics, 1\u201311."},{"key":"2125_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-37962-9","volume-title":"Data-driven modeling of cyber-physical systems using side-channel analysis","author":"SR Chhetri","year":"2020","unstructured":"Chhetri, S. R., & Al Faruque, M. A. (2020). Data-driven modeling of cyber-physical systems using side-channel analysis. Springer."},{"key":"2125_CR10","unstructured":"Duan, J.-G., Ma, T.-Y., Zhang, Q.-L., Liu, Z., & Qin, J.-Y. (2021). Design and application of digital twin system for the blade-rotor test rig. Journal of Intelligent Manufacturing, 1\u201317."},{"issue":"02","key":"2125_CR11","doi-asserted-by":"publisher","first-page":"1850009","DOI":"10.1142\/S1793962318500095","volume":"9","author":"C Ge","year":"2018","unstructured":"Ge, C., Zhu, Y., & Di, Y. (2018). Equipment remaining useful life prediction oriented symbiotic simulation driven by real-time degradation data. International Journal of Modeling, Simulation, and Scientific Computing, 9(02), 1850009.","journal-title":"International Journal of Modeling, Simulation, and Scientific Computing"},{"key":"2125_CR12","doi-asserted-by":"crossref","unstructured":"Grieves, M. W. (2019). Virtually intelligent product systems: Digital and physical twins. In Complex systems engineering: Theory and practice (pp. 175\u2013200). AIAA.","DOI":"10.2514\/5.9781624105654.0175.0200"},{"key":"2125_CR13","doi-asserted-by":"crossref","unstructured":"Guo, M.-H., Liu, Z.-N., Mu, T.-J., & Hu, S.-M. (2021). Beyond self-attention: External attention using two linear layers for visual tasks. arXiv preprint arXiv:2105.02358.","DOI":"10.1109\/TPAMI.2022.3211006"},{"key":"2125_CR14","doi-asserted-by":"crossref","unstructured":"Kosova, F., Unver, H.O. (2022). A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.","DOI":"10.1177\/09544062221132697"},{"issue":"2","key":"2125_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2020.101209","volume":"47","author":"TY Lin","year":"2021","unstructured":"Lin, T. Y., Jia, Z., Yang, C., Xiao, Y., Lan, S., Shi, G., Zeng, B., & Li, H. (2021). Evolutionary digital twin: A new approach for intelligent industrial product development. Advanced Engineering Informatics, 47(2), 101209.","journal-title":"Advanced Engineering Informatics"},{"key":"2125_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2020.101974","volume":"65","author":"W Luo","year":"2020","unstructured":"Luo, W., Hu, T., Ye, Y., Zhang, C., & Wei, Y. (2020). A hybrid predictive maintenance approach for cnc machine tool driven by digital twin. Robotics and Computer-Integrated Manufacturing, 65, 101974.","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"7","key":"2125_CR17","doi-asserted-by":"publisher","first-page":"1899","DOI":"10.1007\/s10845-020-01724-5","volume":"32","author":"K Mykoniatis","year":"2021","unstructured":"Mykoniatis, K., & Harris, G. A. (2021). A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach. Journal of Intelligent Manufacturing, 32(7), 1899\u20131911.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2125_CR18","doi-asserted-by":"publisher","first-page":"662","DOI":"10.7717\/peerj-cs.662","volume":"7","author":"P Narkhede","year":"2021","unstructured":"Narkhede, P., Walambe, R., Poddar, S., & Kotecha, K. (2021). Incremental learning of lstm framework for sensor fusion in attitude estimation. PeerJ Computer Science, 7, 662.","journal-title":"PeerJ Computer Science"},{"issue":"4","key":"2125_CR19","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1007\/s10845-020-01685-9","volume":"32","author":"E Negri","year":"2021","unstructured":"Negri, E., Pandhare, V., Cattaneo, L., Singh, J., Macchi, M., & Lee, J. (2021). Field-synchronized digital twin framework for production scheduling with uncertainty. Journal of Intelligent Manufacturing, 32(4), 1207\u20131228.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2125_CR20","doi-asserted-by":"crossref","unstructured":"Pang, T. Y., Pelaez\u00a0Restrepo, J. D., Cheng, C.-T., Yasin, A., Lim, H., & Miletic, M. (2021). Developing a digital twin and digital thread framework for an \u2018industry 4.0\u2019 shipyard. Applied Sciences, 11(3), 1097.","DOI":"10.3390\/app11031097"},{"key":"2125_CR21","doi-asserted-by":"crossref","unstructured":"Pawar, S., Ahmed, S. E., San, O., & Rasheed, A. (2021). Hybrid analysis and modeling for next generation of digital twins. Journal of Physics: Conference Series, 2018.","DOI":"10.1088\/1742-6596\/2018\/1\/012031"},{"key":"2125_CR22","doi-asserted-by":"crossref","unstructured":"Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems,48, 71\u201377.","DOI":"10.1016\/j.jmsy.2018.04.008"},{"issue":"1","key":"2125_CR23","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1109\/TETC.2022.3143346","volume":"10","author":"Z Ren","year":"2022","unstructured":"Ren, Z., Wan, J., & Deng, P. (2022). Machine-learning-driven digital twin for lifecycle management of complex equipment. IEEE Transactions on Emerging Topics in Computing, 10(1), 9\u201322.","journal-title":"IEEE Transactions on Emerging Topics in Computing"},{"key":"2125_CR24","unstructured":"Saha, B., & Goebel, K. (2009). Modeling li-ion battery capacity depletion in a particle filtering framework. Annual Conference of the PHM Society (Vol. 1)."},{"key":"2125_CR25","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.ast.2019.04.012","volume":"89","author":"G-G Seo","year":"2019","unstructured":"Seo, G.-G., Kim, Y., & Saderla, S. (2019). Kalman-filter based online system identification of fixed-wing aircraft in upset condition. Aerospace Science and Technology, 89, 307\u2013317.","journal-title":"Aerospace Science and Technology"},{"issue":"2012","key":"2125_CR26","first-page":"1","volume":"32","author":"M Shafto","year":"2012","unstructured":"Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., & Wang, L. (2012). Modeling, simulation, information technology & processing roadmap. National Aeronautics and Space Administration, 32(2012), 1\u201338.","journal-title":"National Aeronautics and Space Administration"},{"key":"2125_CR27","doi-asserted-by":"crossref","unstructured":"Song, J. W., Park, Y. I., Hong, J.-J., Kim, S.-G., & Kang, S.-J. (2021). Attention-based bidirectional lstm-cnn model for remaining useful life estimation. In 2021 IEEE international symposium on circuits and systems (ISCAS) (pp. 1\u20135). IEEE.","DOI":"10.1109\/ISCAS51556.2021.9401572"},{"issue":"12","key":"2125_CR28","doi-asserted-by":"publisher","first-page":"9594","DOI":"10.1109\/JIOT.2020.3004452","volume":"8","author":"Y Song","year":"2020","unstructured":"Song, Y., Gao, S., Li, Y., Jia, L., Li, Q., & Pang, F. (2020). Distributed attention-based temporal convolutional network for remaining useful life prediction. IEEE Internet of Things Journal, 8(12), 9594\u20139602.","journal-title":"IEEE Internet of Things Journal"},{"issue":"4","key":"2125_CR29","doi-asserted-by":"publisher","first-page":"1534","DOI":"10.1002\/rnc.4837","volume":"30","author":"K Wang","year":"2020","unstructured":"Wang, K., Tian, E., Liu, J., Wei, L., & Yue, D. (2020). Resilient control of networked control systems under deception attacks: a memory-event-triggered communication scheme. International Journal of Robust and Nonlinear Control, 30(4), 1534\u20131548.","journal-title":"International Journal of Robust and Nonlinear Control"},{"issue":"3","key":"2125_CR30","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1007\/s00170-021-06882-1","volume":"114","author":"L Wang","year":"2021","unstructured":"Wang, L., Liu, Z., Liu, A., & Tao, F. (2021). Artificial intelligence in product lifecycle management. The International Journal of Advanced Manufacturing Technology, 114(3), 771\u2013796.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2125_CR31","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.neucom.2017.02.104","volume":"277","author":"PK Wong","year":"2018","unstructured":"Wong, P. K., Gao, X. H., Wong, K. I., & Vong, C. M. (2018). Online extreme learning machine based modeling and optimization for point-by-point engine calibration. Neurocomputing, 277, 187\u2013197.","journal-title":"Neurocomputing"},{"issue":"1","key":"2125_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40323-020-00147-4","volume":"7","author":"L Wright","year":"2020","unstructured":"Wright, L., & Davidson, S. (2020). How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences, 7(1), 1\u201313.","journal-title":"Advanced Modeling and Simulation in Engineering Sciences"},{"key":"2125_CR33","doi-asserted-by":"crossref","unstructured":"Wunderlich, A., Booth, K., & Santi, E. (2021). Hybrid analytical and data-driven modeling techniques for digital twin applications. In 2021 IEEE Electric Ship Technologies Symposium (ESTS) (pp. 1\u20137). IEEE.","DOI":"10.1109\/ESTS49166.2021.9512364"},{"key":"2125_CR34","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neucom.2019.09.074","volume":"376","author":"Z Xue","year":"2020","unstructured":"Xue, Z., Zhang, Y., Cheng, C., & Ma, G. (2020). Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression. Neurocomputing, 376, 95\u2013102.","journal-title":"Neurocomputing"},{"key":"2125_CR35","doi-asserted-by":"crossref","unstructured":"Yu, Y., Hu, C., Si, X., Zheng, J., & Zhang, J. (2020). Averaged bi-lstm networks for rul prognostics with non-life-cycle labeled dataset. Neurocomputing, 402, 134\u2013147.","DOI":"10.1016\/j.neucom.2020.03.041"},{"issue":"03","key":"2125_CR36","doi-asserted-by":"publisher","first-page":"1950011","DOI":"10.1142\/S1793962319500119","volume":"10","author":"L Zhang","year":"2019","unstructured":"Zhang, L., Huang, C., Wang, L., Zhao, E., & Gao, W. (2019). Data-driven modeling and simulation of complex multistation manufacturing process for dimensional variation analysis. International Journal of Modeling, Simulation, and Scientific Computing, 10(03), 1950011.","journal-title":"International Journal of Modeling, Simulation, and Scientific Computing"},{"key":"2125_CR37","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.jmsy.2021.02.009","volume":"59","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Zhou, L., & Horn, B. K. (2021). Building a right digital twin with model engineering. Journal of Manufacturing Systems, 59, 151\u2013164.","journal-title":"Journal of Manufacturing Systems"},{"issue":"7","key":"2125_CR38","doi-asserted-by":"publisher","first-page":"5695","DOI":"10.1109\/TVT.2018.2805189","volume":"67","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018). Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology, 67(7), 5695\u20135705.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"2125_CR39","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Liu, Y., Feng, J., Guo, J., & Zhang, L. (2022). A framework for development of digital twin industrial robot production lines based on a mechatronics approach. International Journal of Modeling, Simulation, and Scientific Computing, 2341025.","DOI":"10.1142\/S1793962323410258"},{"issue":"3","key":"2125_CR40","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1007\/s12652-018-0911-3","volume":"10","author":"Y Zheng","year":"2019","unstructured":"Zheng, Y., Yang, S., & Cheng, H. (2019). An application framework of digital twin and its case study. Journal of Ambient Intelligence and Humanized Computing, 10(3), 1141\u20131153.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"2125_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113446","volume":"373","author":"T Zohdi","year":"2021","unstructured":"Zohdi, T. (2021). A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety. Computer Methods in Applied Mechanics and Engineering, 373, 113446.","journal-title":"Computer Methods in Applied Mechanics and Engineering"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02125-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02125-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02125-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T22:05:15Z","timestamp":1711404315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02125-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,25]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2125"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02125-0","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,25]]},"assertion":[{"value":"30 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2023","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}