The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

The WEKA data mining software: an update

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750

TensorFlow: A system for large-scale machine learningPreprint

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Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077

Identity mappings in deep residual networks

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Playing Atari with Deep Reinforcement Learning

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471

An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression

Altman, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,657

Fuzzy logic in control systems: fuzzy logic controller. I

Lee, C. C. (1990) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.52551 · Cited by 5,630

Extreme Learning Machine for Regression and Multiclass Classification

Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,575

Systematic Literature Reviews in Software Engineering—A Systematic Literature Review

Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537

Digital Twin in Industry: State-of-the-Art

Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391

Beyond the hype: Big data concepts, methods, and analytics

Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226

Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems

Grieves, M., & Vickers, J. (2017) · Transdisciplinary Perspectives on Complex Systems · doi:10.1007/978-3-319-38756-7_4 · Cited by 3,727

Deep Reinforcement Learning with Double Q-Learning

Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318

Prioritized Experience Replay

Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv · doi:10.48550/arxiv.1511.05952 · Cited by 3,120

Digital twin-driven product design, manufacturing and service with big data

Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920

Hadoop: The Definitive Guide

White, T. (2009) · Cited by 2,800

The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles

Glaessgen, E., & Stargel, D. (2012) · 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA · doi:10.2514/6.2012-1818 · Cited by 2,404 · cited 2×

Digital twin: values, challenges and enablers from a modeling perspective

Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952

A Guide to Conducting a Systematic Literature Review of Information Systems Research

Okoli, C., & Schabram, K. (2011) · doi:10.2139/ssrn.1954824 · Cited by 1,849

DIGITAL TWIN: MANUFACTURING EXCELLENCE THROUGH VIRTUAL FACTORY REPLICATION

Prof. RajratnaKharat*1, V. B. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1493929 · Cited by 1,768

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Lu, Y., Liu, C., Wang, K. I., Huang, H., & Xu, X. W. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101837 · Cited by 1,753

Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison

Qi, Q., & Tao, F. (2018) · IEEE Access · doi:10.1109/access.2018.2793265 · Cited by 1,716

Hadoop: The Definitive Guide

Keulen, D. S. (2014) · Cited by 1,663

About The Importance of Autonomy and Digital Twins for the Future of Manufacturing

Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.141 · Cited by 1,474 · cited 1×

Extreme Learning Machine for Multilayer Perceptron

Tang, J., Deng, C., & Huang, G.-B. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2424995 · Cited by 1,469

Fuzzy logic in control systems: fuzzy logic controller. II

Lee, C. C. (1990) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.52552 · Cited by 1,317

Digital twin-driven product design framework

Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C.-Y., & Nee, A. Y. C. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1443229 · Cited by 1,290

Reengineering aircraft structural life prediction using a digital twin

Tuegel, E. J., Ingraffea, A. R., Eason, T. G., & Spottswood, S. M. (2011) · International Journal of Aerospace Engineering · doi:10.1155/2011/154798 · Cited by 1,119

Benchmarking Deep Reinforcement Learning for Continuous Control

Duan, Y., Chen, X., Houthooft, R., Schulman, J., & Abbeel, P. (2016) · arXiv · doi:10.48550/arxiv.1604.06778 · Cited by 1,114

Digital twin driven prognostics and health management for complex equipment

Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018) · CIRP Annals · doi:10.1016/j.cirp.2018.04.055 · Cited by 1,000

Review of digital twin applications in manufacturing

Cimino, C., Negri, E., & Fumagalli, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.103130 · Cited by 874

A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin

Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L., Wang, F., Liu, R., Pang, Z., & Deen, M. J. (2019) · IEEE Access · doi:10.1109/access.2019.2909828 · Cited by 763

C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems

Alam, K. M., & El Saddik, A. (2017) · IEEE Access · doi:10.1109/access.2017.2657006 · Cited by 741

Digital twin-based smart production management and control framework for the complex product assembly shop-floor

Zhuang, C., Liu, J., & Xiong, H. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-018-1617-6 · Cited by 700

Digital Twin: Vision, Benefits, Boundaries, and Creation for Buildings

Khajavi, S. H., Motlagh, N. H., Jaribion, A., Werner, L. C., & Holmström, J. (2019) · IEEE Access · doi:10.1109/access.2019.2946515 · Cited by 677

Failure diagnosis using deep belief learning based health state classification

Tamilselvan, P., & Wang, P. (2013) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2013.02.022 · Cited by 631

Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation

Li, W., Kwiecien, M., Badeda, J., Jöst, D., Schulte, D., & Sauer, D. U. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101557 · Cited by 590

Digital twins to personalize medicine

on behalf of the Swedish Digital Twin Consortium, Björnsson, B., Borrebaeck, C., Elander, N., Gasslander, T., Gawel, D. R., Gustafsson, M., Jörnsten, R., Lee, E. J., Li, X., et al. (2020) · Genome Medicine · doi:10.1186/s13073-019-0701-3 · Cited by 566

Toward a Digital Twin for real-time geometry assurance in individualized production

Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.04.038 · Cited by 543

Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis

Yin, S., Zhu, X., & Kaynak, O. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2345331 · Cited by 538

An application framework of digital twin and its case study

Zheng, Y., Yang, S., & Cheng, H. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0911-3 · Cited by 534

A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning

Xu, Y., Sun, Y., Liu, X., & Zheng, Y. (2019) · IEEE Access · doi:10.1109/access.2018.2890566 · Cited by 533

A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin

Luo, W., Hu, T., Ye, Y., Zhang, C., & Wei, Y. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.101974 · Cited by 525

Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0

Schluse, M., Priggemeyer, M., Atorf, L., & Roßmann, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2804917 · Cited by 524

A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products

Zhang, Y., Ren, S., Liu, Y., & Si, S. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.07.123 · Cited by 514

A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems

Jain, P., Poon, J., Singh, J. P., Spanos, C. J., Sanders, S. R., & Panda, S. K. (2019) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2019.2911594 · Cited by 464

Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management

Fan, C. H., Zhang, C., Yahja, A., & Mostafavi, A. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.102049 · Cited by 450

A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence

Xia, K., Sacco, C., Kirkpatrick, M. B., Saidy, C., Nguyen, L., Kircaliali, A., & Harik, R. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.012 · Cited by 444

Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry

Min, Q., Lu, Y., Liu, Z., Su, C., & Wang, B. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.05.020 · Cited by 436

The Digital Twin of the City of Zurich for Urban Planning

Schrotter, G., & Hürzeler, C. (2020) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-020-00092-2 · Cited by 434

Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges

Wanasinghe, T. R., Wroblewski, L., Petersen, B. K., Gosine, R. G., James, L. A., Silva, O. D., Mann, G. K. I., & Warrian, P. J. (2020) · IEEE Access · doi:10.1109/access.2020.2998723 · Cited by 411

CNTK: Microsoft's Open-Source Deep-Learning Toolkit

Seide, F., & Agarwal, A. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2945397 · Cited by 382

Virtually Intelligent Product Systems: Digital and Physical Twins

Grieves, M. W. (2019) · Complex Systems Engineering: Theory and Practice · doi:10.2514/5.9781624105654.0175.0200 · Cited by 356

Digital twin for CNC machine tool: modeling and using strategy

Luo, W., Hu, T., Zhang, C., & Wei, Y. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0946-5 · Cited by 350

Digital Twin Enhanced Dynamic Job-Shop Scheduling

Zhang, M., Tao, F., & Nee, A. Y. C. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.04.008 · Cited by 348

Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes

Yin, Z., & Hou, J. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.09.081 · Cited by 346

The digital twin of an industrial production line within the industry 4.0 concept

Vachalek, J., Bartalsky, L., Rovny, O., Sismisova, D., Morhac, M., & Loksik, M. (2017) · 2017 21st International Conference on Process Control (PC) · doi:10.1109/pc.2017.7976223 · Cited by 339

Digital twin modeling method based on biomimicry for machining aerospace components

Liu, S., Bao, J., Lu, Y., Li, J., Lu, S., & Sun, X. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.04.014 · Cited by 310

Deep learning for hybrid 5G services in mobile edge computing systems: learn from a digital twin

Dong, R., She, C., Hardjawana, W., Li, Y., & Vucetic, B. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2927312 · Cited by 295

Deep digital twins for detection, diagnostics and prognostics

Booyse, W., Wilke, D. N., & Heyns, S. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106612 · Cited by 292

Digital Twin and Web-Based Virtual Gaming Technologies for Online Education: A Case of Construction Management and Engineering

Sepasgozar, S. M. E. (2020) · Applied Sciences · doi:10.3390/app10134678 · Cited by 286

Digital-Twin-Based Job Shop Scheduling Toward Smart Manufacturing

Fang, Y., Peng, C., Lou, P., Zhou, Z., Hu, J., & Yan, J. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2938572 · Cited by 281

Analysis of Recurrent Neural Networks for Probabilistic Modeling of Driver Behavior

Morton, J., Wheeler, T. A., & Kochenderfer, M. J. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2603007 · Cited by 273

Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction

Chen, Y. Q., Fink, O., & Sansavini, G. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2721922 · Cited by 260

Human Digital Twin for Fitness Management

Barricelli, B. R., Casiraghi, E., Gliozzo, J., Petrini, A., & Valtolina, S. (2020) · IEEE Access · doi:10.1109/access.2020.2971576 · Cited by 259 · cited 2×

A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems

Gong, D., Sun, J., & Miao, Z. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2634625 · Cited by 259

Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing

Lee, J., Azamfar, M., Singh, J., & Siahpour, S. (2020) · IET Collaborative Intelligent Manufacturing · doi:10.1049/iet-cim.2020.0009 · Cited by 256

Data-driven ship digital twin for estimating the speed loss caused by the marine fouling

Coraddu, A., Oneto, L., Baldi, F., Cipollini, F., Atlar, M., & Savio, S. (2019) · Ocean Engineering · doi:10.1016/j.oceaneng.2019.05.045 · Cited by 252

Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems

Liu, C., Le Roux, L., Körner, C., Tabaste, O., Lacan, F., & Bigot, S. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.05.010 · Cited by 252

Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

Qi, Y., Shen, C., Wang, D., Shi, J., Jiang, X., & Zhu, Z. (2017) · IEEE Access · doi:10.1109/access.2017.2728010 · Cited by 240

Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models

Kapteyn, M. G., Knezevic, D. J., Huynh, D. B. P., Tran, M., & Willcox, K. E. (2020) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.6423 · Cited by 237

A big data driven analytical framework for energy-intensive manufacturing industries

Zhang, Y., Ma, S., Yang, H., Lv, J., & Liu, Y. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.06.170 · Cited by 225

MES-integrated digital twin frameworks

Negri, E., Berardi, S., Fumagalli, L., & Macchi, M. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.05.007 · Cited by 213

Intelligent Digital Twin-Based Software-Defined Vehicular Networks

Zhao, L., Han, G., Li, Z., & Shu, L. (2020) · IEEE Network · doi:10.1109/mnet.011.1900587 · Cited by 208

Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?

Shah, S. A., Şeker, D. Z., Rathore, M. M., Hameed, S., Yahia, S. B., & Draheim, D. (2019) · IEEE Access · doi:10.1109/access.2019.2928233 · Cited by 204

Early Fault Detection Approach With Deep Architectures

Lu, W., Li, Y., Cheng, Y., Meng, D., Liang, B., & Zhou, P. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2800978 · Cited by 201

Real-time machining data application and service based on IMT digital twin

Tong, X., Liu, Q., Pi, S., & Xiao, Y. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01500-0 · Cited by 192

Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

Chen, X., Wu, C., Chen, T., Zhang, H., Liu, Z., Zhang, Y., & Bennis, M. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2963667 · Cited by 191

Health monitoring and prognosis of electric vehicle motor using intelligent‐digital twin

Venkatesan, S., Manickavasagam, K., Tengenkai, N., & Vijayalakshmi, N. (2019) · IET Electric Power Applications · doi:10.1049/iet-epa.2018.5732 · Cited by 185

An ISO/IEEE 11073 Standardized Digital Twin Framework for Health and Well-Being in Smart Cities

Laamarti, F., Badawi, H. F., Ding, Y., Arafsha, F., Hafidh, B., & Saddik, A. E. (2020) · IEEE Access · doi:10.1109/access.2020.2999871 · Cited by 171

Cardio Twin: A Digital Twin of the human heart running on the edge

Velazquez, R., Díaz, R. G., & Saddik, A. E. (2019) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2019.8802162 · Cited by 170

Design and Development of Digital Twins: a Case Study in Supply Chains

Marmolejo-Saucedo, J. A. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-020-01557-9 · Cited by 166

Digital twin for the structural health management of reusable spacecraft: A case study

Ye, Y., Yang, Q., Yang, F., Huo, Y., & Meng, S. (2020) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2020.107076 · Cited by 144

A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance

Kumar, S., Madhumathi, R., Chelliah, P. R., Tao, L., & Wang, S. (2018) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-018-0069-y · Cited by 141

Feature Trend Extraction and Adaptive Density Peaks Search for Intelligent Fault Diagnosis of Machines

Wang, Y., Wei, Z., & Yang, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2810226 · Cited by 137

Digital twin and its application to power grid online analysis

(2019) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2018.01460 · Cited by 135

Digital twin-based process reuse and evaluation approach for smart process planning

Liu, J., Zhou, H., Tian, G., Liu, X., & Jing, X. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-018-2748-5 · Cited by 131

Big data driven Hierarchical Digital Twin Predictive Remanufacturing paradigm: Architecture, control mechanism, application scenario and benefits

Wang, Y., Wang, S., Yang, B., Zhu, L., & Liu, F. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119299 · Cited by 131

Digital twin-driven cyber-physical production system towards smart shop-floor

Zhang, H., Zhang, G., & Yan, Q. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-1125-4 · Cited by 126

A proactive material handling method for CPS enabled shop-floor

Wang, W., Zhang, Y., & Zhong, R. Y. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101849 · Cited by 124

A Digital Twin Paradigm: Vehicle-to-Cloud Based Advanced Driver Assistance Systems

Wang, Z.-R., Liao, X.-S., Zhao, X.-P., Han, K., Tiwari, P., Barth, M., & Wu, G.-Y. (2020) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2020-spring48590.2020.9128938 · Cited by 124

Deep learning-enabled intelligent process planning for digital twin manufacturing cell

Zhang, C., Zhou, G., Jun-sheng, H., & Li, J. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105247 · Cited by 114

Digital-twin-driven geometric optimization of centrifugal impeller with free-form blades for five-axis flank milling

Zhou, Y., Xing, T., Song, Y., Li, Y., Zhu, X., Guo, L., & Ding, S. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.019 · Cited by 108

Cyber-physical systems for temporary structure monitoring

Yuan, X., Anumba, C., & Parfitt, M. K. (2016) · Automation in Construction · doi:10.1016/j.autcon.2016.02.005 · Cited by 108

Digital geoTwin Vienna: Towards a Digital Twin City as Geodata Hub

Lehner, H., & Dorffner, L. (2020) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-020-00101-4 · Cited by 107

Information modeling for cyber-physical production system based on digital twin and AutomationML

Zhang, H.-J., Yan, Q., & Wen, Z.-T. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-05056-9 · Cited by 98

Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins

Döllner, J. (2020) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-020-00102-3 · Cited by 95

A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method

Chakshu, N. K., Carson, J., SAZONOV, I. A., & Nithiarasu, P. (2019) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.3180 · Cited by 92

Beyond digital twins – A commentary

Tomko, M., & Winter, S. (2018) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/2399808318816992 · Cited by 91

Unleashing the Digital Twin's Potential for ICS Security

Dietz, M., & Pernul, G. (2020) · IEEE Security & Privacy · doi:10.1109/msec.2019.2961650 · Cited by 81

Business Models for Industrial Smart Services – The Example of a Digital Twin for a Product-Service-System for Potato Harvesting

Kampker, A., Stich, V., Jussen, P., Moser, B., & Kuntz, J. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.04.114 · Cited by 78

Multisource Model-Driven Digital Twin System of Robotic Assembly

Li, X., He, B., Zhou, Y., & Li, G. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2958874 · Cited by 78

A digital twin feasibility study (Part II): Non-deterministic predictions of fatigue life using in-situ diagnostics and prognostics

Leser, P. E., Warner, J. E., Leser, W. P., Bomarito, G., Newman, J. A., & Hochhalter, J. D. (2020) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2020.106903 · Cited by 77

Self-Diagnosis Technique for Virtual Private Networks Combining Bayesian Networks and Case-Based Reasoning

Bennacer, L., Amirat, Y., Chibani, A., Mellouk, A., & Ciavaglia, L. (2014) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2014.2321011 · Cited by 74

The application of machine learning for the prognostics and health management of control element drive system

Oluwasegun, A., & Jung, J.-C. (2020) · Nuclear Engineering and Technology · doi:10.1016/j.net.2020.03.028 · Cited by 73

Data Super-Network Fault Prediction Model and Maintenance Strategy for Mechanical Product Based on Digital Twin

Liu, Z., Chen, W., Zhang, C., Yang, C., & Chu, H. (2019) · IEEE Access · doi:10.1109/access.2019.2957202 · Cited by 72

Composition and Application of Power System Digital Twins Based on Ontological Modeling

Андрюшкевич, С. К., Kovalyov, S. P., & Nefedov, E. I. (2019) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin41052.2019.8972267 · Cited by 71

Digital Twin for Metasurface Reflector Management in 6G Terahertz Communications

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Synthetic PPG generation from haemodynamic model with baroreflex autoregulation: a Digital twin of cardiovascular system

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