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

Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its significance and research potential with new opportunities and unique challenges. To date, a number of scientific models have been designed and implemented related to this evolving topic. However, there is no systematic review of digital twinning, particularly focusing on the role of AI-ML and big data, to guide the academia and industry towards future developments. Therefore, this article emphasizes the role of big data and AI-ML in the creation of digital twins (DTs) or DT-based systems for various industrial applications, by highlighting the current state-of-the-art deployments. We performed a systematic review on top of multidisciplinary electronic bibliographic databases, in addition to existing patents in the field. Also, we identified development-tools that can facilitate various levels of the digital twinning. Further, we designed a big data driven and AI-enriched reference architecture that leads developers to a complete DT-enabled system. Finally, we highlighted the research potential of AI-ML for digital twinning by unveiling challenges and current opportunities.

[1]  Abdulmotaleb El Saddik,et al.  An ISO/IEEE 11073 Standardized Digital Twin Framework for Health and Well-Being in Smart Cities , 2020, IEEE Access.

[2]  Andrew Y. C. Nee,et al.  Digital twin driven prognostics and health management for complex equipment , 2018 .

[3]  Marietheres Dietz,et al.  Unleashing the Digital Twin's Potential for ICS Security , 2020, IEEE Security & Privacy.

[4]  Marco Macchi,et al.  MES-integrated digital twin frameworks , 2020 .

[5]  S. Michael Spottswood,et al.  Reengineering Aircraft Structural Life Prediction Using a Digital Twin , 2011 .

[6]  Guoyuan Wu,et al.  A Digital Twin Paradigm: Vehicle-to-Cloud Based Advanced Driver Assistance Systems , 2020, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[7]  Jinsong Bao,et al.  Digital twin modeling method based on biomimicry for machining aerospace components , 2020 .

[8]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[9]  Mykel J. Kochenderfer,et al.  Analysis of Recurrent Neural Networks for Probabilistic Modeling of Driver Behavior , 2017, IEEE Transactions on Intelligent Transportation Systems.

[10]  D. B. P. Huynh,et al.  Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models , 2020, International Journal for Numerical Methods in Engineering.

[11]  Mika Gustafsson,et al.  Digital twins to personalize medicine , 2019, Genome Medicine.

[12]  Bernard Butler,et al.  Digital Twin for Metasurface Reflector Management in 6G Terahertz Communications , 2020, IEEE Access.

[13]  Jian Hou,et al.  Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes , 2016, Neurocomputing.

[14]  Jürgen Döllner,et al.  Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins , 2020, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.

[15]  Michael W. Grieves.  Virtually Intelligent Product Systems: Digital and Physical Twins , 2019, Complex Systems Engineering: Theory and Practice.

[16]  Yu Zheng,et al.  An application framework of digital twin and its case study , 2018, Journal of Ambient Intelligence and Humanized Computing.

[17]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[18]  Pingfeng Wang,et al.  Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..

[19]  Siavash H. Khajavi,et al.  Digital Twin: Vision, Benefits, Boundaries, and Creation for Buildings , 2019, IEEE Access.

[20]  Daniel N. Wilke,et al.  Deep digital twins for detection, diagnostics and prognostics , 2020 .

[21]  Awais Ahmad,et al.  Exploiting encrypted and tunneled multimedia calls in high-speed big data environment , 2017, Multimedia Tools and Applications.

[22]  Qiang Liu,et al.  Real-time machining data application and service based on IMT digital twin , 2019, Journal of Intelligent Manufacturing.

[23]  Barbara Rita Barricelli,et al.  Human Digital Twin for Fitness Management , 2020, IEEE Access.

[24]  Chitu Okoli,et al.  A Guide to Conducting a Systematic Literature Review of Information Systems Research , 2010 .

[25]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[26]  Abdulmotaleb El Saddik,et al.  C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems , 2017, IEEE Access.

[27]  Yongli Wei,et al.  Digital twin for CNC machine tool: modeling and using strategy , 2018, Journal of Ambient Intelligence and Humanized Computing.

[28]  Bin He,et al.  Multisource Model-Driven Digital Twin System of Robotic Assembly , 2021, IEEE Systems Journal.

[29]  Suchitra Venkatesan,et al.  Health monitoring and prognosis of electric vehicle motor using intelligent‐digital twin , 2019, IET Electric Power Applications.

[30]  Okyay Kaynak,et al.  Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[31]  Ray Y. Zhong,et al.  A proactive material handling method for CPS enabled shop-floor , 2020, Robotics Comput. Integr. Manuf..

[32]  Sun Guoxi,et al.  A New Incipient Fault Diagnosis Method Combining Improved RLS and LMD Algorithm for Rolling Bearings With Strong Background Noise , 2018, IEEE Access.

[33]  Jianfeng Yan,et al.  Digital twin and its application to power grid online analysis , 2019, CSEE Journal of Power and Energy Systems.

[34]  Chao Liu,et al.  Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems , 2020, Journal of Manufacturing Systems.

[35]  Cheng Zhang,et al.  Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management , 2021, Int. J. Inf. Manag..

[36]  Wei Cao,et al.  Smart steel bridge construction enabled by BIM and Internet of Things in industry 4.0: A framework , 2018, 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC).

[37]  N. Jones,et al.  Top 10 Strategic Technology Trends for 2019: A Gartner Trend Insight Report , 2018 .

[38]  Ján Vachálek,et al.  The digital twin of an industrial production line within the industry 4.0 concept , 2017, 2017 21st International Conference on Process Control (PC).

[39]  Kiseon Kim,et al.  Damage detection of bottom-set gillnet using Artificial Neural Network , 2020 .

[40]  Jay Lee,et al.  Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing , 2020 .

[41]  Pearl Brereton,et al.  Systematic literature reviews in software engineering - A systematic literature review , 2009, Inf. Softw. Technol..

[42]  Junwei Yan,et al.  Digital-Twin-Based Job Shop Scheduling Toward Smart Manufacturing , 2019, IEEE Transactions on Industrial Informatics.

[43]  Yan Xu,et al.  A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning , 2019, IEEE Access.

[44]  Dirk Uwe Sauer,et al.  Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation , 2020 .

[45]  Awais Ahmad,et al.  Real-time video processing for traffic control in smart city using Hadoop ecosystem with GPUs , 2017, Soft Computing.

[46]  Chuen-Chien Lee,et al.  Fuzzy logic in control systems: fuzzy logic controller. I , 1990, IEEE Trans. Syst. Man Cybern..

[47]  Qiong Yan,et al.  Information modeling for cyber-physical production system based on digital twin and AutomationML , 2020, The International Journal of Advanced Manufacturing Technology.

[48]  Zhifeng Liu,et al.  A Product Quality Monitor Model With the Digital Twin Model and the Stacked Auto Encoder , 2020, IEEE Access.

[49]  Dirk Westermann,et al.  Parameter tuning for dynamic digital twins in inverter‐dominated distribution grid , 2019, IET Renewable Power Generation.

[50]  Volker Stich,et al.  Business Models for Industrial Smart Services – The Example of a Digital Twin for a Product-Service-System for Potato Harvesting , 2019, Procedia CIRP.

[51]  He Zhang,et al.  Digital Twin in Industry: State-of-the-Art , 2019, IEEE Transactions on Industrial Informatics.

[52]  Thomas Kuhn,et al.  (Do Not) Trust in Ecosystems , 2019, 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER).

[53]  Cutting tool data representation and exchange , .

[54]  Shangguang Wang,et al.  A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance , 2018, Journal of Reliable Intelligent Environments.

[55]  Fei Tao,et al.  Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison , 2018, IEEE Access.

[56]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[57]  Chimay J. Anumba,et al.  Cyber-physical systems for temporary structure monitoring , 2016 .

[58]  Andrew Y. C. Nee,et al.  Digital twin-driven product design framework , 2019, Int. J. Prod. Res..

[59]  Ralph Langner,et al.  To Kill a Centrifuge A Technical Analysis of What Stuxnet ’ s Creators Tried to Achieve , 2013 .

[60]  Elisa Negri,et al.  Review of digital twin applications in manufacturing , 2019, Comput. Ind..

[61]  Hoon Choi,et al.  Design and implementation of practical asset tracking system in container terminals , 2012 .

[62]  Mojtaba Ahmadieh Khanesar,et al.  Ant Colony Optimization Algorithm for Industrial Robot Programming in a Digital Twin , 2019, 2019 25th International Conference on Automation and Computing (ICAC).

[63]  Jianwen Chen,et al.  An Algorithm for Obstacle Detection based on YOLO and Light Filed Camera , 2018, 2018 12th International Conference on Sensing Technology (ICST).

[64]  Marc Priggemeyer,et al.  Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0 , 2018, IEEE Transactions on Industrial Informatics.

[65]  Chuen-Chien Lee,et al.  Fuzzy logic in control systems: fuzzy logic controller. II , 1990, IEEE Trans. Syst. Man Cybern..

[66]  Guo Li,et al.  Digital-twin-driven geometric optimization of centrifugal impeller with free-form blades for five-axis flank milling , 2020 .

[67]  Jianwei Yang,et al.  Feature Trend Extraction and Adaptive Density Peaks Search for Intelligent Fault Diagnosis of Machines , 2019, IEEE Transactions on Industrial Informatics.

[68]  Raymond G. Gosine,et al.  Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges , 2020, IEEE Access.

[69]  Michael W. Grieves,et al.  Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems , 2017 .

[70]  V. Costalat,et al.  Comparison of Pipeline Embolization Device Sizing Based on Conventional 2D Measurements and Virtual Simulation Using the Sim&Size Software: An Agreement Study , 2019, American Journal of Neuroradiology.

[71]  Branka Vucetic,et al.  Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin , 2019, IEEE Transactions on Wireless Communications.

[72]  Rikard Söderberg,et al.  Toward a Digital Twin for real-time geometry assurance in individualized production , 2017 .

[73]  Feng Liu,et al.  Big data driven Hierarchical Digital Twin Predictive Remanufacturing paradigm: Architecture, control mechanism, application scenario and benefits , 2020 .

[74]  Wojciech Zaremba,et al.  OpenAI Gym , 2016, ArXiv.

[75]  James E. Warner,et al.  A digital twin feasibility study (Part II): Non-deterministic predictions of fatigue life using in-situ diagnostics and prognostics , 2020, Engineering Fracture Mechanics.

[76]  Gerhard Schrotter,et al.  The Digital Twin of the City of Zurich for Urban Planning , 2020, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.

[77]  Xiaojun Liu,et al.  Digital twin-based process reuse and evaluation approach for smart process planning , 2018, The International Journal of Advanced Manufacturing Technology.

[78]  Bo Wang,et al.  Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry , 2019, Int. J. Inf. Manag..

[79]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[80]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[81]  Steffen Bangsow,et al.  Tecnomatix Plant Simulation , 2016 .

[82]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[83]  Adebena Oluwasegun,et al.  The application of machine learning for the prognostics and health management of control element drive system , 2020 .

[84]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[85]  Guohui Zhang,et al.  Digital twin-driven cyber-physical production system towards smart shop-floor , 2018, Journal of Ambient Intelligence and Humanized Computing.

[86]  Pieter Abbeel,et al.  Benchmarking Deep Reinforcement Learning for Continuous Control , 2016, ICML.

[87]  Tom Schaul,et al.  Prioritized Experience Replay , 2015, ICLR.

[88]  Eann A. Patterson,et al.  A framework for an integrated nuclear digital environment , 2016 .

[89]  Christopher Sacco,et al.  A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence , 2020 .

[90]  Pearl Brereton,et al.  Performing systematic literature reviews in software engineering , 2006, ICSE.

[91]  Martin Tomko,et al.  Beyond digital twins – A commentary , 2018, Environment and Planning B: Urban Analytics and City Science.

[92]  Oishee Mazumder,et al.  Synthetic PPG generation from haemodynamic model with baroreflex autoregulation: a Digital twin of cardiovascular system , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[93]  Xianfu Chen,et al.  Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective , 2019, IEEE Transactions on Wireless Communications.

[94]  Rong Xiong,et al.  Perception of Demonstration for Automatic Programing of Robotic Assembly: Framework, Algorithm, and Validation , 2018, IEEE/ASME Transactions on Mechatronics.

[95]  Omer San,et al.  Digital Twin: Values, Challenges and Enablers From a Modeling Perspective , 2019, IEEE Access.

[96]  Samad M. E. Sepasgozar,et al.  Digital Twin and Web-Based Virtual Gaming Technologies for Online Education: A Case of Construction Management and Engineering , 2020, Applied Sciences.

[97]  Fei Wang,et al.  A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin , 2019, IEEE Access.

[98]  Guang-Bin Huang,et al.  Extreme Learning Machine for Multilayer Perceptron , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[99]  Abdulmotaleb El-Saddik,et al.  Cardio Twin: A Digital Twin of the human heart running on the edge , 2019, 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[100]  Congbin Yang,et al.  Data Super-Network Fault Prediction Model and Maintenance Strategy for Mechanical Product Based on Digital Twin , 2019, IEEE Access.

[101]  Fan Yang,et al.  Digital twin for the structural health management of reusable spacecraft: A case study , 2020 .

[102]  Dirk Draheim,et al.  Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers? , 2019, IEEE Access.

[103]  Xiaoli Ma,et al.  A Constraint Multi-Objective Evolutionary Optimization of a State-of-the-Art Dew Point Cooler using Digital Twins , 2020, Energy Conversion and Management.

[104]  Chun Jin,et al.  Deep reinforcement learning for a color-batching resequencing problem , 2020 .

[105]  Giovanni Sansavini,et al.  Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction , 2018, IEEE Transactions on Industrial Electronics.

[106]  Jianhua Liu,et al.  Digital twin-based smart production management and control framework for the complex product assembly shop-floor , 2018, The International Journal of Advanced Manufacturing Technology.

[107]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[108]  Luca Oneto,et al.  Data-driven ship digital twin for estimating the speed loss caused by the marine fouling , 2019, Ocean Engineering.

[109]  Jianan Zhao,et al.  An In Silico Subject-Variability Study of Upper Airway Morphological Influence on the Airflow Regime in a Tracheobronchial Tree , 2017, Bioengineering.

[110]  Yingfeng Zhang,et al.  A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products , 2017 .

[111]  Changqing Shen,et al.  Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery , 2017, IEEE Access.

[112]  Yusheng Ji,et al.  Resource Awareness In Unmanned Aerial Vehicle-Assisted Mobile-Edge Computing Systems , 2019, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[113]  Tom White,et al.  Hadoop: The Definitive Guide , 2009 .

[114]  Jose Antonio Marmolejo-Saucedo,et al.  Design and Development of Digital Twins: a Case Study in Supply Chains , 2020, Mobile Networks and Applications.

[115]  Hubert Lehner,et al.  Digital geoTwin Vienna: Towards a Digital Twin City as Geodata Hub , 2020, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.

[116]  Guanghui Zhou,et al.  Deep learning-enabled intelligent process planning for digital twin manufacturing cell , 2020, Knowl. Based Syst..

[117]  David Silver,et al.  Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.

[118]  Edward H. Glaessgen,et al.  The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles , 2012 .

[119]  Amit Agarwal,et al.  CNTK: Microsoft's Open-Source Deep-Learning Toolkit , 2016, KDD.

[120]  Spiridon Bakiras,et al.  Multilevel Graph-Based Decision Making in Big Scholarly Data: An Approach to Identify Expert Reviewer, Finding Quality Impact Factor, Ranking Journals and Researchers , 2018, IEEE Transactions on Emerging Topics in Computing.

[121]  Evgeny Nefedov,et al.  Composition and Application of Power System Digital Twins Based on Ontological Modeling , 2019, 2019 IEEE 17th International Conference on Industrial Informatics (INDIN).

[122]  Liang Zhao,et al.  Intelligent Digital Twin-Based Software-Defined Vehicular Networks , 2020, IEEE Network.

[123]  Frédéric Thiesse,et al.  LotTrack: RFID-based process control in the semiconductor industry , 2006, IEEE Pervasive Computing.

[124]  Dunwei Gong,et al.  A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems , 2018, IEEE Transactions on Evolutionary Computation.

[125]  Roland Rosen,et al.  About The Importance of Autonomy and Digital Twins for the Future of Manufacturing , 2015 .

[126]  Perumal Nithiarasu,et al.  A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method , 2019, International journal for numerical methods in biomedical engineering.

[127]  Costas J. Spanos,et al.  A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems , 2020, IEEE Transactions on Power Electronics.

[128]  Yongli Wei,et al.  A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin , 2020, Robotics Comput. Integr. Manuf..

[129]  Yu Cheng,et al.  Early Fault Detection Approach With Deep Architectures , 2018, IEEE Transactions on Instrumentation and Measurement.

[130]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[131]  Attila Piros,et al.  Error handling method for digital twin-based plasma radiation detection , 2020 .

[132]  Meng Zhang,et al.  Digital Twin Enhanced Dynamic Job-Shop Scheduling , 2020 .

[133]  Yingfeng Zhang,et al.  A big data driven analytical framework for energy-intensive manufacturing industries , 2018, Journal of Cleaner Production.