Methods and approaches for creation of digital twins of cyber-physical systems

  • 1 Dept. of Industrial Automation, University of Chemical Technology and Metallurgy, Sofia, Bulgaria


The idea of creating and using digital twins has been strongly influenced by the process of integrating artificial intelligence methods with big data analytics of data from Internet of Things (IoT) devices. The concept of “Digital Twin” has become increasingly influential and culminating in the field of CPS. The main objective of the study is to define the basic requirements to the digital twins for cyber-physical system and based on the different definitions and components of digital twins, to summarize and analyze approaches, methods and tools used for their development. This analysis should serve as a basis for the development of a methodology for creating digital twins for cyber-physical manufacturing systems in the process industry.



  1. Lee E. (2008), Cyber Physical Systems: Design Challenges. Technical Report. Berkeley: University of California.
  2. Oughton E. J. (2018), Building a Digital Twin: Testing the effectiveness of telecommunication policies in a virtual world, University of Oxford.
  3. Barricelli B. R., Casiraghi E., Fogli D. (2019), A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications, in IEEE Access, vol. 7, pp. 167653-167671, doi: 10.1109/ACCESS.2019.2953499.
  4. Semeraro C., Lezoche M., Panetto H., Dassisti M. (2021), Digital twin paradigm: A systematic literature review, Computers in Industry, Elsevier, 130, pp.103469. pp.103469.10.1016/j.compind.2021.103469. hal-03218786
  5. Grieves M. (2014), Digital Twin: Manufacturing Excellence through Virtual Factory Replication. White Paper. NASA.
  6. Malykhina G. F., Tarkhov D. A. (2018), Digital twin technology as a basis of the industry in future, 18th PCSF 2018 Professional Сulture of the Specialist of the Future,
  7. Kritzinger W., Karner M., Traar G., Henjes J., Sihn W. (2018),
  8. Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine 51, 1016–1022., doi:10.1016/j.ifacol.2018.08.474.
  9. Fuller A., Fan Z., Day C., Barlow C. (2020), Digital Twin: Enabling Technologies, Challenges and Open Research, in IEEE Access, vol. 8, pp. 108952-108971, 2020, doi: 10.1109/ACCESS.2020.2998358.
  10. Selic B. (2003), The pragmatics of model-driven development. IEEE Software, 20(5):19–25.
  11. Sangiovanni-Vincentelli A. (2002) Defining platform-based design. EEDesign of EETimes.
  12. MapleSim.
  13. Modelica.
  14. Simulink.
  15. J. Magee and J. Kramer. Concurrency: State Models and Java Programming, Second Edition. Wiley, 2006.
  16. G. Frehse, C. L. Guernic, A. Donz´e, S. Cotton, R. Ray, O. Lebeltel, R. Ripado, A. Girard, T. Dang, and O. Maler. SpaceEx: scalable verification of hybrid systems. In Proceedings of the 23rd International Conference on Computer Aided Verification (CAV), 2011.
  17. OMNeT++.
  18. G. J. Holzmann. The SPIN model checker: primer and reference manual. Addison Wesley, 2003.
  19. OMG SysML: The OMG Systems Modeling Language,, May 2006.
  20. OMG MARTE (2007). Profile for Modeling and Analysis of Real-Time and Embedded (MARTE) systems, Beta 1, 2007.
  21. UPPAAL,
  22. Dessault Systemes,
  23. Verosim,
  24. AutomationML,
  25. Magargle R., Johnson L., Mandloi P., Davoudabadi P., Kesarkar O., Krishnaswamy S., Batteh J., Pitchaikani A. (2017), A simulation-based digital twin for model-driven health monitoring and predictive maintenance of an automotive braking system, in: International Modelica Conference, pp. 35–46, doi:10.3384/ecp1713235.
  26. IoTIFY,
  27. BoschIoT Suite,
  28. Seebo,
  29. PTC, ThingWorx Operator Advisor
  30. Petrik D., Herzwurm G. (2019), iIoT ecosystem development through boundary resources: a Siemens MindSphere case study, in: Proceedings of the 2nd ACM SIGSOFT International Workshop on Software-Intensive Business: Start-ups, Platforms, and Ecosystems - IWSiB 2019, ACM Press, Tallinn, Estonia. pp. 1–6. doi:10.1145/3340481.3342730.
  31. Chen X., Kang E., Shiraishi S., Preciado V.M., Jiang Z. (2018), Digital behavioral twins for safe connected cars, in: Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems - MODELS’18, ACM Press, Copenhagen, Denmark, pp. 144–153, doi:10.1145/3239372.3239401.
  32. Kumar S., Jasuja A. (2017), Air quality monitoring system based on IoT using Raspberry Pi, in: 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 1341– 1346. doi:10.1109/CCAA.2017.8230005.
  33. Damjanovic - Behrendt V. (2018), A Digital Twin-based Privacy Enhancement Mechanism for the Automotive Industry, in: 2018 International Conference on Intelligent Systems (IS), pp. 272– 279. doi:10. 1109/IS.2018.8710526.
  34. Automation, 2018. Bentley Systems releases iModel.js open source library. URL:

Article full text

Download PDF