Skip to main content
SpringerLink
Log in
Book cover

Cyber-Physical Systems: Advances in Design & Modelling pp 253–262Cite as

Assessment of the State of Production System Components for Digital Twins Technology

  • Chapter
  • First Online:

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 259))

Abstract

The problem of assessment of the state of production systems is considered. The chapter is suggested applying the technology of digital twins to solve the problem of diagnosing and predicting the state of the components of the production system. The hierarchical structure of modern production is described, as well as the interaction of the production system and its digital twin. The correspondence of the system components and models of their state assessment is indicated. Methods and tools for assessing the state of the components of different hierarchical levels of the production system representation are proposed. As an example, the assessment of the state of stamp-tool production is considered and the models for assessing the state of its components for the digital twin are given. Also, a criterion and method for assessing the state of the upper organizational and technical level of this system are proposed.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Nikolova, N., Hirota, K., Kolev, K., Tenekedjiev, K.: Technical diagnostic system in the maintenance of turbomachinery for ammonia synthesis in the process Industries. J. Loss Prev. Process Ind. 58, 102–115 (2019). https://doi.org/10.1016/j.jlp.2019.02.002

    Article  Google Scholar 

  2. Efthymiou, K., Papakostas, N., Mourtzis, D., Chryssolouris, G.: On a predictive maintenance platform for production systems. Procedia CIRP 3, 221–226 (2012). https://doi.org/10.1016/j.procir.2012.07.039

    Article  Google Scholar 

  3. Kumenko, A.I.: The improvement modification of rotor unbalance verification technique in monitoring systems and automatic diagnostics. Procedia Eng. 113, 324–331 (2015). https://doi.org/10.1016/j.proeng.2015.07.273

    Article  Google Scholar 

  4. Protalinsky, O.M., Shcherbatov, I.A., Stepanov, P.V.: Identification of the actual state and entity availability forecasting in power engineering using neural-network technologies. J. Phys.: Conf. Ser. 891(1), 10. Nov 2017, Article 012289 (2017). https://doi.org/10.1088/1742-6596/891/1/012289

    Google Scholar 

  5. Protalinsky, O., Khanova, A., Shcherbatov, I.: Simulation of power assets management process. In: Dolinina, O. et al. (eds.) Recent Research in Control Engineering and Decision Making, ICIT-2019. Studies in Systems, Decision and Control, vol. 199, pp. 488–501 Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12072-6_40

    Chapter  Google Scholar 

  6. Lu, Y.: Industry 4.0: a survey on technologies, applications and open research issues. J. Ind. Inf. Integr. 6, 1–10 (2017). https://doi.org/10.1016/j.jii.2017.04.005

    Article  Google Scholar 

  7. Lee, J., Bagheri, B., Kao, H.A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)

    Article  Google Scholar 

  8. Hermann, M., Pentek, T., Otto, B.: Design principles for industrie 4.0 scenarios. In: Proceedings of the Annual Hawaii International Conference on System Sciences, Article 7427673, pp. 3928–3937 (2016). http://dx.doi.org/10.1109/HICSS.2016.488

  9. Herwan, J., Kano, S., Ryabov, O., Sawada, H., Kasashima, N.: Cyber-physical system architecture for machining production line. In: 2018 IEEE Industrial Cyber-Physical Systems (ICPS), pp. 387–391 (2018). https://doi.org/10.1109/ICPHYS.2018.8387689

  10. Koval’, V.A., Osenin, V.N., Suyatinov, S.I., Torgashova, O.Y.: Synthesis of discrete controller for construction of a distributed controller of temperature conditions of steam oil heater. J. Comput. Syst. Sci. Int. 50(4), 638–653 (2011). https://doi.org/10.1134/S1064230711040125

    Article  MathSciNet  MATH  Google Scholar 

  11. Sowe, S.K., Zettsu, K., Simmon, E., de Vaulx, F., Bojanova, I.: Cyber-physical human systems: putting people in the loop. IT Prof. 18(1), 10–13 (2016). https://doi.org/10.1109/MITP.2016.14

    Article  Google Scholar 

  12. Sénéchal, O., Trentesaux, D.: A framework to help decision makers to be environmentally aware during the maintenance of cyber physical systems. Environ. Impact Assess. Rev. 77, 11–22 (2019). https://doi.org/10.1016/j.eiar.2019.02.007

    Article  Google Scholar 

  13. Sharpe, R., Lopik, K.V., Neal, A., Goodall, P., Conway, P.P., West, A.A.: An industrial evaluation of an Industry 4.0 reference architecture demonstrating the need for the inclusion of security and human components. Computers in Industry, vol. 108, pp. 37–44 (2019). https://doi.org/10.1016/j.compind.2019.02.007

    Article  Google Scholar 

  14. Skvortsov, V., Proletarsky, A., Arzybaev, A.: Feature recognition module of the CAPP system. In: Proceedings of the 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus (2019). http://dx.doi.org/10.1109/EIConRus.2019.8656655

  15. Tarassov, V.B.: Enterprise total agentification as a way to industry 4.0: forming artificial societies via Goal-resource networks. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds.) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). Advances in Intelligent Systems and Computing, vol. 874, pp. 26–40. Springer, Cham (2019). http://dx.doi.org/10.1007/978-3-030-01818-4_3

    Google Scholar 

  16. Bozhko, A.: Math modeling of sequential coherent and linear assembly plans in CAD systems. In: 2018 Global Smart Industry Conference (GloSIC), pp. 1–5 (2018). http://dx.doi.org/10.1109/GloSIC.2018.8570090

  17. Prado, M., Roa, L., Reina-Tosina, J.: Virtual center for renal support: technological approach to patient physiological image. IEEE Trans. Biomed. Eng. 49(12), 1420–1430 (2002)

    Article  Google Scholar 

  18. Suyatinov, S.I.: Criteria and method for assessing the functional state of a human operator in a complex organizational and technical system. In: Global Smart Industry Conference (GloSIC), pp. 1–6. Chelyabinsk, Russia (2018). http://dx.doi.org/10.1109/GloSIC.2018.8570088

  19. Buldakova, T., Krivosheeva, D.: Data protection during remote monitoring of person’s state. In: Dolinina, O., et al. (eds.) Recent Research in Control Engineering and Decision Making, ICIT-2019. Studies in Systems, Decision and Control, vol. 199, pp. 3–14. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12072-6_1

    Chapter  Google Scholar 

  20. Qian, P., Zhang, D., Tian, X., Si, Y., Li, L.: A novel wind turbine condition monitoring method based on cloud computing. Renew. Energ. 135, 390–398 (2019). https://doi.org/10.1016/j.renene.2018.12.045

    Article  Google Scholar 

  21. Chattal, M., Bhan, V., Madiha, H., Shaikh, S.A.: Industrial automation control trough PLC and labview. In: 2nd International Conference on Computing, Mathematics and Engineering Technologies, iCoMET (2019). https://doi.org/10.1109/ICOMET.2019.8673448

  22. Buldakova, T.I., Suyatinov, S.I.: Registration and identification of pulse signal for medical diagnostics. In: Proceedings of SPIE—The International Society for Optical Engineering, vol. 4707, Article 48, pp. 343–350 (2002)

    Google Scholar 

  23. Buldakova, T.I., Suyatinov, S.I.: Reconstruction method for data protection in telemedicine systems. In: Progress in Biomedical Optics and Imaging—Proceedings of SPIE, vol. 9448, Article 94481U (2014). https://doi.org/10.1117/12.2180644

  24. Efstathiou, J., Calinescu, A., Blackburn, G.: A web-based expert system to assess the complexity of manufacturing organizations. Robot. Comput. Integr. Manuf. 18, 305–311 (2002). https://doi.org/10.1016/S0736-5845(02)00022-4

    Article  Google Scholar 

  25. Modrak, V., Soltysova, Z.: Novel complexity indicator of manufacturing process chainsand and its relations to indirect complexity indicators. Complexity, Article ID 9102824, pp. 1–15 (2017). https://doi.org/10.1155/2017/9102824

    Article  MathSciNet  Google Scholar 

  26. Kedadouche, M., Thomas, M., Tahan, A., Guilbault, R.: Nonlinear parameters for monitoring gear: comparison between Lempel-Ziv, approximate entropy, and sample entropy complexity. Shock. Vib., Article ID 959380, 1–12 (2015). http://dx.doi.org/10.1155/2015/959380

    Article  Google Scholar 

  27. Isik, F.: An entropy-based approach for measuring complexity in supply chains. Int. J. Prod. Res. 48(12), 3681–3696 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bauman Moscow State Technical University, 2-ya Baumanskaya, 5, Moscow, 105005, Russia

    T. I. Buldakova & S. I. Suyatinov

Authors
  1. T. I. Buldakova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. I. Suyatinov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. I. Buldakova .

Editor information

Editors and Affiliations

  1. Volgograd State Technical University, Volgograd, Russia

    Alla G. Kravets

  2. Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    Alexander A. Bolshakov

  3. Volgograd State Technical University, Volgograd, Russia

    Maxim V. Shcherbakov

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Buldakova, T.I., Suyatinov, S.I. (2020). Assessment of the State of Production System Components for Digital Twins Technology. In: Kravets, A., Bolshakov, A., Shcherbakov, M. (eds) Cyber-Physical Systems: Advances in Design & Modelling. Studies in Systems, Decision and Control, vol 259. Springer, Cham. https://doi.org/10.1007/978-3-030-32579-4_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-32579-4_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32578-7

  • Online ISBN: 978-3-030-32579-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation