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Evidence Theory for Complex Engineering System Analyses

  • Boris Palyukh
  • Vladimir Ivanov
  • Alexander Sotnikov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)

Abstract

The evolution of manufacturing systems with internal multi-stage processes takes place in a fuzzy dynamic environment. One of the instruments of system component state and property modelling is a mathematical theory of evidence. The article examines some examples of the evidence theory applications which deal with the two different stages of the system development. In particular, the problem of simulation, diagnostics, and assessment of complex engineering system states is considered. Necessity and sufficiency conditions of critical component states are shown to be relaxed. The other example of the evidence theory application illustrates the quantitative assessment approach to technical system component innovation. The coprocessing of primary innovation data is taken up, with the data being retrieved from various sources with different measurement and expert appraisal completeness and reliability.

Keywords

Theory of evidence Multi-stage process Dynamic environment Diagnostics Innovation Assessment 

Notes

Acknowledgment

The work was supported by RFBR (Projects No. 18-07-00358 and No. 17-07-01339).

References

  1. 1.
    Dempster, A.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325339 (1967).  https://doi.org/10.1214/aoms/1177698950MathSciNetCrossRefGoogle Scholar
  2. 2.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  3. 3.
    Yager, R., Liu, L.: Classic Works of the Dempster-Shafer Theory of Belief Functions. Springer, London (2010)zbMATHGoogle Scholar
  4. 4.
    Vejnarov, J., Kratochvl, V.: Belief Functions: Theory and Applications. Springer International Publishing, Prague (2016). 4th International Conference (Czech Republic, Prague, 21–23 September 2016)Google Scholar
  5. 5.
    Horst, C. (ed.): Handbook of Technical Diagnostics. Springer, Heidelberg (2013)Google Scholar
  6. 6.
    Palyukh, B.V., Vetrov, A.N., Yegereva, I.A.: Architecture of intelligent optimal control system for multi-stage processes evolution in fuzzy dynamic environment. Softw. Prod. Syst. 30(4), 619624 (2017)Google Scholar
  7. 7.
    Palyukh, B.V., Ivanov, V.K., Yegereva, I.A.: Innovation intelligent search and production system evolution control. In: 8th International Research and Practice Conference of AI Integrated Models and Soft Computing, Kolomna, 18–20 May 2015, vol. 1, p. 418427 (2015)Google Scholar
  8. 8.
    Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data. The Measurement of Scientific and Technological Activities, 3rd Edition (2005). https://www.oecd-ilibrary.org/science-and-technology/oslo-manual_9789264013100-en
  9. 9.
    Tucker, R.B.: Driving Growth Through Innovation: How Leading Firms are Transforming Their Futures, 2nd edn. Berrett-Koehler Publishers, San Francisco (2008)Google Scholar
  10. 10.
    Schumpeter, J.A.: Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process, New York(1939)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Boris Palyukh
    • 1
  • Vladimir Ivanov
    • 1
  • Alexander Sotnikov
    • 2
  1. 1.Tver State Technical UniversityTverRussia
  2. 2.Joint Supercomputer Centre of the RASMoscowRussia

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