Information Support of Gas-Turbine Engine Life Cycle Based on Agent-Oriented Technology

  • A. Zagitova
  • N. Kondratyeva
  • S. ValeevEmail author
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


The development of a lifecycle support system for a complex technical object that enables information exchange in a shared information environment during the whole lifecycle is highly important. A problem of information flows control generated at various stages of life cycle of gas-turbine engine, using intelligent software agents is considered. The use of agent-based technologies allows increasing information exchange effectiveness and optimally using hardware resources, as well as to reduce time costs due to synchronization of the information exchange procedures in multiagent environment. In this paper, a prototype of multiagent system for neural network approximation of gas-turbine engine compressor performance is suggested. The example of neural network model of map performance of low-pressure compressor is presented.


Lifecycle support Gas-turbine engine Multiagent system Neural network 


  1. 1.
    Fedorchenkova DG, Novikova DK (2016) Cycle counting methods of the aircraft engine. Int J Environ Sci Educ 11(4):3832–3846Google Scholar
  2. 2.
    Novikov D (2014) Development of squeeze film damper characteristics calculation methods which take into account a liquid inertia forces. Res J Appl Sci 9(10):649–653Google Scholar
  3. 3.
    Astafiev VI, Fedorchenko DG, Tzypkaikin IN (1996) Complex stress-time cycles influence on aircraft engine parts fatigue strength. In: Proceedings of the sixth international fatigue congress. Berlin, 6–10 May 1996Google Scholar
  4. 4.
    Pierre C, Smith TE, Murthy DV (1991) Localization of aeroelastic modes in mistuned high-energy turbines. AIAA Paper 91–3379:1991Google Scholar
  5. 5.
    Gunetti P, Mills A, Thompson H (2008) A distributed intelligent agent architecture for gas-turbine engine health management. In: Proceedings of 46th AIAA aerospace sciences meeting and exhibit. Reno, NV, 7–10 Jan 2008Google Scholar
  6. 6.
    Gunetti P, Thompson H (2008) A soar-based planning agent for gas-turbine engine control and health management. In: Proceedings of 17th IFAC world congress. Seoul, Korea, 6–11 July 2008Google Scholar
  7. 7.
    Jennings N, Wooldridge M (1998) Applications of intelligent agents. In: Agent technology: foundation, applications and markets. Springer-Verlag Berlin HeidelbergGoogle Scholar
  8. 8.
    Wooldridge W (1999) Intelligent agents. In: Multi-agent systems: a modern approach to distributed artificial intelligence. The MIT Press, Cambridge, Massachusetts, London, EnglandGoogle Scholar
  9. 9.
    Rzevski G, Skobelev P (2014) Managing complexity. WIT Press, Southampton, BostonGoogle Scholar
  10. 10.
    Rzevski G, Brebbia CA (eds) (2018) Complex systems studies. WIT Press, Southampton, BostonGoogle Scholar
  11. 11.
    Rzevski G, Brebbia CA (eds) (2017) Complex systems theory and applications. WIT Press, Southampton, BostonzbMATHGoogle Scholar
  12. 12.
    Rzevski G, Brebbia CA (eds) (2016) Complex systems fundamentals and applications. WIT Press, Southampton, BostonzbMATHGoogle Scholar
  13. 13.
    Rzevski G (2016) Managing complexity: theory and practice. In: Proceedings of 11th system of systems engineering conference (SoSE), Kongsberg, Norway, 12–16 June 2016Google Scholar
  14. 14.
    Rzevski G, Knezevic J, Skobelev P et al (2016) Managing aircraft lifecycle complexity. Int J Des Nat Ecodyn 11(2):77–87CrossRefGoogle Scholar
  15. 15.
    Dambrosio L, Mastrovito M, Camporeale S (2007) Performance of gas turbine power plants controlled by multiagent scheme. J Eng Gas Turbines Power 129:738–745CrossRefGoogle Scholar
  16. 16.
    Kondratyeva NV, Valeev SS (2016) Simulation of the life cycle of a complex technical object within the concept of big data. In: CEUR proceedings of 3rd Russian conference on mathematical modeling and information technologies. Yekaterinburg, Russia, 16 Nov 2016Google Scholar
  17. 17.
    Kovtunenko A, Bilyalov A, Valeev S (2018) Distributed streaming data processing in IoT systems using multi-agent software architecture. In: Proceedings of the 18th international conference on next generation wired/wireless networking (NEW2AN), and 11th conference on internet of things and smart spaces (ruSMART). St. Petersburg, Russia, 27–29 Aug 2018Google Scholar
  18. 18.
    Kondratyeva N, Valeev S (2016) Fatigue test optimization for complex technical system on the basis of lifecycle modeling and big data concept. In: Proceedings of 10th conference on application of information and communication technologies AICT 2016. Baku, Azerbaijan, 12–14 Oct 2016Google Scholar
  19. 19.
    Vasilyev VI, Valeyev SS, Shilonosov AA (2001) Design of neurocontroller for gas-turbine engine multi-mode control. In: Proceedings of 8th international conference on neural information processing (ICONIP-2001). Fudan University Press, Shanghai, China, Nov 2001Google Scholar
  20. 20.
    JAVA Agent Development Framework (2018). Accessed 15 Sep 2018

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Ufa State Aviation Technical UniversityUfaRussia

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