Simulation Approach for Optimal Maintenance Intervals Estimation of Electronic Devices

  • Alexander LyubchenkoEmail author
  • Pedro A. Castillo
  • Antonio M. Mora
  • Pablo García-Sánchez
  • Maribel G. Arenas
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 466)


Simulation is a powerful and flexible technique for imitation of variety of stochastic processes and it has attractive advantages in comparison to analytical routine solutions. In this paper, the Monte Carlo simulation technique is used for imitation of operational process of electronic devices which is formalized by the model of Semi Markov process. The model considers sudden, gradual, latent and fictitious failures, human factor of service staff and time parameters of preventive maintenance. Simulation approach permits to obtain necessary data for estimation of recommended value of maintenance interval according to suggested optimality criterion. Moreover, it could be easily used for investigation and analyzing of the process with different combinations of input parameters.


Preventive maintenance (PM) Optimization Simulation Monte carlo Semi markov process 



This work has been supported in part by projects ERANET-Plus (European Commission), TIN2014-56494-C4-3-P (Spanish Ministry of Economy and Competitiveness), PROY-PP2015-06 (Plan Propio 2015 UGR), and UMNIK Program (Russian Foundation for Assistance to Small Innovative Enterprises in Science and Technology).


  1. 1.
    Gertsbakh, I.: Reliability Theory: With Applications to Preventive Maintenance. Springer Science and Business Media (2000)Google Scholar
  2. 2.
    Lutchenko, S.S.: Optimization of control and preventive maintenance of radio communication devices. Ph.D. Thesis, Omsk State Technical University (2000)Google Scholar
  3. 3.
    Filenkov, V.V.: Improvement of methods for safety control and estimation of automation and radio communication facilities. Ph.D. Thesis, Omsk State Technical University (2004)Google Scholar
  4. 4.
    Lutchenko, S.S., Bogachkov, I.V., Kopytov, E.Y.: The technique of determination of fiber-optical lines availability and maintenance intervals. In: 2015 International Siberian Conference on Control and Communications (SIBCON 2015), pp. 184–187. IEEE Press, Omsk (2015). doi: 10.1109/SIBCON.2015.7147004
  5. 5.
    Sirota, A.: Computational Modeling and Effectiveness Estimation of Complex Systems. Technosfera, Moscow (2006)Google Scholar
  6. 6.
    Barbu, V.S., Limnios, N.: Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications: Their Use in Reliability and DNA Analysis. Springer Science and Business Media (2009)Google Scholar
  7. 7.
    Pardoux, E.: Markov Processes and Applications: Algorithms, Networks, Genome and Finance. Wiley (2008)Google Scholar
  8. 8.
    Kelton, W.D., Law, A.M.: Simulation Modeling and Analysis. McGraw Hill, Boston (2000)Google Scholar
  9. 9.
    Asmussen, S., Glynn, P.W.: Stochastic Simulation: Algorithms and Analysis. Springer Science and Business Media (2007)Google Scholar
  10. 10.
    Press, W.H.: Numerical recipes 3rd edn: The Art of Scientific Computing. Cambridge University Press (2007)Google Scholar
  11. 11.
    Bartosh, S.V., Kogut, A.T., Lyubchenko, A.A.: The analysis of properties and selection of pseudorandom number generators for simulation. Vestnik Kompyuternyih i Informatsionnyih Tehnologiy, vol. 2 (116), pp. 52–57. Moscow (2014)Google Scholar
  12. 12.
    Sokolowski, J.A., Banks, C.M.: Principles of Modeling and Simulation: A Multidisciplinary Approach. Wiley (2011)Google Scholar

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Authors and Affiliations

  • Alexander Lyubchenko
    • 1
    Email author
  • Pedro A. Castillo
    • 2
  • Antonio M. Mora
    • 2
  • Pablo García-Sánchez
    • 2
  • Maribel G. Arenas
    • 2
  1. 1.Omsk State Transport UniversityOmskRussia
  2. 2.University of GranadaGranadaSpain

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