On Systemological Approach to Intelligent Decision-Making Support in Industrial Cyber-Physical Systems

  • Alexey V. KizimEmail author
  • Alla G. Kravets
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 260)


The presented study solves the actual scientific and technical problem of developing a new approach related to the creation of models, methods, and algorithms, as well as an application development platform for intelligent decision support in man-aging the process of maintenance, repair and upgrading to increase the efficiency of industrial equipment at all stages life cycle. Systematization of tasks, methods and means of maintenance and repair of Industrial Cyber-Physical Systems in the light of the equipment life cycle was build. Input effects and response systems, components, internal communications was identified. Systemological model of the process of maintenance and repair was formalized. To improve the efficiency of maintenance and repair, a method of continuous improvement of the process of maintenance and repair of the maintenance program has been developed. As a result of approbation, an increase in the efficiency and quality of equipment maintenance, a reduction in costs up to 15%, and an increase in the overall efficiency of the organization of maintenance and repair processes up to 20% were obtained, which is confirmed by the implementation certificates.


Maintenance and repair Industrial cyber-physical systems Intelligent decision-making support Systemological approach Continuous improvement Equipment ontology Multi-agent system 



This research was supported by the Russian Fund of Basic Research (grant No. 19-07-01200).


  1. 1.
    Aliseichik, A.P., Pavlovsky, V.E.: The model and dynamic estimates for the controllability and comfortability of a multiwheel mobile robot motion. Autom. Remote Control 76(4), 675–688 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Arlazarov, V.L., Slavin, O.A., Shustov, A.V.: Evaluation of value of information and technical complex of complex system. Proc. Inst. Syst. Anal. Russ. Acad. Sci. 29, 152–182 (2007)Google Scholar
  3. 3.
    Azarov, V.N., Kaperko, A.F.: General topics of metrology and measurement technology-analysis of the state, development trends, and new developments of transducers and information converters for measurement, monitoring. Meas. Tech. 41(1), 2–9 (1998)CrossRefGoogle Scholar
  4. 4.
    Bogomolov, A. V., et al.: Information-logical modeling of information collection and processing at the evaluation of the functional reliability of the aviation ergate control system operator. In: 2018 Third International Conference on Human Factors in Complex Technical Systems and Environments (ERGO) s and Environments (ERGO), IEEE, pp. 106–110 (2018)Google Scholar
  5. 5.
    Bosenko, V.N., Kravets, A.G., Kamaev, V.A.: Development of an automated system to improve the efficiency of the oil pipeline construction management. World Appl. Sci. J. 24(24), 24–30 (2013)Google Scholar
  6. 6.
    Bukin, A.G., Lychagov, A.S., Sadekov, R.N., Slavin, O.A.: A computer vision system for navigation of ground vehicles: hardware and software. Gyroscopy Navig. 7(1), 66–71 (2016)CrossRefGoogle Scholar
  7. 7.
    Chernyshev, S.L., Lyapunov, S.V., Wolkov, A.V.: Modern problems of aircraft aerodynamics. Adv. Aerodyn. 1(1), 7p (2019)Google Scholar
  8. 8.
    Chistyakova, T.B., et al.: Decision support system for optimal production planning polymeric materials using genetic algorithms. In: 2016 XIX IEEE International Conference on Soft Computing and Measurements (SCM), IEEE, pp. 257–259 (2016)Google Scholar
  9. 9.
    Galyaev, A.A., Miller, B.M., Rubinovich, E.Y.: Optimal Impulsive Control of Dynamical System in an Impact Phase. Analysis and Simulation of Contact Problems, pp. 385–386. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Karpenko, A.P., Leshchev, I.A.: Nature-Inspired Algorithms for Global Optimization in Group Robotics Problems. Smart Electromechanical Systems, pp. 91–106. Springer, Cham (2019)Google Scholar
  11. 11.
    Kizim, A.V., et al.: Developing a model of multi-agent system of a process of a tech inspection and equipment repair. In: Joint Conference on Knowledge-Based Software Engineering, pp. 457–465. Springer, Cham (2014)Google Scholar
  12. 12.
    Kizim, A.V., et al.: Development of the intelligent platform of technical systems modernization at different stages of the life cycle. Proc. Comput. Sci. 121, 913–919 (2017)CrossRefGoogle Scholar
  13. 13.
    Kizim, A.V., et al.: Predictive modeling as a basis for monitoring, diagnosis, forecasting and upgrading of a technical system. In: 2017 IEEE 11th International Conference on Application of Information and Communication Technologies (AICT), IEEE, pp. 1–5 (2017)Google Scholar
  14. 14.
    Kizim, A.V., et al.: Cretion and use of ontology of subject domain ‘electrical engineering’. In: 2015 9th International Conference on Application of Information and Communication Technologies (AICT), IEEE, pp. 25–29 (2015)Google Scholar
  15. 15.
    Kizim, A.V.: The developing of the maintenance and repair body of knowledge to increasing equipment maintenance and repair organization efficiency. Inf. Resour. Manage. J. (IRMJ) 29(4), 49–64 (2016)CrossRefGoogle Scholar
  16. 16.
    Kizim, A., Matokhina, A., Nesterov, B.: Development of ontological knowledge representation model of industrial equipment. In: Proceedings of the Creativity in Intelligent Technologies and Data Science, CIT&DS 2015, Volgograd, Russia, 15–17 Sept 2015, vol. 535, p. 354. Springer, Heidelberg (2015)Google Scholar
  17. 17.
    Kozlov, V.N.: The system analysis, optimization and decision-making: study guide. Prospect, Moscow (2010)Google Scholar
  18. 18.
    Kravets, A., Kozunova, S.: The risk management model of design department’s PDM information system. Commun. Comput. Inf. Sci. 754, 490–500 (2017)Google Scholar
  19. 19.
    Kravets, A., Shumeiko, N., Lempert, B., Salnikova, N., Shcherbakova, N.: “Smart Queue” approach for new technical solutions discovery in patent applications. Commun. Comput. Inf. Sci. 754, 37–47 (2017)Google Scholar
  20. 20.
    Kravets, A.G., Belov, A.G., Sadovnikova, N.P.: Models and methods of professional competence level research. Recent Patents Comput. Sci. 9(2), 150–159 (2016)Google Scholar
  21. 21.
    Kravets, A.G., Bui, N.D., Al-Ashval, M.: Mobile security solution for enterprise network. Commun. Comput. Inf. Sci. 466 CCIS, 371–382 (2014)Google Scholar
  22. 22.
    Kravets, A.G., Fomenkov, S.A., Kravets, A.D.: Component-based approach to multi-agent system generation. Commun. Comput. Inf. Sci. 466 CCIS, 483–490 (2014)Google Scholar
  23. 23.
    Kravets, A.G., Kravets, A.D., Korotkov, A.A.: Intelligent multi-agent systems generation. World Appl. Sci. J. 24(24), 98–104 (2013)Google Scholar
  24. 24.
    Matokhina, A.V., Kizim, A.V., Nikitin, N.A.: Technical system modernization during the operation stage. In: Conference on Creativity in Intelligent Technologies and Data Science, pp. 350–360. Springer, Cham (2017)Google Scholar
  25. 25.
    Moshev, E.R., Meshalkin, V.P.: Computer-based logistics support system for the maintenance of chemical plant equipment. Theoret. Found. Chem. Eng. 48(6), 855–863 (2014)CrossRefGoogle Scholar
  26. 26.
    Parygin, D., Sadovnikova, N., Kravets, A., Gnedkova, E.: Cognitive and ontological modeling for decision support in the tasks of the urban transportation system development management. IISA 2015—6th International Conference on Information, Intelligence, Systems and Applications, Art. no. 7388073 (2016)Google Scholar
  27. 27.
    Shcherbakov, M., Groumpos, P.P., Kravets, A.: A method and IR4I index indicating the readiness of business processes for data science solutions. Commun. Comput. Inf. Sci. 754, 21–34 (2017)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Volgograd State Technical UniversityVolgogradRussian Federation

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