Diagnosis in SEMS Based on Cognitive Models

  • Vladimir V. Korobkin
  • Anna E. Kolodenkova
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 174)


Problem statement: SEMS is a complex dynamic object in the operational phase of which it is likely that abnormal situations may occur in which the state of the equipment goes beyond the normal functioning, which can subsequently lead to an accident. Despite the importance of the need and importance of effectively solving the problems of diagnostics of SEMS, at the present time there is no single approach to solving similar problems taking into account the variety of emergent contingencies. Therefore, the actual task is the development of methods, algorithms and special diagnostic tools that allow predicting the development of defects, diagnose processes and recognize violations of normal operation at an early stage of their development to ensure the efficiency, reliability and safety of the operation of SEMS in real time. Results: cognitive to diagnose SEMS in conditions of interval uncertainty and fuzzy initial data, cognitive and fuzzy cognitive modeling is used to reflect the problems of SEMS in a simplified form (in the model), to investigate possible scenarios for the emergence of risk situations at an early stage of their development, and to find ways to resolve them in the model of the situation. As an example, a fuzzy cognitive model of SEMS diagnostics is proposed. Pessimistic and optimistic scenarios of possible development of risk situations, developed with the help of impulse simulation, are given and their brief analysis is given. The system indices of the fuzzy cognitive model are calculated, allowing to identify which of the factors have the greatest impact on SEMS and vice versa; To search for the best values of factors reflecting the normal operation of SEMS. Practical significance: the ability to systematically take into account the long-term consequences of possible abnormal situations and identify side effects that allow to take into account the multifactority of the process of diagnosing SEMS in the process of operation. The task of identifying possible risks in general, and at the operational stage in particular, should be an important part of the diagnostics of SEMS equipment.


SEMS Information-control systems Cognitive and fuzzy cognitive modeling 



This work was financially supported by Russian Foundation for Basic Research, Grant No. 17-08-00402.


  1. 1.
    Yurkov, N.K.: A systematic approach to the organization of the life cycle of complex technical systems. Reliability and quality of complex systems. J. Res. Pract. 1, 27–35 (2013)Google Scholar
  2. 2.
    Bushueva, M.E., Belyakov, V.V.: Diagnostics of complex technical systems. In: Novgorod, N. (eds.) Proceedings of the First Workshop on the NATO Project SfP-973799 Semiconductors “Development of Radio-Resistant Semiconductor Devices for Communication Systems and Precision Measurements Using Noise Analysis”, pp. 63–99 (2001)Google Scholar
  3. 3.
    Semenov, S.S.: The main provisions of the system analysis in assessing the technical level of complex systems using the expert method. Reliab. Qual. Complex Syst. 4, 45–53 (2013)Google Scholar
  4. 4.
    Krioni, N.K., Kolodenkova, A.E., Korobkin, V.V., Gubanov, N.G.: Intelligent decision-making support system using cognitive modeling for project feasibility assessment on creating complex technical systems. Int. J. Appl. Bus. Econ. Res. 14(10), 7289–7300 (2016)Google Scholar
  5. 5.
    Roberts, F.S.: Discrete Mathematical Models Applied to Social, Biological and Ecological Problems. Nauka, Moscow (1986)Google Scholar
  6. 6.
    Dickerson, J., Kosko, B.: Virtual worlds as fuzzy cognitive maps. Virtual Reality Annual International Symposium, pp. 471-477 (1993)Google Scholar
  7. 7.
    Kosko, B.: Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 1, 65–75 (1986)CrossRefGoogle Scholar
  8. 8.
    Silov, V.B.: Strategic Decision Making in a Fuzzy Environment. INPRO-RES, Moscow (1995)Google Scholar
  9. 9.
    Kulba, V.V., Kononov, D.A., Kovalevsky, S.S.: Scenario analysis of dynamics of behavior of socio-economic systems. IPP RAS, Moscow (2002)Google Scholar
  10. 10.
    Casty, J.: Large Systems: Connectivity, Complexity and Disasters. Mir, Moscow (1982)Google Scholar
  11. 11.
    Novichikhin, A.V., Ulankin, A.N.: Methodical features of project programming for the development of enterprises in the resource region (on the example of the coal industry). Min. Inf. Anal. Bull. 3, 332–337 (2011)Google Scholar
  12. 12.
    Sadovnikova, N.P.: Application of the cognitive modeling for analysis of the ecological and economical efficiency of the urban planning project. Internet-Vestnik VolgGASU 5, 1–4 (2011)Google Scholar
  13. 13.
    Kazanin, IYu.: Research of social and economic security of Rostov Region, cognitive modeling of development strategy. News SFU Tech. Sci. 3, 12–16 (2009)Google Scholar
  14. 14.
    Bozhenyuk, A.V., Ginis, L.A.: Application of fuzzy models to the analysis of complex systems. Control Syst. Inf. Technol. 51(1.1), 122–126 (2013)Google Scholar
  15. 15.
    Atkin, R.H., Casti, J.: Polyhedral Dynamics and the Geometry of Systems, RR-77-6. International Institute for Applied Systems Analysis, Laxenburg (1977)zbMATHGoogle Scholar
  16. 16.
    Gorelova, G.V., Zakharova, E.N., Radchenko, S.A.: Investigation of semi-Structured Problems of Socio-economic Systems: A Cognitive Approach. RSU, Rostov-on-Don (2006)Google Scholar
  17. 17.
    Korostelev, D.A., Lagerev, D.G., Podvesovsky, A.G.: Application of fuzzy cognitive models to formation of a set of alternatives in decision-making problems. Vestnik Bryansk State Techn. Univ. 4, 77–85 (2009)Google Scholar
  18. 18.
    Borisov, V.V., Kruglov, V.V., Fedulov, A.S.: Fuzzy Models and Networks. Goryachaya Linia - Telecom, Moscow (2007)Google Scholar
  19. 19.
    Kolodenkova, A.E.: Evaluation of the feasibility of the project on the creation of information control systems using the fuzzy cognitive model training procedure. Vestnik USATU, 20(2 (72)), 123–133 (2016)Google Scholar
  20. 20.
    Papageorgiou, E.I.: Unsupervised hebbian algorithm for fuzzy cognitive map training. In: Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P. (eds.) Proceedings of the 5-th International Workshop on Computer Science and Information Technologies. – Ufa, vol. 1., pp. 209–216 (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Acad. Kalyaev Scientific Research Institute of Multiprocessor Computer SystemsTaganrogRussia
  2. 2.Department Chair «Information Technologies»Samara State Technical UniversitySamaraRussia

Personalised recommendations