The Diagnostic System with an Artificial Neural Network for Identifying States in Multi-valued Logic of a Device Wind Power

  • Stanisław Duer
  • Dariusz BernatowiczEmail author
  • Paweł Wrzesień
  • Radosław Duer
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 928)


The present article covers the idea of the examination of the value of the k-th logics of diagnostic information related to the assessment of the states of complex technical items. For this purpose, an intelligent diagnostic system was presented whose particular property is the possibility to select any k-th logic of inference from set {k = 4, 3, 2}. An important part of this study is the presentation of theoretical grounds that describe the idea of inference in the multi-valued logic examined. Furthermore, it was demonstrated that the permissible range of the values of the properties of diagnostic signals constitutes the basis of the classification of states in multi-valued logic in the DIAG 2 diagnostic system. For this purpose, a procedure of the classification of states in selected values of multi-valued logic was presented and described. An important element in the functioning of diagnostic systems, i.e. the module of inference was presented, as well. The rules of diagnostic inference were characterized and described based on which the process of inference is realized in the system.


Technical diagnostic Intelligent system Artificial neural networks Knowledge base Diagnostics information 


  1. 1.
    Barlow, R.E., Proschan, F.: Mathematical Theory of Reliability. Wiley, New York (1995)zbMATHGoogle Scholar
  2. 2.
    Birolini, A.: Reliability Engineering Theory and Practice. Springer, New York (1999). Scholar
  3. 3.
    Buchannan, B.G., Shortliffe, E.: Rule-Based Expert Systems, 2nd edn. Addison-Wesley, Boston (1985)Google Scholar
  4. 4.
    Dhillon, B.S.: Applied Reliability and Quality, Fundamentals Methods and Procedures. Springer-Verlag, London (2007). Scholar
  5. 5.
    Duer, S.: Artificial neural network in the control process of objects states basis for organization of a servicing system of a technical objects. Neural Comput. Appl. 21(1), 153–160 (2012)CrossRefGoogle Scholar
  6. 6.
    Duer, S., Bernatowicz, D.: The computer diagnostic program (DIAG 2) for identifying states of complex technical objects. In: International Conference Energy, Environment and Material Systems (EEMS). E3S Web of Conferences, Poland, vol. 19, pp. 241–247 (2017)Google Scholar
  7. 7.
    Duer, S., Duer, R.: Diagnostic system with an artificial neural network which determines a diagnostic information for the servicing of a reparable technical object. Neural Comput. Appl. 19(5), 755–766 (2010)CrossRefGoogle Scholar
  8. 8.
    Duer, S., Wrzesień, P., Duer, R.: Creating of structure of facts for the knowledge base of an expert system for wind power plant’s equipment diagnosis. In: International Conference Energy, Environment and Material Systems (EEMS). E3S Web of Conferences, Poland, vol. 19, pp. 242–247 (2017)Google Scholar
  9. 9.
    Gupta, M.M., Liang, J., Homma, N.: Static and Dynamic Neural Networks From Fundamentals to Advanced Theory. Wiley, New Jersey (2003)CrossRefGoogle Scholar
  10. 10.
    Hojjat, A., Shih-Lin, H.: Machine Learning, Neural Networks, Genetic Algorithms and Fuzzy Systems. Wiley, New Jersey (1995)zbMATHGoogle Scholar
  11. 11.
    Kacalak, W., Majewski, M.: New intelligent interactive automated systems for design of machine elements and assemblies. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012. LNCS, vol. 7666, pp. 115–122. Springer, Heidelberg (2012). Scholar
  12. 12.
    Nagagawa, T.: Maintenance Theory of Reliability. Springer-Verlag, London (2005). Scholar
  13. 13.
    Pokordi, Laszlo Duer, S.: Investigation of maintenance process with Markov matrix. In: Proceedings of the 4th International Scientific Conference on Advances in Mechanical Engineering, Hungary, vol. 19, pp. 402–407 (2016)Google Scholar
  14. 14.
    Rosiński, A.: Design of the electronic protection systems with utilization of the method of analysis of reliability structures. In: Nineteenth International Conference On Systems Engineering (ICSEng), USA, pp. 402–407 (2008)Google Scholar
  15. 15.
    Rosiński, A.: Reliability analysis of the electronic protection systems with mixed - three branches reliability structure. In: Bris, R. Guedes Soares, C., Martorell, S. (eds.) Reliability, Risk and Safety: Theory and Applications. CRC Press/Balkema, London/Amsterdam (2010)Google Scholar
  16. 16.
    Zadeh, L.A.: Toward extended fuzzy logic - a first step, fuzzy sets and systems. Fuzzy Sets Syst. 160(21), 3175–3181 (2009)CrossRefGoogle Scholar
  17. 17.
    Zajkowski, K.: The method of solution of equations with coefficients that contain measurement errors, using artificial neural network. Neural Comput. Appl. 24(2), 431–439 (2014)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Zurada, J.M.: Introduction to Artificial Neural Systems. West Publishing Company, Saint Paul (1992)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Stanisław Duer
    • 1
  • Dariusz Bernatowicz
    • 2
    Email author
  • Paweł Wrzesień
    • 3
  • Radosław Duer
    • 2
  1. 1.Faculty of Mechanical EngineeringKoszalin University of TechnologyKoszalinPoland
  2. 2.Faculty of Electronics and Computer ScienceKoszalin University of TechnologyKoszalinPoland
  3. 3.Department of Technical and Commercial ManagementVortex Energy Poland sp. z o.o.SzczecinPoland

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