Expert System Supporting the Diagnosis of the Wind Farm Equipments

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


An expert system that supports diagnosing of wind farm equipments is presented in this paper. First, the created functional and diagnostic models were presented for two basic elements of farms, such as: the wind power plant (turbine) and the electrical substation. Next, a division was made of the aforementioned elements into internal structure components (blocks), and diagnostic signals were determined for them. Based on these signals and their properties, a set of input data, parameters and an expert knowledge base in the form of facts and rules were developed. On further stages, the inference process of the expert system was characterized and the individual paths of obtaining a diagnosis of the working condition of wind farm devices were described. Additionally, the graphical user interface was discussed, and the manner of the presentation of inference process results was explained for general and detailed diagnosis.


Technical diagnostics Reliability of a technical object Neural networks Servicing process Expert system Knowledge base Diagnostic information 


  1. 1.
    Ackermann, T.: Wind Power in Power Systems, 2nd edn. Wiley, Hoboken (2012)CrossRefGoogle Scholar
  2. 2.
    Buchannan, B.G., Shortliffe, E.: Rule-Based Expert Systems, 2nd edn. Wesley, Boston (1985)Google Scholar
  3. 3.
    Duer, S.: Expert knowledge base to support the maintenance of a radar system. Defence Sci. J. 60(5), 531–540 (2010)CrossRefGoogle Scholar
  4. 4.
    Duer, S.: Artificial neural network in the control process of object’s states basis for organization of a servicing system of a technical objects. Neural Comput. Appl. 21(1), 153–160 (2012)CrossRefGoogle Scholar
  5. 5.
    Duer, S.: Intelligent system of supporting the process renewal of operating characteristics in complex technical objects. Technical University of Koszalin, Koszalin (2012)Google 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., Duer, R., Mazuru, S.: Determination of the expert knowledge base on the basis of a functional and diagnostic analysis of a technical object. In: Association of Nonconventional Technologies, Romanian, vol. 6, pp. 23–29 (2016)Google Scholar
  9. 9.
    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
  10. 10.
    Duer, S., Zajkowski, K.: Taking decisions in the expert intelligent system to support maintenance of a technical object on the basis information from an artificial neural network. Neural Comput. Appl. 23(7), 2185–2197 (2013)CrossRefGoogle Scholar
  11. 11.
    Duer, S., Zajkowski, K., Duer, R., Paś, J.: Designing of an effective structure of system for the maintenance of a technical object with the using information from an artificial neural network. Neural Comput. Appl. 23(3–4), 913–925 (2013)CrossRefGoogle Scholar
  12. 12.
    Hayer-Roth, F., Waterman, D.A., Lenat, D.E.: Building Expert Systems. Addison-Wesley Publishing Company, Boston (1983)Google Scholar
  13. 13.
    Jendrock, E., Cervera-Navarro, R., Evans, I., Haase, K., William, M.: Java Platform Enterprise Edition. The Java EE Tutorial, Release 7. Oracle (2014)Google Scholar
  14. 14.
    Kobayashi, S., Nakamura, K.: Knowledge compilation and refinement for fault diagnosis. IEEE Expert 6(5), 39–46 (1991)CrossRefGoogle Scholar
  15. 15.
    SourceFourge: CLIPS. A Tool for Building Expert Systems, Version 6.3, 03 July 2017.

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

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

Personalised recommendations