Skip to main content

Advertisement

SpringerLink
Log in

Taking decisions in the expert intelligent system to support maintenance of a technical object on the basis information from an artificial neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this paper, an intelligent operation system, which consists of an intelligent diagnostic subsystem (with a neural network) and an intelligent maintenance subsystem (with an expert system), was presented and discussed. The artificial neural network and the expert system, which use the information developed in the neural network, perform a special function in this system. The functional combination of the artificial neural network and the expert system together created a new solution in the form of an intelligent system, which was referred to as an intelligent maintenance system. This article also covers decision-making methods that are used in an expert maintenance system and whose purpose is an organization and control of the process of the prevention of technical objects. For this purpose, the method was described of taking decisions by an expert for complex parametric type hypotheses and for simple finished type hypotheses in the set of possible decisions’ hypotheses. A considerable part of this paper covers the presentation of the method to transform diagnostic information into the required form of maintenance information. For this purpose, an algorithm of the work of maintenance system was performed and descried. In the creation process of the maintenance knowledge base, the specialist knowledge of a human specialist was also used. Hence, a skilful and proper taking of decisions by an expert to create this set of information is essential. Two inference methods were characterized and described in this paper. The theoretical results obtained were verified in the examination of the influence of each of these decision-making inference methods on the final results of the process of the prevention treatment of an object.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Buchannan B, Shortliffe E (1985) Rule-based expert systems. Addison-Wesley, Reading, p 387

    Google Scholar 

  2. Duer S (2009) Artificial neural network-based technique for operation process control of a technical object. Def Sci J 59(3):305–313

    Google Scholar 

  3. Duer S, Duer R (2010) 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

    Article  Google Scholar 

  4. Duer S (2010) Investigation of the operation process of a repairable technical object in an expert servicing system with an artificial neural network. Neural Comput Appl 19(5):767–774

    Article  Google Scholar 

  5. Duer S (2010) Diagnostic system with an artificial neural network in diagnostics of an analogue technical object. Neural Comput Appl 19(1):55–60

    Article  Google Scholar 

  6. Duer S (2010) Qualitative evaluation of the regeneration process of a technical object in a maintenance system with an artificial neural network. Neural Comput Appl. doi:10.1007/s00521-010-0418-0

    Google Scholar 

  7. Duer S (2010) Expert knowledge base to support maintenance of a radar system. Def Sci J 60(5):531–540

    Google Scholar 

  8. Duer S (2011) Qualitative evaluation of the regeneration process of a technical object in a maintenance system with an artificial neural network. Neural Comput Appl 20(5):741–752. http://www.springer.com/home?SGWID=0-0-1003-0-0&aqId=1320967&checkval=ad10dea9b0005e4b1523cfd0443fbf7d

  9. Duer S (2011) Assessment of the quality of decisions worked out by an artificial neural network which diagnoses a technical object. Neural Comput Appl. doi:10.1007/s00521-011-0725-0

  10. Duer S (2011) Modelling of the operation process of repairable technical objects with the use information from an artificial neural network. Expert Syst Appl 38:5867–5878. http://dx.doi.org/10.1016/j.eswa.2010.11.036

  11. Duer S (2011) Examination of the reliability of a technical object after its regeneration in a maintenance system with an artificial neural network. Neural Comput Appl. doi:10.1007/s00521-011-0723-2

  12. Duer S (2011) Applications of an artificial intelligence for servicing of a technical object. Neural Comput Appl. doi:10.1007/s00521-011-0788-y

    Google Scholar 

  13. Duer S (2012) 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

    Article  Google Scholar 

  14. Dhillon BS (2006) Applied reliability and quality, fundamentals, methods and procedures. Springer, London, p 186

    Google Scholar 

  15. Hayer-Roth F, Waterman D, Lenat D (1983) Building expert systems. Addison-Wesley, Reading, p 321

    Google Scholar 

  16. Hojjat A, Hung S-L (1995) Machine learning, neural networks, genetic algorithms and fuzzy systems. Wiley, Hoboken, p 398

    MATH  Google Scholar 

  17. Gupta MM, Jin L, Homma N (2003) Static and dynamic neural networks, from fundamentals to advanced theory. Wiley, Hoboken, p 718

    Book  Google Scholar 

  18. Mathirajan M, Chandru V, Sivakumar AI (2007) Heuristic algorithms for scheduling heat-treatment furnaces of steel casting industries. Sadahana 32(Part 5):111–119

    Google Scholar 

  19. Nakagawa T (2005) Maintenance theory of reliability. Springer, London, p 264

    Google Scholar 

  20. Nakagawa T, Ito K (2000) Optimal inspection policies for a storage system with degradation at periodic tests. Math Comput Model 31:191–195

    Article  MathSciNet  MATH  Google Scholar 

  21. Palkova Z, Okenka I (2007) Programovanie. Slovak University of Agriculture in Nitra, Nitra, p 203

    Google Scholar 

  22. Palkova Z (2010) Modeling the optimal capacity of an irrigation system using queuing theory, vol 55. Warsaw University of Life Sciences Press, Warszawa, pp 5–11

    Google Scholar 

  23. Palkova Z, Rodny T (2010) Modeling of object-related database structures in the development of artificial irrigation system. In: Technika v technológiách agrosektora 2010 [elektronický zdroj]: zborník vedeckých prác, Nitra, November. Slovak University of Agriculture in Nitra http://www.slpk.sk/eldo/2011/zborniky/04-11/palkova-rodny.pdf

  24. Tang L, Liu J, Rong A, Yang Z (2002) Modeling and genetic algorithm solution for the slab stack shuffling problem when implementing steel rolling schedules. Int J Prod Res 40(7):272–276

    Article  Google Scholar 

  25. Wald A (1945) Sequential tests of statistical hypotheses. Ann Math Stat 16:117–186

    Article  MathSciNet  MATH  Google Scholar 

  26. Wiliams JM, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1:270–280

    Article  Google Scholar 

  27. Zurada IM (1992) Introduction to artificial neural systems. West Publishing Company, St. Paul, p 324

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanics, Technical University of Koszalin, 15-17 Raclawicka St., 75-620, Koszalin, Poland

    Stanisław Duer & Konrad Zajkowski

Authors
  1. Stanisław Duer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Konrad Zajkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stanisław Duer.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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 & Applic 23, 2185–2197 (2013). https://doi.org/10.1007/s00521-012-1169-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-1169-x

Keywords

Search

Navigation