PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Classification of elements in the diagnostic model of a technical object for building an expert knowledge base

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The following paper presents the problem of classification (identification) elements in the internal structure of a technical object. This problem is directly linked with diagnostics and compilation of an expert data base. The basis of a process of grouping elements into classes is to make a diagnostic model of a given object in a form of a structure or set of basic elements of an object. In order to conduct the grouping of elements into subsets of s-th classes, the following paper compiles and presents analytical formulas and classification rules. Theoretical considerations presented in this paper are also verified using an engine control system as an example of a complex technical object.
Rocznik
Strony
71--78
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
autor
  • Faculty of Mechanical Engineering, Department of Energy Engineering , Koszalin University of Technology, 15-17 Raclawicka St., 75-620 Koszalin, Poland
autor
  • Faculty of Mechanical and Safety Engineering, Institute of Mechatronics and Vehicle Engineering, Óbuda University, Budapest, H-1081 Budapest, Népszinház u. 8
  • Faculty of Electronics and Informatics, Department of Informatics, Koszalin University of Technology, 2 Śniadeckich St., 75-453 Koszalin, Poland
autor
  • Faculty of Electronics and Informatics, Department of Informatics, Koszalin University of Technology, 2 Śniadeckich St., 75-453 Koszalin, Poland
Bibliografia
  • 1. Będkowski, L., Dąbrowski, T. (2006). The Basis of Exploitation, Part II: The Basis of Exploational Reliability. Military University of Technology, Warsaw, p. 243.
  • 2. Birolini A. (1999). Reliability Engineering Theory and Practice. Springer, New York. p. 221.
  • 3. Christer A.H. (2002). A review of delay time analysis for modelling plant maintenance. In: Osaki S (ed) Stochastic Models in Reliability and Maintenance. Springer, New York. pp. 89–123.
  • 4. Duer S. (2009). Artificial Neural Network-based technique for operation process control of a technical object. Defence Science Journal, Vol. 59, No. 3, pp. 305-313.
  • 5. Duer S. (2010). Diagnostic system with an artificial neural network in diagnostics of an analogue technical object. Neural Computing & Applications, Vol. 19, No. 1, pp. 55-60.
  • 6. Duer S. (2010). Diagnostic system for the diagnosis of a reparable technical object, with the use of an artificial neural network of RBF type. Neural Computing & Applications, Vol. 19, No. 5, pp. 691-700.
  • 7. 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 Computing & Applications, Vol. 19, No. 5, pp. 755-766.
  • 8. Duer S. (2010). Expert knowledge base to support the maintenance of a radar system. Defence Science Journal, Vol. 60, No. 5, pp. 531-540.
  • 9. Duer S. (2011). Modelling of the operation process of repairable technical objects with the use information from an artificial neural network. Expert Systems With Applications. 38, pp. 5867-5878.
  • 10. 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 Computing & Applications. Vol. 21, No. 1, pp. 153-160.
  • 11. Duer S., Zajkowski K.: Duer R., Paś J. (2013). Designing of an effective structure of system for the maintenance of a technical object with the using information from an artificial neural network. Neural Computing & Applications. Vol. 23, No. 3-4, pp. 913-925.
  • 12. Duer S., Duer R., Mazuru S. (2016). Determination of the expert knowledge base on the basis of a functional and diagnostic analysis of a technical object. Romanian Association of Nonconventional Technologies, 6/2016 Vol. XX, Nr.2, pp. 23-29.
  • 13. Dhillon B.S. (2006). Applied Reliability and Quality, Fundamentals, Methods and Procedures. Springer – Verlag London Limited, p. 186.
  • 14. Hojjat A., Shih – Lin hung. (1995). Machine learning, neural networks, genetic algorithms and fuzzy systems. John Wiley End Sons, Inc, Hoboken, New Jersey, p. 398.
  • 15. Ito K., Nakagawa T. (2000). Optimal inspection policies for a storage system with degradation at periodic tests. Math and Comput Model 31:191–195.
  • 16. Kacalak W., Majewski M., Zurada J. M. (2010). Intelligent e-learning systems for evaluation of user's knowledge and skills with efficient information processing. International Conference on Artificial Intelligence and Soft Computing - ICAISC 2010, Zakopane, Poland, 13-17 June 2010. Lecture Notes in Artificial Intelligence 6114. Springer, pp. 508-515.
  • 17. Kacalak W., Majewski M. (2012). Effective Handwriting Recognition System using Geometrical Character Analysis Algorithms.19th International Conference on Neural Information Processing - ICONIP 2012, Doha, Qatar, 12-15 November 2012. Lecture Notes in Computer Science 7666, Part IV. Springer. pp. 248-255.
  • 18. Kaczorek T. (1994). Matrices in automation and electrical engineering. WNT, Warszawa, p. 254.
  • 19. Kobayashi S., Nakamura K. (2011). Knowledge compilation and refinement for fault diagnosis, IEEE Expert, October, pp. 39-460.
  • 20. Madan M. Gupta, Liang Jin and Noriyasu Homma (2003). Static and Dynamic Neural Networks, From Fundamentals to Advanced Theory. John Wiley End Sons, Inc, Hoboken, New Jersey, p. 718.
  • 21. Mathirajan M., Chandru V., Sivakumar A.I. (2007). Heuristic algorithms for scheduling heat-treatment furnaces of steel casting industries. Sadahana, Vol. 32, Part 5, pp. 111-119.
  • 22. Nakagawa T. (2005). Maintenance Theory of Reliability. Springer – Verlag London Limited, p. 264.
  • 23. Palkova Z., Okenka I. (2007). Programovanie. Slovak University of Agriculture in Nitra, p. 203.
  • 24. Palkova Z. (2010). Modeling the optimal capacity of an irrigation system using queuing theory. Warszawa: Warsaw University of Life Sciences Press. No. 55, pp. 5-11.
  • 25. Pokorádi L., Duer S. (2016). Investigation of maintenance process with Markov matrix. Proceedings of the 4th International Scientific Conference on Advances in Mechanical Engineering. 13-15 October 2016, Debrecen, Hungary, pp. 402-407.
  • 26. Rosiński A. (2010). Reliability analysis of the electronic protection systems with mixed – three branches reliability structure. „Reliability, Risk and Safety. Theory and Applications. Volume 3”. Editors: R. Bris, C. Guedes Soares & S. Martorell. CRC Press/Balkema, London, UK.
  • 27. Rosiński A. (2012). Reliability analysis of the electronic protection systems with mixed m–branches reliability structure. „Advances in Safety, Reliability and Risk Management”. Editors: Berenguer, Grall & Guedes Soares. Taylor & Francis Group, London, UK.
  • 28. 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. International Journal of Production Research. Vol.No. 7, pp. 272-276.
  • 29. Waterman D. (1986). A guide to export systems. Addison – Wesley Publishing Company, p. 545.
  • 30. Zajkowski K. (2014). The method of solution of equations with coefficients that contain measurement errors, using artificial neural network. Neural Computing and Applications, Vol. 24, no. 2, pp. 431-439.
  • 31. Zurada I. M. (1992). Introduction to Artificial Neural Systems. West Publishing Company, St. Paul, MN p. 324.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-208322a8-464e-423e-9ff0-5d423509bba2
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.