TAKING DECISIONS IN THE DIAGNOSTIC INTELLIGENT SYSTEMS ON THE BASIS INFORMATION FROM AN ARTIFICIAL NEURAL NETWORK
This paper presents a method to control an operation process of a complex technical object, with the use of trivalent diagnostic information. Also, a general diagram of the complex technical object was presented, and its internal structure was described. A diagnostic analysis was conducted, as a result of which sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was
a functional and diagnostic model, which constituted the basis for initial diagnostic information, which is provided by the sets of information concerning the elements of the basic modules and their output signals. The theoretical results obtained in the present study were verified in practice on the example of a complex and reparable technical object. It belongs to the group of technical equipment for which a short time of shutdowns is required (an ineffective use of the object).
2. A. Birolini, “Reliability Engineering Theory and Practice”. Springer, New York. P. 221 (1999).
3. B. Buchannan, E. Shortliffe, “Rule – Based expert systems,” Addison – Wesley Publishing Company, p. 387, (1985).
4. S. Duer: “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, (2010).
5. S. Duer, R. Duer, “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, (2010).
6. S. Duer, “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, (2012).
7. S. Duer, ”Intelligent system of supporting the process renewal of operating characteristics in complex technical objects”. Technical University of Koszalin, Koszalin, p. 242, (2012).
8. S. Duer, “Examination of the reliability of a technical object after its regeneration in a maintenance system with an artificial neural network,” Neural Computing & Applications, vol. 21, No. 3, pp. 523-534, (2012).
9. S. Duer, K. Zajkowski, “Taking decisions in the expert intelligent system to support maintenance of a technical object on the basis information from an artificial neural network”. Neural Computing & Applications. Vol. 23, No. 7, pp. 2185-2197, (2013).
10. S. Duer, K. Zajkowski, R. Duer, J. Paś, “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, (2013).
11. S. Duer, “Applications of an artificial intelligence for servicing of a technical object”. Neural Computing & Applications. Vol. 22 No. 5 pp. 955-968, (2013).
12. S. Duer S., R. Duer, S. Mazuru, “Determination of the expert knowledge base on the basis of a functional and diagnostic analysis of a technical object”. Nonconventional Technologies Review, 2016 Romanian Association of Nonconventional Technologies, Romanian, June, 2016, 6/2016 Vol. XX, NR.2, pp. 23-29.
13. W. Kacalak, M. Majewski, “New Intelligent Interactive Automated Systems for Design of Machine Elements and Assemblies,” 19th International Conference on Neural Information Processing - ICONIP 2012, Doha, Qatar, 12-15 November 2012. Lecture Notes in Computer Science 7666, Part IV. Springer, 115-122, (2012).
14. W. Kacalak, M. Majewski, “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, (2012).
15. W. Kacalak, M. Majewski, “New Intelligent Interactive Automated Systems for Design of Machine Elements and Assemblies”.
19th International Conference on Neural Information Processing - ICONIP 2012, Doha, Qatar, 12-15 November 2012. Lecture Notes in Computer Science 7666, Part IV. Springer. 115-122, (2012).
16. S. Kobayashi, K. Nakamura, "Knowledge compilation and refinement for fault diagnosis", IEEE Expert, October, pp. 39-460, (2011).
17. D. Lipinski, M. Majewski, “System for Monitoring and Optimization of Micro- and Nano-Machining Processes Using Intelligent Voice and Visual Communication”.14th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2013, 20-23 October 2013, Hefei, Anhui, China. Lecture Notes in Computer Science Vol. 8206, Springer 2013. 16-23, (2013).
18. M. Madan, M. Gupta, J. Liang, Homma N. “Static and Dynamic Neural Networks, From Fundamentals to Advanced Theory”. John Wiley & Sons, Inc, Hoboken, New Jersey, p. 718, (2003).
19. T. Nakagawa, "Maintenance Theory of Reliability". Springer – Verlag London Limited, p. 264, (2005).
20. T. Nakagawa, K. Ito, "Optimal inspection policies for a storage system with degradation at periodic tests". Math. Comput. Model. Vol. 31, pp. 191-195, (2000).
21. Z. Palkova, I. Okenka, "Programovanie". Slovak University of Agriculture in Nitra, p. 203, (2007).
22. L. Pokoradi, “Logical Tree of Mathematical Modeling”. Theory and Applications of Mathematics & Computer Science 5 (1) pp.
23. L. Pokoradi, “Failure Probability Analysis of Bridge Structure Systems”, 10th Jubilee IEEE International Symposium on Applied Computational Intelligence and Informatics. May 21-23, Timişoara, Romania, (2015).
24. L. Pokorádi, S. Duer, “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.
25. A. Rosiński, “Design of the electronic protection systems with utilization of the method of analysis of reliability structures,” Nineteenth International Conference On Systems Engineering, ICSEng, Las Vegas, USA (2008).
26. A. Rosiński, “Reliability analysis of the electronic protection systems with mixed – three branches reliability structure,” Reliability, Risk and Safety. Theory and Applications, vol. 3. Editors: R. Bris, C. Guedes Soares & S. Martorell. CRC Press/Balkema, London, UK (2010).
27. D. Waterman, "A guide to export systems". Addison – Wesley Publishing Company, p. 545, (1986).
28. K. Zajkowski, “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, (2014).
29. I.M. Zurada, "Introduction to Artificial Neural Systems," West, (2007).