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Diagnostic system with an artificial neural network which determines a diagnostic information for the servicing of a reparable technical object

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Abstract

In this paper, the features of a diagnostic measurement setup have been determined. The purpose of the setup was registration of diagnostic signals taken from an electronically controlled gasoline-powered engine. Special attention has been paid to the presentation of the features of an analog–digital converter card used, as well as to the possibility of its practical use. Another important aspect is the design and programming of computer software dedicated for action along with the setup. The results of the work have been presented using the example of the engine’s microprocessor control module. 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.

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References

  1. Będkowski L, Dąbrowski T (2006) Podstawy eksploatacji cz. 2. Wyd. WAT, Warszawa, p 187 (in polish)

  2. Duer S (2004) The concept of assistant system for analogue class technical object servicing. In: Sixth international conference on unconventional elektromechanical and electrical system UEES′04. Alushta, The Crimea, Ukraine, pp 687–690

  3. Dhillon BS (2006) Applied reliability and quality, fundamentals, methods and procedures. Springer—Verlag London Limited, p 186

  4. Gercbach JB, Kordoński CB (1972) Modele niezawodności obiektów technicznych. WNT, Warszawa (in polish)

  5. Gniedenko BW, Bielajew JK, Sołowiew AD (1968) Metody matematyczne w teorii niezawodności. WNT, Warszawa (in polish)

  6. Nakagawa T (2005) Maintenance theory of reliability. Springer—Verlag London Limited, p 264

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

    Article  MATH  MathSciNet  Google Scholar 

  8. Duer S, Duer R, Duer P, Płocha I (2009) Measurement system for the diagnosis of analogue technical objects with the use of artificial neural networks. Academic Journals, Poznan University of Technology, s. Electrical Engineering, Published by Poznan University of Technology, No. (59), pp 61–72

  9. Spath H (1980) Cluster analysis for data reduction and classification of object. Ellis Horwood Ltd., Chichester, UK

    Google Scholar 

  10. Buchannan B, Shortliffe E (1985) Rule—based expert systems. Wesley Publishing Company, Addison, p 387

    Google Scholar 

  11. Hayer-Roth F, Waterman D, Lenat D (1983) Building expert systems. Wesley Publishing Company, Addison, p 321

    Google Scholar 

  12. Jackson P (1990) Introduction to expert systems. Wesley Publishing Company, Addison, p 526

    Google Scholar 

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

    Google Scholar 

  14. Sinha M, Gupta MM, Zadeh LA (1999) Soft-computing and intelligent control systems, theory and applications. Academic Press, New York, p 321

    Google Scholar 

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

    Google Scholar 

  16. Hojjat A, Shih-Lin Hung (1995) Machine learning, neural networks, genetic algorithms and fuzzy systems. John Wiley End Sons, Inc., p 398

  17. Gupta MM, Jin L, Noriyasu H (2003) Static and dynamic neural networks, from fundamentals to advanced theory. John Wiley End Sons Inc., Hoboken, New Jersey, p 718

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

    Article  Google Scholar 

  19. Zurada IM (1992) Introduction to artificial neural systems. West Publishing Company, St. Paul, p 263

    Google Scholar 

  20. Białko M (2000) Podstawowe właściwości sieci neuronowych i hybrydowych systemów ekspertowych. Wyd. Politechniki Koszalińskiej, Koszalin (in polish)

  21. Gupta MM (2001) Fuzzy tests, fuzzy logic and fuzzy systems, in encyclopedia of physical science and technology. Meyer RA (ed), Academic Press, San Diego, p 452

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Authors and Affiliations

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

    Stanisław Duer

  2. Departament of Electronics and Informatics, Technical University of Koszalin, 2 Sniadeckich St., 75-543, Koszalin, Poland

    Radosław Duer

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  1. Stanisław Duer
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  2. Radosław Duer
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Correspondence to Radosław Duer.

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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 & Applic 19, 755–766 (2010). https://doi.org/10.1007/s00521-009-0333-4

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  • DOI: https://doi.org/10.1007/s00521-009-0333-4

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