Determination of the Course of Pressure in an Internal Combustion Engine Cylinder with the Use of Vibration Effects and Radial Basis Function – Preliminary Research

  • Piotr Czech
Part of the Communications in Computer and Information Science book series (CCIS, volume 329)


A huge development of new technologies applied in the automotive industry has been observed in recent years. It is visible both in designing, manufacturing and operating new means of transport. All over the world, a number of research institutions deal with such issues. Currently, there are many tests in progress on the development of internal combustion engines which are all mainly conditioned by ecological aspects. All that is aimed at designs which are the most eco-friendly and at the same time which have the best technological parameters. To maintain a proper functioning the presently produced engines are controlled with a lot of electronic sensors installed in the vehicle. In the test, an attempt was made to determine the course of internal combustion pressure in a cylinder on the basis of registered vibration signals. Radial neural networks taught using the data achieved from the decomposition with the application of a discrete wavelet transform were used in this test.


internal combustion engines pressure vibration artificial neural networks diagnostics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Czech, P., Łazarz, B., Wojnar, G.: Detection of local defects of gear teeth using artificial neural networks and genetic algorithms. ITE, Radom (2007)Google Scholar
  2. 2.
    Grega, R., Krajňák, J., Kaššay, P.: Impact of pneumatic flexible coupling on effective vibration value within the mechanical systems. Transactions of the Universities of Košice 3 (2009)Google Scholar
  3. 3.
    Heywood, J.B.: Internal combustion engines fundamentals. McGraw Hill Inc. (1988)Google Scholar
  4. 4.
    Homišin, J.: Tuning methods of mechanical systems by means of torsional oscillation tuner application. Pneumatyka  61(6) (2006)Google Scholar
  5. 5.
    Kneba, Z., Makowski, S.: Power supply and steering of the engines. WKiŁ, Warsaw (2004)Google Scholar
  6. 6.
    Liu, B.: Selection of wavelet packet basis for rotating machinery fault diagnosis. Journal of Sound and Vibration 284 (2005)Google Scholar
  7. 7.
    Madej, H.: Diagnosing the mechanical defects in internal combustion engines masked by electronic steering devices. ITeE, Radom (2009)Google Scholar
  8. 8.
    Mikulski, J.: The Possibility of Using Telematics in Urban Transportation. In: Mikulski, J. (ed.) TST 2011. CCIS, vol. 239, pp. 54–69. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Młyńczak, J.: Analysis of Intelligent Transport Systems (ITS) in Public Transport of Upper Silesia. In: Mikulski, J. (ed.) TST 2011. CCIS, vol. 239, pp. 164–171. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Osowski, St.: Neural networks for information processing. House of University of Technology in Warsaw (2000)Google Scholar
  11. 11.
    Peng, Z.K., Chu, F.L.: Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing 18 (2004)Google Scholar
  12. 12.
    Tadeusiewicz, R., Lula, P.: Introductin to neural networks. StatSoft, Cracow (2001)Google Scholar
  13. 13.
    Wajand, J.A.: Piston internal combustion engines of average rotational speed and high-speed. WNT, Warsaw (2005)Google Scholar
  14. 14.
    Wendeker, M.: Steering of ignition in car engine. On-board diagnostic systems of car vehicles. Lublin (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Piotr Czech
    • 1
  1. 1.Faculty of TransportSilesian University of TechnologyKatowicePoland

Personalised recommendations