Technical State Assessment of Charge Exchange System of Self-Ignition Engine, Based On the Exhaust Gas Composition Testing

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This paper presents possible use of results of exhaust gas composition testing of self - ignition engine for technical state assessment of its charge exchange system under assumption that there is strong correlation between considered structure parameters and output signals in the form of concentration of toxic compounds (ZT) as well as unambiguous character of their changes. Concentration of the analyzed ZT may be hence considered to be symptoms of engine technical state. At given values of the signals and their estimates it is also possible to determine values of residues which may indicate a type of failure. Available tool programs aimed at analysis of experimental data commonly make use of multiple regression model which allows to investigate effects and interaction between model input quantities and one output variable. Application of multi-equation models provides great freedom during analysis of measurement data as it makes it possible to simultaneously analyze effects and interaction of many output variables. It may be also implemented as a tool for preparation of experimental material for other advanced diagnostic tools such as neural networks which, in contrast to multi-equation models, make it possible to recognize a state at multistate classification and - in consequence - to do diagnostic inference. Here , these authors present merits of application of the above mentioned analytical tools on the example of tests conducted on an experimental engine test stand.

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  • 1. Korbicz J. Kościelny J.M. Kowalczuk Z. Cholewa W.: Diagnostics of processes. Models. Artificial intelligence methods. Applications. (in Polish) Warsaw WNT 2002.

  • 2. Kropiwnicki J. Kneba Z.: Carbon dioxide potential reduction using Start-Stop system in a car. Key Engineering Materials Vol. 597 (2014) s. 185-192.

  • 3. Kufel T.: Econometrics. Solving Problems Using GRETL Software in Polish Polish Scientific Publishers PWN Warszawa. 2007.

  • 4. Kukiełka L.: Basics of Engineering Research in Polish Polish Scientific Publishers PWN Warszawa 2002.

  • 5. Markowski J. Pielecha J. Jasiński R. Kniaziewicz T. Wirkowski P.:Development of alternative ship propulsion in terms of exhaust emissions. 1st International Conference on the Sustainable Energy and Environment Development (SEED 2016) E3S Web of Conferences 10 00140 (2016)

  • 6. Piaseczny L. Zadrąg R.: The influence of selected damages of engine SI type on the changes of emission of exhaust gas components Diesel Engines Opole 2009.

  • 7. Polański Z.: Design of Experiments in Technology Polish Scientific Publishers PWN Warszawa 1984.

  • 8. Rudnicki J. Zadrąg R.: Problems of modelling toxic compounds emitted by a marine internal combustion engine for the evaluation of its structure parameters. Combustion Engines No.3/2015(162) ISSN 2300-9896 Poznań 2015.

  • 9. Skoundrianos E.N. Tzafestas S.G.: Fault diagnosis via local neural networks. Mathematics and Computers in Simulation 60 (2002) 169-180. Elsevier Science 2002.

  • 10. Tadeusiewicz R.: Neural networks (in Polish). Warszawa Akademicka Oficyna Wydawnicza RM 1993.

  • 11. Zadrąg R.: Criteria for the selection of the diagnostic parameter for diagnosis of marine diesel engine LOGISTYKA No. 4/2010 ISSN 1231-5478 Poznań 2010.

  • 12. Zadrąg R.: The Multi-equational models of leakproofness of charge exchange system of ship engine (in Polish) in monograph ‘Gaseous engines - selected issues’’ edited by Adam Dużyński University of Czestochowa Publishing ISBN 978-83-7193-461-2 ISSN 0860-501. Częstochowa 2010.

  • 13. Zadrąg R. et al.: Identification models for the technical condition of the engine on the basis of exhaust component emissions in Polish The report of the research project no. 4T12D 055 29 AMW Gdynia 2008.

  • 14. Zadrąg R. Zellma M.: The usage of multi-equation models in analysis of dynamic process in marine diesel engine research. JOURNAL OF POLISH CIMAC Vol.7 No 1 ISSN 1231-3998 str. 295-304 Gdańsk 2012.

  • 15. STATISTICA Neural NetworksTM. Prze-wodnik problemowy. StatSoft Krakow 2001.

  • 16. Neural Network Toolbox™. Matlab. User’s guide. The MathWorks Inc. 2014.

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