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Precision diagnostics of a diesel engine under agricultural tractor operating conditions

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a method for the precise diagnosis of a diesel engine in an agricultural tractor based on the analysis of efficiency changes and parameters characterizing the process of fuel-air mixture preparation. We proposed that the technical condition be identified based on available data from the engine controller, as this enables the implementation of precise online diagnostics of an agricultural tractor. The method was verified using the original cycle, during which we simulated several engine defects leading to a change in conditions and quality of the processes of creating and burning the fuel/air/flue gas mixture. In the paper, we justified the selection of the points at which the engine parameters were measured, as they provide the most information and allow for efficient identification of damage. These results indicate the possibility of damage identification without the use of the diagnostic cycle in the operation of operator-driven vehicles and autonomous vehicles.
Czasopismo
Rocznik
Strony
181--194
Opis fizyczny
Bibliogr. 20 poz.
Twórcy
  • Department of Vehicle and Machinery Construction and Operation, Faculty of Technical Sciences University of Warmia and Mazury in Olsztyn, Olsztyn 10719, Poland
  • Department of Vehicle and Machinery Construction and Operation, Faculty of Technical Sciences University of Warmia and Mazury in Olsztyn, Olsztyn 10719, Poland
  • Department of Vehicle and Machinery Construction and Operation, Faculty of Technical Sciences University of Warmia and Mazury in Olsztyn, Olsztyn 10719, Poland
Bibliografia
  • 1. Alvarez, I. & Bertrand, J.J. & Me´chineau, D. Automated diagnosis of engines of agricultural tractors. Proceedings of the International Conference on Artificial Intelligence for Agriculture and Food. 1993. P. 137-144.
  • 2. Alvarez, I. & Huet, S. Automatic diagnosis of engine of agricultural tractors: The BED experiment. Biosystems Engineering. 2008. Vol. 10(3). P. 362-369.
  • 3. Bieniek, A. Wewnątrzsilnikowe ograniczenie emisji substancji szkodliwych w silniku wyposażonym w układ EGR pojazdu pozadrogowego. Inżynieria Rolnicza, Agricultural Engineering. 2013. Vol. 2(143). P. 31-41. [In Polish: In-engine reduction of harmful emissions in an engine equipped with an EGR system of an off-road vehicle].
  • 4. Carrera González, A. & Alonso García, S. & Gómez Gil, J. Design, Development and Implemementation of a Steering Controller Box for an Automatic Agricultural Tractor Guidance System, Using Fuzzy Logic. Technological Developments in Education and Automation. 2009.
  • 5. Dou, D. Application of Diesel Oxidation Catalyst and Diesel Particulate Filter for Diesel Engine Powered Non-Road Machines. Platinum Metals Rev. 2012. Vol. 56(3). P. 144-154.
  • 6. Dyer, J. & Desjardins, R. Carbon Dioxide Emissions Associated with the Manufacturing of Tractors and Farm Machinery in Canada. Biosystems Eng. 2017. Vol. 93. P. 107-118.
  • 7. Gosala, D.B. & Allen, C.M. & Ramesh, A.K. & Shaver, G.M. & McCarthy, J. & Stretch, D. & Farrell, L. Cylinder deactivation during dynamic diesel engine operation. International Journal of Engine Research. 2017. Vol. 18(10). P. 991-1004.
  • 8. Grisso, R. & Pitman, R. Gear Up and Throttle Down-Saving Fuel. Virginia Cooperative Extension, Virginia Tech. 2001. P. 442-450.
  • 9. Grytsyuk, O. & Vrublevskyi, O. Investigations of diesel engine in the road test. Diagnostyka. 2018. Vol. 19(2). P. 89-94.
  • 10. Heywood, J.B. Internal Combustion Engines Fundamentals. Singapore, McGraw-Hill Book Co. 1998.
  • 11. Knefel, T. & Nowakowski J. Model-based analysis of injection process parameters in a common rail fuel supply system. Eksploatacja i Niezawodność - Maintenance and Reliability. 2020. Vol. 22(1). P. 94-101.
  • 12. Michalski, R. & Gonera, J. & Janulin, M. A simulation model of damage-induced changes in the fuel consumption of a wheeled tractor. Eksploatacja i Niezawodność - Maintenance and Reliability. 2014. Vol. 16(3). P. 452-457.
  • 13. Naji, A.A. A Comparison of Measured Diesel Emissions in Agriculture and Australian National Standard Emission Factors. Master Thesis. University of Southern Queensland Faculty of Engineering & Surveying. 2013.
  • 14. Napiórkowski, J. & Gonera, J. Analysis of failures and reliability model of farm tractors. Agricultural Engineering. 2020. Vol. 24(2). P. 89-101.
  • 15. Nuthall, K. Tractor comparisons. Farm Industry News 1. 2003.
  • 16. Okut, H. Bayesian Regularized Neural Networks for Small n Big p Data. Artificial Neural Networks - Models and Applications. 2016.
  • 17. Reif, K. Automotive Mechatronics. Automotive Networking, Driving Stability Systems, Electronics. Springer Fachmedien Wiesbaden. 2015. 549 p.
  • 18. Rovira Más, F. & Zhang, Q. & Hansenm, A.C. Mechatronics and Intelligent Systems for Off-Road Vehicles. Springer Fachmedien Wiesbaden. 2010.
  • 19. Rymaniak, Ł. & Lijewski, P. & Kamińska, M. & Fuć, P. & Kurc, B. & Siedlecki, M. & Kalociński, T. & Jagielski, A. The role of real power output from farm tractor engines in determining their environmental performance in actual operating conditions. Computers and Electronics in Agriculture. 2020. Vol. 173. P. 1-7.
  • 20. Vrublevskyi, O. Modelling of processes in electro-hydraulic valves of an engine’ fuel system. Mechanika. 2019. Vol. 25(2). P. 141-148.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6c868cd1-b18c-4add-96ad-0239bd00b39d
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