Design of a tribotechnical diagnostics model for determining the technical condition of an internal combustion engine during its life cycle

• Autorzy: Furch Jan , Jelínek Josef

Identyfikatory

DOI

10.17531/ein.2022.3.5

• Języki publikacji: EN

Abstrakty

EN

The paper proposes a model of tribotechnical diagnostics, which allows us to determine the technical condition of an internal combustion engine within its life cycle and then take measures, including its decommissioning due to excessive wear of major components. The paper also focuses on tribodiagnostic methods that are suitable for assessing the technical condition of internal combustion engines used in various means of transport (automobiles, railway locomotives powered by internal combustion engines, aircraft powered by reciprocating internal combustion engines, special and garden equipment). An internal combustion engine from agricultural equipment was selected for the experiment and monitored throughout its life cycle. The paper describes in detail the appropriate methods used for the proposed tribotechnical diagnostics model, including the results from the measurements by these methods. The said methods were then evaluated and mutually compared. The following advanced instrumental analytical methods were used to evaluate the collected engine oil samples: atomic emission spectrometry (AES), ferrography, automatic laser counter and LNF particle classifier, FTIR infrared spectrometry. The result of the work (paper) is the design of a tribotechnical diagnostics model for determining the technical condition of an internal combustion engine during its life cycle and the determination of limit values for assessing the technical condition of a Honda GCV 165 internal combustion engine. The results are based on individual measurements.

Słowa kluczowe

EN

tribotechnical diagnostic; Fourier transform-infrared spectrometry; kinematic viscosity; atomic emission spectrometry; ferrography

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2022
• Tom: Vol. 24, no. 3
• Strony: 437--445
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Furch Jan

• University of Defence, Faculty of Military Technology, Kounicova str. 65, 662 10 Brno, Czech Republic

• https://orcid.org/0000-0002-7332-1580

autor

Jelínek Josef

• University of Defence, Faculty of Military Technology, Kounicova str. 65, 662 10 Brno, Czech Republic

• https://orcid.org/0000-0001-5533-6888

Bibliografia

• 1. Al-Ghouti MA, Al-Degs YS, Amer M. Application of chemometrics and FTIR for determination of viscosity index and base number of motor oils. Talanta, 2010; 81(3): 1096-1011, https://doi.org/10.1016/j.talanta.2010.02.003.
• 2. ASTM D445-19a, Standard Test Method for Kinematic Viscosity of Transparent and Opaque Liquids (and Calculation of Dynamic Viscosity); 2020. ASTM International.
• 3. Caneca AR, Pimentel MF, Galvão RKH, da Matta CE, de Carvalho FR, Raimundo IM, Pasquini C, Rohwedder JJR. Assessment of infrared spectroscopy and multivariate techniques for monitoring the service condition of diesel-engine lubricating oils. Talanta, 2016; 70(2): 344-352, https://doi.org/10.1016/j.talanta.2006.02.054.
• 4. Furch J, Glos J. Utilization of tribodiagnostics for the evaluation of technical condition of mechanical gearboxes. Iternational Conference Transport Means 2020; Kaunas: University of Kaunas, 255-259.
• 5. Glos J. Tribologic methods used for an engine diagnostics. International Conference Intelligent Technologies in Logistics and Mechatronics Systems ITELMS 2011, Kaunas: University of Kaunas, 9-13.
• 6. Glos J, Sejkorová M. Tribo-diagnostics as an indicator and input for the optimization of vehicles preventive maintenance. International Conference on Intelligent Technologies in Logistics and Mechatronics Systems ITELMS 2016, Medimond: 8.
• 7. Green DA, Lewis R. The effects of soot-contaminated engine oil on wear and friction: A review. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 2008; 222(9): 1669-1689, https://doi.org/10.1243/09544070JAUTO468.
• 8. Haaland DM, Thomas EV. Partial Least-Squares Methods for Spectral Analyses. 1. Relation to Other Quantitative Calibration Methods and the Extraction of Qualitative Information. Analytical Chemistry 1988; 60(11): 1193-1202, https://doi.org/10.1021/ac00162a020
• 9. Kemp AW, Siotani M, Hayakawa T, Fujikoshi Y. Modern Multivariate Statistical Analysis: A Graduate Course and Handbook. Biometrics 1987; 43(2): 479-480, https://doi.org/10.2307/2531832.
• 10. Král J, Konečný B, Král J, Madáč K, Fedorko G, Molnar V. Degradation and chemical change of longlife oils following intensive use in automobile engines. Measurement: Journal of the International Measurement Confederation 2014; 50(1): 34-42, https://doi.org/10.1016/j.measurement.2013.12.034.
• 11. Kučera M, Aleš Z, Pavlů J, Hnilicová M. Applying of automatic laser particle counter as technique to morphology assessment and distribution of wear particles during lifetime of transmission oils. In: Key Engineering Materials 2016; 669: 417-425, https://doi.org/10.4028/www.scientific.net/KEM.669.417.
• 12. Kumbár V, Glos J, Votava J. Monitoring of chemical elements during lifetime of engine oil. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 2014; 62(1): 155-159, https://doi.org/10.11118/actaun201462010155.
• 13. Ľubomír H, Juraj J, Jaromír M, Juraj T, Mirko S, Marcin Z, & Romana J. Design of laboratory test equipment for automotive oil filters to evaluate the technical life of engine oil. Applied Sciences 2021; 11(2): 483, https://doi.org/10.3390/app11020483.
• 14. Machalíková J, Sejkorová M, Livorová M, Krtička F. Assessment of Morphology of Wear Particles in Oils for Vehicles. Transactions on Transport Sciences 2008; 1(4): 185-192, https://doi.org/10.5507/tots.2008.024.
• 15. Macián V, Tormos B, Bastidas S, Pérez T. Improved fleet operation and maintenance through the use of low viscosity engine oils. Fuel economy and oil performance. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(2): 201-211, https://doi.org/10.17531/ein.2020.2.3.
• 16. Pinheiro CT, Rendall R, Quina MJ, Reis MS, Gando-Ferreira L. M. Assessment and prediction of lubricant oil properties using infrared spectroscopy and advanced predictive analytics. Energy and Fuels 2017; 31(1): 179-187, https://doi.org/10.1021/acs.energyfuels.6b01958.
• 17. Rodrigues J, Costa I, Farinha JT, Mendes M, Margalho L. Predicting motor oil condition using artificial neural networks and principal component analysis. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(3): 440-448, https://doi.org/10.17531/ein.2020.3.6.
• 18. Sejkorová M, Hurtová I, Jilek P, Novák M, Voltr O. Study of the effect of physicochemical degradation and contamination of motor oils on their lubricity. Coatings 2021; 11(1): 60, https://doi.org/10.3390/coatings11010060.
• 19. Sejkorová M, Kučera M, Hurtová I, Voltr O. Application of FTIR-ATR spectrometry in conjunction with multivariate regression methods for viscosity prediction of worn-out motor oils. Applied Sciences 2021; 11(9): 3842, https://doi.org/10.3390/app11093842.
• 20. Sejkorová M, Šarkan B, Caban J, Marczuk A. On relationship between infrared spectra of worn out engine oils and their kinematic viscosity. Przemysl Chemiczny 2018; 97(1): 49-54, https://sigma-not.pl/publikacja-111646-2018-1.html.
• 21. Soejima M. Characteristics of friction, wear and scuffing for cam and follower-investigating tribotechnology to improve performance and reliability for engines. Toraibarojisuto - Journal of Japanese Society of Tribologists 2019; 64(2).
• 22. Synák F, Kalašová A, Synák J. Air filter and selected vehicle characteristics. Sustainability 2020; 12(22): 9326, https://doi.org/10.3390/su12229326.
• 23. Toms A, Toms L. Oil Analysis and Condition Monitoring. In Chemistry and Technology of Lubricants. Dordrecht: Springer 2010; 459-495, https://doi.org/10.1023/b105569_16.
• 24. Vališ D, Gajewski J, Žák L. Potential for using the ANN-FIS meta-model approach to assess levels of particulate contamination in oil used in mechanical systems. Tribology International 2019; 135: 324-334, https://doi.org/10.1016/j.triboint.2019.03.012.
• 25. Vališ, D, Žák L, Vintr Z, Hasilova K. Mathematical analysis of soot particles in oil used as system state indicator. IEEE International Conference on Industrial Engineering and Engineering Management 2016: 486-490, https://doi.org/10.1109/IEEM.2016.7797923.
• 26. Varmuza K, Filzmoser P. Introduction to multivariate statistical analysis in chemometrics. CRC Press 2016: 336, https://doi.org/10.1201/9781420059496.
• 27. Wei L, Duan H, Jia D, Jin Y, Chen S, Liu L, Liu J, Sun X, Li J. Motor oil condition evaluation based on on-board diagnostic system. Friction 2020; 8(1): 95-106, https://doi.org/10.1007/s40544-018-0248-0.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).