Archives of Acoustics, 43, 3, pp. 385–395, 2018

Vibroacoustic Real Time Fuel Classification in Diesel Engine

Kielce University of Technology,

Michał KEKEZ
Kielce University of Technology

Kielce University of Technology

University of Žilina

Five models and methodology are discussed in this paper for constructing classifiers capable of recognizing in real time the type of fuel injected into a diesel engine cylinder to accuracy acceptable in practical technical applications. Experimental research was carried out on the dynamic engine test facility. The signal of in-cylinder and in-injection line pressure in an internal combustion engine powered by mineral fuel, biodiesel or blends of these two fuel types was evaluated using the vibro-acoustic method. Computational intelligence methods such as classification trees, particle swarm optimization and random forest were applied.
Keywords: fuel recognition; classification trees; particle swarm optimization; random forest
Full Text: PDF


Albarbar A., Gu F., Ball A.D., Starr A. (2010), Acoustic monitoring of engine fuel injection based on adaptive filtering techniques, Applied Acoustics, 71, 12, 1132–1141.

Ambrozik A., Ambrozik T., Kurczyński D., Łagowski P. (2014), The influence of injection advance angle on fuel spray parameters and nitrogen oxide emissions for a self-ignition engine fed with diesel oil and FAME, Polish Journal of Environmental Studies, 23, 6, 1917–1923.

Barelli L., Bidini G., Buratti C., Mariani R. (2009), Diagnosis of internal combustion engine through vibration and acoustic pressure non-intrusive measurements, Applied Thermal Engineering, 29, 1707–1713.

Bąkowski A., Radziszewski L. (2015), Determining selected diesel engine combustion descriptors based on the analysis of the coefficient of variation of in-chamber pressure, Bulletin of the Polish Academy of Sciences: Technical Sciences, 63, 2, 457–464.

Breiman L. (2001), Random forests, Machine Learning, 45, 1, 5–32.

Brzozowski K., Nowakowski J. (2014), Model for calculating compression ignition engine performance, Maintenance and Reliability, 16, 3, 407–414.

Chiatti G. et al. (2015), Diagnostic methodology for internal combustion diesel engines via noise radiation, Energy Conversion and Management, 89, 34–42.

Delvecchio S., Bonfiglio P., Pompoli F. (2018), Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques, Mechanical Systems and Signal Processing, 99, 1, 661–683.

Deptuła A., Kunderman D., Osiński P., Radziwanowska U., Włostowski R. (2016), Acoustic diagnostics applications in the study of technical condition of combustion engine, Archives of Acoustics, 41, 2, 345–350.

Elghamry M.H. et al. (1998), Gaseous fuel quality identification for a spark ignition gas engine using Acoustic Emission analysis, [In:] 11th International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management COMADEM 1998, pp. 235–244.

Engelbrecht A.P. (2006), Fundamentals of computational swarm intelligence, John Wiley & Sons.

Figlus T. et al. (2014), Condition monitoring of engine timing system by using wavelet packet decomposition of an acoustic signal, Journal of Mechanical Science and Technology, 28, 5, 1663–1671.

Flekiewicz M., Flekiewicz B., Fabiś P. (2007), Engine block vibration level as a tool for fuel recognition, SAE Technical Paper, No. 2007-01-2162.

Grajales J.A., Quintero H.F., López J.F., Romero C.A., Henao E., Cardona O. (2017), Engine diagnosis based on vibration analysis using different fuel blends, Diagnostyka, 18, 4, 27–36.

Gravalos I. et al. (2013), Detection of fuel type on a spark ignition engine from engine vibration behaviour, Applied Thermal Engineering 54, 171–175. doi: 10.1016/j.applthermaleng.2013.02.003.

Hardenberg H.O., Hase F.W. (1979), An empirical formula for computing the pressure rise and delay of a fuel from its cetane number and from the relevant parameters of direct-injection diesel engines, SAE Technical Paper, No. 790493.

Kekez M., Radziszewski L. (2011), Modelling of pressure in the injection pipe of a diesel engine by computational intelligence, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 225, 12, 1660–1670.

Kekez M., Radziszewski L., Sapietova A. (2016), Fuel type recognition by classifiers developed with computational intelligence methods using combustion pressure data and the crankshaft angle at which heat release reaches its maximum, Procedia Engineering, 136, 353–358.

Lowe D.P., Lin T.R., Wu W., Tan A.C.C. (2011), Diesel knock combustion and its detection using acoustic emission, Journal of Acoustic Emission, 29, 78–88.

Maurya R.K. et al. (2013), Digital signal processing of cylinder pressure data for combustion diagnostics of HCCI engine, Mechanical Systems and Signal Processing, 36, 95–109.

Morón-Villarreyes J.A., Soldi C., de Amorim A.M., Pizzolatti M.G., de Mendonça Jr, A.P., D’Oca M. G. (2007), Diesel/biodiesel proportion for by-compression ignition engines, Fuel, 86, 12–13, 1977–1982.

Payri F., Luján J.M., Martín J., Abbad A. (2010), Digital signal processing of in-cylinder pressure for combustion diagnosis of internal combustion engines, Mechanical Systems and Signal Processing, 24, 1767–1784.

Pietraszek J., Gądek-Moszczak A., Radek N. (2014), The estimation of accuracy for the neural network approximation in the case of sintered metal properties, Studies in Computational Intelligence, 513, 125–134.

Ranachowski Z., Bejger A. (2005), Fault diagnostics of the fuel injection system of a medium power maritime diesel engine with application of acoustic signal, Archives of Acoustics, 30, 4, 465–472.

Ruiz F.A., Isaza C.V., Agudelo A.F., Agudelo J.R. (2017), A new criterion to validate and improve the classification process of LAMDA algorithm applied to diesel engines, Engineering Applications of Artificial Intelligence, 60, 117–127.

Sakthivel G. (2016), Prediction of CI engine performance, emission and combustion characteristics using fish oil as a biodiesel at different injection timing using fuzzy logic, Fuel, 183, 214–229.

Szymański G.M., Tomaszewski F. (2016), Diagnostics of automatic compensators of valve clearance in combustion engine with the use of vibration signal, Mechanical Systems and Signal Processing, 68–69, 479–490.

Teixeira L.S.G. et al. (2008), Multivariate calibration in Fourier transform infrared spectrometry as a tool to detect adulterations in Brazilian gasoline, Fuel, 87, 346–352.

Valencia F.A., Armas I.P. (2005), Ignition quality of residual fuel oils, Journal of Maritime Research, 2, 3, 77–96.

Witten I.H., Frank E. (2005), Data mining, Morgan Kaufmann.

Yoon M. et al. (2007), A method for combustion phasing control using cylinder pressure measurement in a CRDI diesel engine, Mechatronics, 17, 9, 469–479.

DOI: 10.24425/123910

Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN)