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Article

Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting

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Institute of Thermodynamics and Sustainable Propulsion Systems, Graz University of Technology, 8010 Graz, Austria
2
Know-Center GmbH, Research Center for Data-Driven Business & Big Data Analytics, 8010 Graz, Austria
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LEC GmbH, Large Engine Competence Center, 8010 Graz, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Alberto Borboni
Sensors 2022, 22(11), 4235; https://doi.org/10.3390/s22114235
Received: 2 May 2022 / Revised: 28 May 2022 / Accepted: 30 May 2022 / Published: 1 June 2022
(This article belongs to the Section Physical Sensors)
An optimal control of the combustion process of an engine ensures lower emissions and fuel consumption plus high efficiencies. Combustion parameters such as the peak firing pressure (PFP) and the crank angle (CA) corresponding to 50% of mass fraction burned (MFB50) are essential for a closed-loop control strategy. These parameters are based on the measured in-cylinder pressure that is typically gained by intrusive pressure sensors (PSs). These are costly and their durability is uncertain. To overcome these issues, the potential of using a virtual sensor based on the vibration signals acquired by a knock sensor (KS) for control of the combustion process is investigated. The present work introduces a data-driven approach where a signal-processing technique, designated as discrete wavelet transform (DWT), will be used as the preprocessing step for extracting informative features to perform regression tasks of the selected combustion parameters with extreme gradient boosting (XGBoost) regression models. The presented methodology will be applied to data from two different spark-ignited, single cylinder gas engines. Finally, an analysis is obtained where the important features based on the model’s decisions are identified. View Full-Text
Keywords: knock sensor; pressure sensor; virtual sensor; engine vibrations; combustion parameters; discrete wavelet transform; gradient boosting; explainable AI knock sensor; pressure sensor; virtual sensor; engine vibrations; combustion parameters; discrete wavelet transform; gradient boosting; explainable AI
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MDPI and ACS Style

Kefalas, A.; Ofner, A.B.; Pirker, G.; Posch, S.; Geiger, B.C.; Wimmer, A. Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting. Sensors 2022, 22, 4235. https://doi.org/10.3390/s22114235

AMA Style

Kefalas A, Ofner AB, Pirker G, Posch S, Geiger BC, Wimmer A. Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting. Sensors. 2022; 22(11):4235. https://doi.org/10.3390/s22114235

Chicago/Turabian Style

Kefalas, Achilles, Andreas B. Ofner, Gerhard Pirker, Stefan Posch, Bernhard C. Geiger, and Andreas Wimmer. 2022. "Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting" Sensors 22, no. 11: 4235. https://doi.org/10.3390/s22114235

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