Journal
SENSORS
Volume 22, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/s22114235
Keywords
knock sensor; pressure sensor; virtual sensor; engine vibrations; combustion parameters; discrete wavelet transform; gradient boosting; explainable AI
Funding
- Graz University of Technology
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The study investigates the potential of using a virtual sensor based on vibration signals acquired by a knock sensor for controlling the combustion process. A data-driven approach utilizing discrete wavelet transform as a preprocessing step and extreme gradient boosting regression models for regression tasks of combustion parameters is introduced. The methodology will be applied to data from two different spark-ignited, single cylinder gas engines, with analysis to identify important features based on the model's decisions.
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.
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