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

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This study explores using engine knock sensor vibrations to virtually estimate combustion parameters like peak firing pressure. This offers a cost-effective alternative to traditional pressure sensors for engine control.

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Area of Science:

  • Internal Combustion Engine Research
  • Automotive Engineering
  • Signal Processing

Background:

  • Optimal engine control requires monitoring combustion parameters like peak firing pressure (PFP) and 50% mass fraction burned crank angle (MFB50).
  • Traditional methods rely on intrusive, costly pressure sensors with uncertain durability.
  • Developing non-intrusive sensing methods is crucial for advanced engine management systems.

Purpose of the Study:

  • To investigate the feasibility of a virtual sensor approach for estimating key combustion parameters using engine vibration signals.
  • To develop and validate a data-driven methodology for real-time combustion monitoring.
  • To reduce reliance on expensive and potentially unreliable in-cylinder pressure sensors.

Main Methods:

  • Utilized vibration signals from a knock sensor (KS) as input data.
  • Applied discrete wavelet transform (DWT) for signal preprocessing and feature extraction.
  • Employed extreme gradient boosting (XGBoost) regression models for predicting PFP and MFB50.
  • Validated the approach on data from two distinct spark-ignited, single-cylinder gas engines.

Main Results:

  • Successfully demonstrated the capability of vibration signals to predict critical combustion parameters.
  • Identified informative features derived from DWT analysis crucial for regression accuracy.
  • The data-driven virtual sensor approach showed promising results for engine control applications.
  • Feature importance analysis provided insights into the relationship between vibration signals and combustion characteristics.

Conclusions:

  • A virtual sensor based on knock sensor vibration signals, processed with DWT and XGBoost, can effectively estimate engine combustion parameters.
  • This non-intrusive method presents a viable, cost-effective alternative to traditional pressure sensors for closed-loop engine control.
  • The findings support the integration of vibration-based sensing for enhanced engine efficiency, reduced emissions, and improved diagnostics.