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Using multiple linear regression to predict engine oil life.

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This study predicts engine oil viscosity using FTIR spectroscopy and multiple linear regression. The developed model accurately estimates oil lifetime, aiding in predictive maintenance for diesel engines.

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Area of Science:

  • Tribology and Lubrication Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Engine oil viscosity is critical for lubrication and performance.
  • Predicting engine oil degradation is essential for maintenance and efficiency.
  • Fourier Transform Infrared (FTIR) spectroscopy offers a non-destructive method for oil analysis.

Purpose of the Study:

  • To develop a predictive model for engine oil viscosity at 100°C.
  • To utilize FTIR spectral data and key oil parameters for viscosity prediction.
  • To estimate the remaining useful life of engine oil in diesel engines.

Main Methods:

  • Multiple linear regression and Bayesian Model Averaging (BMA) for model development.
  • Stepwise regression for variable selection from FTIR-derived parameters.
  • Analysis of used motor oil samples (n=221) using standardized laboratory methods.
  • Pre-processing of FTIR spectra including baseline correction, normalization, and noise filtering.

Main Results:

  • A robust regression model was established to predict viscosity at 100°C.
  • Key predictors included Total Base Number (TBN), fuel content, oxidation, sulphation, and Anti-wear Particles (APP).
  • The model achieved a Root Mean Squared Error (RMSE) of 0.287, indicating high accuracy.

Conclusions:

  • FTIR spectroscopy combined with regression modeling provides an effective method for predicting engine oil viscosity.
  • The developed model can accurately estimate engine oil lifetime, supporting predictive maintenance strategies.
  • This approach is particularly valuable for diesel engines operating under severe conditions.