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This study introduces a dual-parameter diagnostic framework using vibration analysis and oil degradation indices to monitor diesel engine performance with Jatropha biodiesel blends. The 20% blend showed optimal performance, enabling predictive maintenance.

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Area of Science:

  • * Tribology and Lubrication Engineering
  • * Combustion Engines and Renewable Fuels
  • * Predictive Maintenance and Signal Processing

Background:

  • * Renewable fuel adoption requires robust engine and lubricant monitoring.
  • * Jatropha biodiesel blends present an alternative to conventional diesel.
  • * Existing diagnostic tools may not fully capture the interplay between mechanical stress and oil degradation.

Purpose of the Study:

  • * To develop and validate an integrated diagnostic methodology combining vibration analysis and oil degradation indices.
  • * To evaluate the long-term performance of a diesel engine using Jatropha biodiesel blends (10%, 20%, 30%).
  • * To establish a dual-parameter framework for predictive maintenance in biodiesel-fueled engines.

Main Methods:

  • * Vibration signature analysis using Fast Fourier Transform (FFT) over 100 hours of engine operation.
  • * Oil condition monitoring via viscosity, density, and Fourier Transform Infrared (FTIR) spectroscopy.
  • * Statistical analysis, including Pearson correlation, to link vibration data with oil degradation markers (soot, oxidation, nitration, sulfation, additive depletion).

Main Results:

  • * Strong correlations found between vibration amplitude and chemical oil degradation markers, indicating a mechanical-chemical linkage.
  • * The 20% Jatropha biodiesel blend demonstrated superior performance with minimal vibration and stable oil properties.
  • * A regression model (R² = 0.81, p < 0.05) was developed linking vibration amplitude to oxidation and viscosity indices.

Conclusions:

  • * The integrated diagnostic framework effectively correlates engine vibration with oil degradation for biodiesel blends.
  • * The 20% biodiesel blend offers a promising sustainable alternative with performance comparable to conventional diesel.
  • * This approach provides a foundation for real-time predictive maintenance, enhancing lubricant life and reducing engine downtime.