The transition to renewable fuels necessitates reliable diagnostic tools for monitoring engine health and lubricant stability. This study introduces an integrated methodology that combines vibration signature analysis with oil degradation indices to evaluate the long-term performance of a single-cylinder diesel engine operated with Jatropha-based biodiesel blends (10%, 20%, and 30% v/v). Over 100 h (h) of operation, vibration data were recorded using fast Fourier transform analysis, while oil condition was monitored through viscosity, density, and infrared spectroscopy to quantify soot, oxidation, nitration, sulfation, and additive depletion. Statistical evaluation, including Pearson correlation, revealed strong interdependencies between vibration amplitude and chemical degradation markers, confirming a direct mechanical-chemical linkage. Among all blends, the 20% biodiesel blend exhibited the most favorable performance, showing the lowest vibration amplitude, minimal additive depletion, and stable physicochemical oil properties, thereby aligning closely with baseline diesel. These findings establish a novel dual-parameter diagnostic framework for predictive maintenance, offering significant potential for extending the lubricant life, reducing downtime, and supporting the adoption of sustainable biodiesel in compression ignition engines. To the best of our knowledge, this is the first study to directly correlate vibration signatures with FTIR-based oil degradation indices to establish a dual-parameter predictive diagnostic framework for biodiesel-fueled engines. The study also develops a regression-based predictive model (R 2 = 0.81, p < 0.05) linking vibration amplitude with oxidation and viscosity indices, establishing a foundation for real-time predictive maintenance applications.
Yadav et al. (Tue,) studied this question.