Application of Used Oil Analysis for Predictive Maintenance of Diesel Engines in Power and Energy Systems

The reliability of diesel engines in power and energy systems depends largely on effective lubrication and timely detection of mechanical degradation. This study investigates the application of used oil analysis (UOA) as a predictive maintenance tool for diesel generators operating under continuous and variable load conditions. The objective was to establish correlations between oil condition indicators such as viscosity, Total Base Number (TBN), oxidation, nitration, and wear metal concentration and the operational health of engines. Oil samples were collected from four diesel engines (200–500 kVA) over 500 operational hours and analyzed according to ASTM D445, D664, D5185, and D2272 standards. Results revealed that viscosity increased by an average of 18%, while TBN decreased by 45% during service, correlating strongly with increased iron and copper concentrations indicative of wear. Predictive modeling demonstrated that oil degradation trends could forecast component failures (e.g., piston ring or bearing wear) up to 150 hours before occurrence. These findings confirm that routine UOA significantly enhances reliability, reduces unplanned downtime, and optimizes oil drain intervals, providing a cost-effective framework for predictive maintenance in diesel-powered energy systems.