Development of Maintenance Decision Guidelines from Used Oil Data of a Thermal Powerplant.

Abstract

Engine is the most critical operating mechanism in industrial machines. Its function is to transmit power to the parts of the machine. Diesel power plants utilize two or four stroke engines in their operations and needs to be lubricated with the appropriate lubricant to maintain their performance and reliability. The lubricant creates a thin film between the sliding surfaces thus reducing friction in the machine. Base oil and additives are two ingredients of the lubricant. The base oil gives lubrication to the moving parts to protect them from wear caused by friction while additives prevents deterioration of oil under extreme temperature. Condition monitoring in power plants arises from the fact that in a power plant unexpected fault or shutdown may result to fatal accident or huge loss of output. The recent development in computer and transducer technologies, signal processing and artificial–intelligence (AI) techniques has made it possible to implement condition based maintenance (CBM) more effectively. The objective of this study was to develop a maintenance decision guideline to enhance CBM in a power plant. A detailed analysis of used oil data was done. Viscosity, TBN, pentane insoluble, silicon and vanadium were the parameters analyzed using trend graphs which located their patterns of performance at different points of usage time. Parameter that exceeded threshold limits were marked for RCA analysis. Cause and effect analysis and the five whys were applied in the methodology to further investigate the reasons for oil degradation. Oil degradation as observed in the results occurred at 3000 hrs. Of engine run but control action was taken at 5000 hrs. Which marked the optimum useful life of the lubricant. Lubricant failure triggered mitigating action to be instituted. Combination of theory and experimental results was useful in developing maintenance decision guidelines to enhance CBM in the plant.