A LUBRICANT CONDITION MONITORING APPROACH FOR MAINTENANCE DECISION SUPPORT - A DATA EXPLORATORY CASE STUDY

Abstract

Lubricant condition monitoring is a Condition Based Maintenance (CBM) technique which indicates the state or condition of the equipment, the lubricant and any contamination noted within the lubricant. Failure of the various lubricant parameters translates to loss of lubrication capability which consequently leads to wear of the equipment and catastrophic failure of critical rotating elements if intervention is not made. Trend analysis on used oil data has been in use for a long period of time, but the results from the analysis is seldom used for robust maintenance decision support. The aim of this study is to analysefailure patterns within the lubricant parameters , and embedded in data of used oil analysis, where a case study of a thermal power plant is evaluated. In this study, a methodology is adopted starting with correlation analysis performed on the parameters of the used oil with a view of evaluating the associtations or relationships where such relations are validated from both literature and expert assessment. Next,trend analysis is performed where the parameters of the used oil are compared against thresholds recommended in industry standards and in practice,to expose the performance of various parameters. Next, from the trend and correlation analysis, failure patterns embedded in the used oil data were mapped alongside failure events recorded in maintenance databases, from which useful decision support was derived regarding feasible associations between degradation of oil parameters and occurence of specific failure events. This methodology demonstrates that correlations between deterioration of common lubricant parameters vis a vis equipment failure events can be better monitored and predicted. This could enhance the maintenance decision making through averting impending equipment failures or assist in better maintenance planning. This will invariably reduce downtime and improve operational efficiency of technical systems.