Analysis of lubrication oil towards maintenance grouping for multiple equipment using fuzzy cluster analysis

Abstract

Maintenance of similar multiple equipment is challenged by the complexities brought by respective maintenance needs and intervals for each equipment. Therefore, maintenance scheduling and planning becomes expensive and time intense, affecting productivity and profitability of the plant. Organizations are embracing the need to enhance maintenance planning by evaluating equipment characteristics which potentially offer benefits from reduction in maintenance costs and downtime to avoiding of unplanned shutdowns and efficiency maximization. To address this need, this study proposes a methodology that groups equipment with similar characteristics picked from lubricant analysis using fuzzy cluster analysis. Grouped equipment tend to require similar corrective and preventive maintenance (PM) actions enhancing maintenance planning and equipment availability. To validate this framework, lubricant analysis data for seventeen medium speed engines (MSE) of a thermal power plant is utilized where the derived clusters are subsequently used to group the engines. The framework offers benefits towards reduction of maintenance cost, improved planning and overall availability of the plant and equipment.