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.
