Quantifying the Benefit of Prognostic Information in Maintenance Decision Making

Many models and methodologies to predict the remaining useful life (RUL) of a component or system are investigated nowadays. However, decision making based on these predictions (RUL) is still an underexplored area in maintenance management. The objective of this paper is to quantify the added value of this prognostic information (RUL). This is done by constructing a stochastic discrete-event simulation model, which optimizes maintenance action scheduling, based on prognostic information on the different components. Cost and availability criteria are taken into account in this optimization model as the objectives. The added value of the prognostic information is determined by comparing this prognostic maintenance policy to four other conventional maintenance policies: corrective maintenance, preventive maintenance, offline condition-based maintenance and online condition-based maintenance. The benefit of prognostic information and the stochastic discrete-event simulation model are validated by a real life case study on bearings of manufacturing equipment. Looking at more than one machine, a plant level approach is taken in the case study.