Maintenance optimization using probabilistic cost-benefit analysis

Over the recent decades, plant maintenance strategies have evolved from a corrective to a preventive approach. Also, deterministic models have been increasingly replaced by those based on reliability and risk, which are probabilistic. Approaches to obtaining the optimum maintenance interval have typically involved minimization of the total associated cost. The present work demonstrates an improved technique involving the maximization of reliability-based benefit-to-cost ratio (BCR), i.e., the ratio of potential monetary benefit that can accrue from an optimized preventive maintenance (PM) schedule to the costs incurred in implementing such a schedule. It is shown that the methodology can be used to optimize the PM schedule for process units whose reliability function is either exponential or follows a Weibull distribution. A sensitivity analysis has also been performed to demonstrate the effect of various model parameters on the benefit-to-cost ratio. The proposed approach constitutes an improvement over the cost minimization methodology reported in contemporary literature, and can even be extended to plant shutdown planning.