Bayesian nonparametric analysis of single item preventive maintenance strategies

This work addresses the problem of finding the minimal-cost preventive maintenance schedule for a single item. We develop an optimization algorithm that reduces the computational effort to find the optimal schedule. This approach relies on the item having an increasing failure rate, which is typical, and employs a Gibbs sampling algorithm to simulate from the failure rate distribution using real data. We also analyze the case when the item has a "bathtub" failure rate; we develop techniques that lead to an algorithm that finds an optimal schedule for this case as well. We then analyze the effectiveness of our approach on both artificial and real data sets from the South Texas Project nuclear power plant.