Impact of few failure data on the opportunistic replacement policy for multi-component systems

In continuous operating units, the production loss is often very large during the system shut down. Their economic profitability is conditioned by the implementation of suitable maintenance policy that could increase the availability and reduce the operating costs. In this paper, an opportunistic replacement policy is proposed for multi-component series system in the context of data uncertainty, where the expected total cost per unit time is minimized under general lifetime distribution. When the system is down, either correctively or preventively, the opportunity to replace preventively non-failed components is considered. To deal with the problem of the small size of failure data samples, the Bootstrap technique is applied, in order to model the uncertainties in parameter estimates. The Weibull parameters are considered as random variables rather than just deterministic point estimates. A solution procedure based on Monte Carlo simulations with informative search method is proposed and applied to the optimization of preventive maintenance plan for a hydrogen compressor in an oil refinery.