Condition-based multi-component maintenance decision support under degradation uncertainties

Multi-component maintenance optimization is a well-studied area for age-based failure models but in contrast, incorporation of condition-based maintenance (CBM) is still an open area of research. Taking advantage of condition monitoring information for updating components’ health conditions demands a dynamic short-term approach when grouping multiple activities subject to CBM policy. Degradation models are commonly utilized in CBM for predicting the future condition of a given component to decide appropriate maintenance actions where inherent uncertainties exist in the degradation processes. There are a limited number of works in literature that account for degradation uncertainties where maintenance cost is a function of such uncertainty. This paper aims to develop a maintenance decision support for a multi-component system by incorporating CBM while considering the degradation uncertainties. In this paper, a two-stage stochastic programming is proposed to address such an issue and the problem is formulated for situations where maintenance opportunities are limited due to practical constraints (e.g., remote offshore maintenance operations of wind farms, unmanned platforms in oil and gas industries, etc.). The concept of marginal cost is used in developing the equation of optimality. This is a combinatorial problem and becomes intractable when the number of components is large therefore a heuristic is proposed to reduce the problem size which reduces the required computational time substantially. It is shown that significant cost savings are possible, especially, when the downtime cost and common setup cost are significant. A numerical example is provided with a system of six components achieving above $$10\%$$ 10 % cost reduction when the degradation uncertainties are taken into account.