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Safety, mission, and infrastructure critical systems are adopting prognostics and health management, a discipline consisting of technologies and methods to assess the reliability of a product in its actual life-cycle conditions to determine the advent of failure and mitigate system risks. The output from a prognostic system is the remaining useful life of the system; it gives the decision-maker lead-time and flexibility to manage the health of the system. This paper develops a decision support model based on options theory, a financial derivative tool extended to real assets, to valuate maintenance decisions after a remaining useful life prediction. We introduce maintenance options, and develop a hybrid methodology based on Monte Carlo simulations and decision trees for a cost-benefit-risk analysis of prognostics and health management. We extend the model, and combine it with least squares Monte Carlo methods to valuate one type of maintenance options, the waiting options; their value represents the cost avoidance opportunities and revenue obtained from running the system through its remaining useful life. The methodologies in this paper address the fundamental objective of system maintenance with prognostics: to maximize the use of the remaining useful life while concurrently minimizing the risk of failure. We demonstrate the methodologies on decision support for sustaining wind turbines by showing the value of having a prognostics system for gearboxes, and determining the value of waiting to perform maintenance. The value of the waiting option indicates that having the system available throughout the predicted remaining useful life is more beneficial than having downtime for maintenance, even if there is a high risk of failure.