A Prognostic-Information-Based Order-Replacement Policy for a Non-Repairable Critical System in Service

This paper proposes a prognostic-information-based joint order-replacement policy for a non-repairable critical system in service. The primary difference from existing work is to take the online condition monitoring data into consideration during the joint decision-making process. Towards this end, the system's degradation trajectory is modeled by a Wiener process whose parameters are real-time estimated based on the newly obtained condition monitoring data by utilizing the expectation maximization algorithm and Bayesian inference. By doing so, the remaining useful life distribution of the system of interest can be predicted in real-time, which is then used as the prognostic information to dynamically update the optimal ordering and replacement times jointly. This process makes the jointly obtained order-replacement decisions rely on the prognostic information available from the system's degradation monitoring. Finally, a practical case study of the inertial navigation system in aircraft is provided to validate the proposed joint decision policy.