Addressing challenges posed by operational uncertainty, noise in signal acquisition, and limited real-time data, this chapter presents a hybrid multi-stage methodology for RUL prediction in control systems. The approach combines a variant of the unscented Kalman filter (UKF) with DBNs to improve uncertainty analysis in predicting nonlinear degradation processes. In the early stages, the dynamic UKF models estimate the distribution of random faults and process noise, match the system’s degradation stage, and acquire operational data. The degradation process is then optimized through cyclic iteration, calculating the system’s covariance and optimal estimates. This process compensates for inaccuracies in measuring real system degradation, enhancing the precision and robustness of RUL predictions. The methodology is validated through application to a subsea Christmas tree with electro-hydraulic compound control, demonstrating its effectiveness in improving RUL prediction under complex operational conditions.