Lifecycle Prognostic Algorithm Development and Application to Test Beds

Abstract

On-line monitoring of nuclear plant system degradation is quickly becoming a crucial consideration as the licenses of many nuclear power plants are being extended. Accurate measurement of the current degradation of system components and structures is important for correct estimates of their remaining useful life (RUL). The propagation of the uncertainty involved in both the measurements and model construction of these system components is vital for finding the uncertainty of the overall system RUL calculation.
Prognostic methods should seamlessly operate from beginning of component life (BOL) to end of component life (EOL). We term this "Lifecycle Prognostics." When a component is put into use, the only information available may be past failure times, and the predicted failure distribution can be estimated with reliability methods such as Weibull Analysis (Type I). As the component operates, it begins to consume its available life. This life consumption may be a function of system stresses, and the failure distribution should be updated (Type II). When degradation becomes apparent, this information can be used again to improve the failure distribution estimate (Type III). Current research typically focuses on developing methods for the three types of prognostics. This research focused on developing a framework using Bayesian methods to transition between prognostic model types and update failure distribution estimates as new information becomes available.
This paper will present methods developed that integrate models from the three prognostics categories into a single prognostic system to estimate RUL over the life of the component: Lifecycle Prognostics. The methods will also be validated on a range of test beds.