The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. This class of prognostic algorithms is termed effects-based, or Type III, prognostics. A unit-specific prognostic model, called the General Path Model, involve identifying an appropriate degradation measure to characterize the system's progression to failure. A functional fit of this parameter is then extrapolated to a pre-defined failure threshold to estimate the remaining useful life of the system or component. This paper proposes a specific formulation of the General Path Model with dynamic Bayesian updating as one effects-based prognostic algorithm. The method is illustrated with an application to the prognostics challenge problem posed at PHM '08.

Industrial components and systems undergo degradation in process operations. Prognostics and health management (PHM) is a process to assess and predict health conditions of components and can be applied to condition-based monitoring and maintenance. PHM is commonly utilized to analyze the health condition of a single lifecycle until failure. When maintenance occurs, degradation can be removed, and the PHM model can be restarted with new parameters related to the expected postmaintenance conditions. Maintenance actions, mostly imperfect repairs, may not entirely reset the condition to as good as new, and further degradation may occur at a higher rate. Maintenance repairs should be considered in prognostic models to predict component health more accurately. Furthermore, processes typically have more than one component that degrade and influence process measurements. The dependence of process measurements to multiple fault modes and related degradation can make individual component health monitoring complex. Commonly, faults and their related effects on process parameters must be isolated. In these cases, the diagnostics and prognostics framework should handle unsynchronized failure and maintenance reinitialization of different components for multiple fault processes. This research paper presents the Maintenance-Dependent Monitoring and Prognostics Model (MDMPM) to detect anomalies, decouple faults for different components, and predict future health conditions to calculate remaining useful life (RUL). The model is demonstrated with semisimulated nuclear power plant (NPP) data, with simultaneous condenser pump degradation and condenser tube fouling. The MDMPM shows a reliable prediction of RULs of NPP maintenance-dependent processes with interacting component degradation modes.

Specification of prior distribution is one of the most important methodological as well practical problems in Bayesian inference. Although a number of approaches have been proposed, none of them is completely satisfactory from both theoretical and practical points of view. We propose a new method to infer prior distribution from a priori information which may be available from observations. The method consists of specifying a predictive distribution of the value of interest and then working backwards towards the prior distribution on the parameters. The method requires the solution of the Fredholm integral equation of the first kind, which can be effectively approximated using Tikhonov regularization. Numerical examples for two cases of Bayesian inference are presented.

The article overviews current trends in research studies related to reliability prediction and prognostics. The trends are organized into three major types of prognostic models: failure data models, stressor models, and degradation models. Methods in each of these categories are presented and examples are given. Additionally, three particular computational prognostic approaches are considered; these are Markov chain-based models, general path models, and shock models. A Bayesian technique is then presented which integrates the prognostic types by incorporate prior reliability knowledge into the prognostic models. Finally, the article also discusses the usage of diagnostic/prognostic predictions for optimal control.

Doses from solar particle events can be a serious threat to the wellbeing of crews traveling through space. Therefore, methods for predicting the time such events will take place, methods for forecasting the dose buildup over time, and methods for forecasting the potential total dose from such events are needed to enable crews to take actions to mitigate the effects by entering a shielded area designed for their protection. This work focuses on forecasting the total dose expected for an event, based upon doses obtained very early in the event, using the kernel regression method. The model uses tables of calculated doses for historical solar particle events augmented with hypothetical events similar to the actual ones for training purposes. Reasonably accurate predictions of the total dose expected for an event can be made within the first hour after event onset. Predictive accuracies generally increase as the event progresses in time. The only inputs required are doses and times since event onset as provided by dosimetry devices. One hundred thirteen actual events with total doses between 1 and 1,000 cGy were tested using the model. At 1 hr into the event, total dose predictions were within ±30% of the actual total doses for 91 events (81%) and within ±15% for 54 of them (48%). Within the first 4 hr following event onset, total dose predictions were within ±30% for 98 events (87%) and within ±15% for 66 of them (58%). A software package implementing the model has been provided to the Space Radiation Analysis Group at NASA Johnson Space for incorporation into their operational procedures for analyzing possible threats to space crews from solar particle events.