![]() As part of these activities in the USA, there are major initiatives focused on "life extension" for existing light-water nuclear power reactors (LWR) from 60 to 80 (or 100) years. In recent years, there has been renewed interest in expanding the use of nuclear power to provide sustainable, carbon-free energy. The resulting lifecycle prognostics algorithm uses all available information throughout the component lifecycle. The general approach to applying the Bayesian method to lifecycle prognostics consisted of identifying the prior, which is the RUL estimate and uncertainty from the previous prognostics type, and combining it with observational data related to the newer prognostics type. Fundamentally Bayesian analysis updates a prior belief with new data to get a posterior belief. However, as more data are collected, the data will be weighted more heavily and will eventually swamp out the prior in calculating the posterior distribution of model parameters. If the variance of the prior is small compared to the uncertainty of the data, the prior will be weighed more heavily. The weightings of the prior belief and information contained in the sampled data are dependent on the variance (uncertainty) of the prior, the variance (uncertainty) of the data, and the amount of measured data (number of samples). The use of a prior also means that information is conserved when new data are available. Bayesian methods are best used when limited data are available. As you operate it, you may collect information related to its condition that will allow you to update your estimated failure time. For example, when you purchase a component you have a prior belief, or estimation, of how long it will operate before failing. Bayesian methods, as opposed to classical frequency statistics, show how an expected value, a priori, changes with new data to form a posterior distribution. This employs established Type I, II, and III prognostic methods, and Bayesian transitioning between each Type. To address these issues a "Lifecycle Prognostics" method was developed to create RUL distributions from Beginning of Life (BOL) to End of Life (EOL). Without enough degradation data leading to failure, prognostic models can yield RUL distributions with large uncertainty or mathematically unsound predictions. One major bottleneck for data-driven prognostics is the availability of run-to-failure degradation data. Calculated risk would decrease, saving money by avoiding unnecessary maintenance. From a reliability and maintenance standpoint, there would be improved safety by avoiding all failures. The ultimate goal of prognostics is to provide an accurate assessment for RUL predictions, with as little uncertainty as possible. The developed methods were then validated on a range of accelerated degradation test beds. This research focused on developing prognostics algorithms for the three types of prognostics, developing uncertainty quantification methods for each of the algorithms, and, most importantly, developing a framework using Bayesian methods to transition between prognostic model types and update failure distribution estimates as new information becomes available. When degradation becomes apparent, more ยป this information can be used to again improve the RUL estimate (Type III Prognostics). This life consumption may be a function of system stresses, and the failure distribution should be updated to account for the system operational stress levels (Type II Prognostics). As the component operates, it begins to degrade and consume its available life. We term this "Lifecycle Prognostics." When a component is put into use, the only information available may be past failure times of similar components used in similar conditions, and the predicted failure distribution can be estimated with reliability methods such as Weibull Analysis (Type I Prognostics). Prognostic methods should seamlessly operate from beginning of component life (BOL) to end of component life (EOL). This research project developed and validated methods to perform RUL estimation throughout the lifecycle of plant components. The correct quantification and propagation of both the measurement uncertainty and model uncertainty is necessary for quantifying the uncertainty of the RUL prediction. Accurate prediction of the current degradation state of system components and structures is important for accurate estimates of their remaining useful life (RUL). On-line monitoring and tracking of nuclear plant system and component degradation is being investigated as a method for improving the safety, reliability, and maintainability of aging nuclear power plants.
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