Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

Abstract

Prognostic health management technologies are expected to play a vital role in the deployment and safe, cost-effective operation of advanced reactors by providing the technical means for lifetime management of significant passive components and reactor internals. This report describes a Bayesian methodology for the prediction of remaining life of materials and passive AR components. This approach, previously applied to predict time-to-failure of materials subjected to localized aging and degradation, is adapted for component-level prognostics. The Bayesian framework for component-level prognostics incorporates the ability to fuse information from multiple sources, including information on localized degradation and component-level condition indicators. The ability to switch between multiple models of degradation accumulation rate and/or multiple models of measurement physics becomes important in this context, and a reversible jump Markov chain Monte Carlo methodology has been developed for this purpose. Evaluations of the Bayesian framework and the model switching and selection methodology were performed using synthetic data as well as experimental measurements on a high-temperature creep testbed. Results to date indicate that the feasibility of the proposed Bayesian framework for prognostics, though an improvement over previous methods' accuracy, will require the ability to quantify sources of uncertainties within the various models used in the prognostic framework. Ongoing efforts are focused on sensing and measurement (particularly in-situ measurements) that would be applied as inputs to the prognostics framework, with the objective of identifying measurement methods that can provide early indicators of material degradation and quantifying the reliability and sensitivity of these measurement methods.

Experimental Design for Evaluating Selected Nondestructive Measurement Technologies - Advanced Reactor Technology Milestone: M3AT-16PN2301043

“…A number of technical requirements for PHM systems in AdvRx were identified based on these gaps, and technologies were developed to address several of these requirements. These technologies are documented in several previous reports in this series (Meyer et al 2013a;Meyer et al 2013c;Ramuhalli et al 2014b;Ramuhalli et al 2015).…”

“…Two laboratory-scale testbeds were fabricated previously for the purpose of generating specimens for ex-situ measurements and for in-situ measurement assessments at elevated temperatures (up to 650°C). These testbeds, described in previous reports (for instance, Ramuhalli et al 2015), were adapted to provide the necessary capability for testing and evaluation of NDE sensors at elevated temperatures under load. These are briefly described below.…”

Status Report on Regulatory Criteria Applicable to the Use of Digital Twins

“…Diagnostics and prognostics provide the technical means for enhancing affordability and safe operation of ARs over their lifetime by enabling lifetime management of significant passive components and reactor internals [24]. Here, diagnostics refers to the ability to determine the presence and cause of any specific condition or quantity of interest, whereas prognostics refers to the prediction of the expected level of change in this condition over time [25].…”

“…Here, diagnostics refers to the ability to determine the presence and cause of any specific condition or quantity of interest, whereas prognostics refers to the prediction of the expected level of change in this condition over time [25]. All systems for diagnostics and prognostics (often referred to as prognostic health management [PHM] systems) have several technical elements, including [24] "(1) sensors for performing measurements of both process parameters, as well as indicators of degradation; (2) diagnostic algorithms that use the sensor measurements to estimate the condition of the component; (3) prognostics algorithms to calculate the RUL [remaining useful life] of the component with degradation; and (4) interfaces to decision and control systems that are used to make O&M decisions." While the use of DTs for diagnostics and prognostics are a relatively recent phenomenon, these systems have always required robust models that enable diagnostic and prognostic algorithms to perform their function.…”

From Nondestructive Testing to Prognostics: Revisited

“…In the nuclear power field these technologies are being customized for each class of advanced small modular reactor (AdvSMR) and the modalities account for the specific operational history of the unit. Such information, when integrated with plant control systems and risk monitors, helps control O&M costs by enabling lifetime management of significant passive components, relieving the cost and labor burden of currently required periodic in-service inspection, and informing operators of O&M decisions in real-time to target maintenance activities [29]. One current open issue is that there is no regulatory relief from currently mandated NDE inspection when SHM is deployed.…”