Diagnostics and Prognostics Tools for Assessing Remaining Useful Life of Nuclear Power Plant Materials
In recent years, there has been renewed interest in expanding the use of nuclear power to provide sustainable, carbon-free energy. 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. To enable longer term operation, a range of advanced diagnostics and prognostics methods that are suitable for on-line, continuous, in-plant monitoring over extended time periods (months to years) are necessary. A central issue in life extension for the current fleet of LWRs is the early detection and monitoring of materials degradation. Material aging and degradation due to stresses and irradiation is a critical element in assessing potential for the failure of components in legacy nuclear power plants. A related issue is the ability to estimate remaining useful life (RUL) of components and systems based on condition assessment or degradation information. Detection of early stage damage in materials and assessment of remaining life is important in proactive or prognostic-based life management of legacy nuclear power plants. These approaches go beyond what is currently included in "condition-based maintenance," this strategy can potentially improve safety and reduce costs by detecting damage and scheduling appropriate maintenance/mitigation strategies early in the component lifecycle. For early detection of degradation, novel nondestructive (i.e., without destroying the utility of the specimen) tests that are suitable for continuous monitoring over extended time periods are needed, as are new techniques for data integration. The challenge of predicting remaining life starting from earlier phases of degradation is also largely unsolved and will require new prognostics tools. This paper will discuss the development and application of advanced diagnostics and prognostics tools to the life extension problem. The focus of these activities will be on ferritic and stainless steels, and degradation mechanisms common to an LWR operating environment in these materials. The use of micromagnetic measurements to assess early stages of degradation will be discussed. The measurements will be used, along with models of degradation accumulation over time, to assess the remaining useful life using a Bayesian prognostic algorithm. The primary advantage of a Bayesian approach is the ability to incorporate a priori information to constrain the solution of the problem and mitigate uncertainties in the measurements. Further, a Bayesian approach enables the incorporation of additional information as it becomes available, which can be used to update, and refine, the RUL estimates. Results of the proposed techniques on experimentally acquired measurements will be presented in the full paper.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1113623
- Report Number(s):
- PNNL-SA-78162
- Resource Relation:
- Conference: The Applied Systems Health Management Conference (MFPT 2011): Enabling Sustainable Systems, May 10-12, 2011, Virginia Beach, Virginia
- Country of Publication:
- United States
- Language:
- English
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