Abstract
As the licenses of many nuclear power plants in the US and abroad are being extended, accurate knowledge of system and component condition is becoming increasingly important. The US Department of Energy (DOE) has funded a project with the primary goal of developing lifecycle prognostic methods to generate accurate and continuous Remaining Useful Life (RUL) estimates as components transition through unique stages of the component lifecycle. Specific emphasis has been placed on creating and transitioning between three distinct stages of operational availability. These stages correspond to Beginning Of Life (BOL) where little or no operational information is available, early onset operations at various expected and observed stress levels where there is the onset of detectable degradation, and degradation towards the eventual End Of Life (EOL). This paper provides an application overview of a developed lifecycle prognostic approach and applies it to a heat exchanger fouling test bed under accelerated degradation conditions resulting in an increased understanding of system degradation. Bayesian and Bootstrap Aggregation methods are applied to show improvements in RUL predictions over traditional methods that do not utilize these methods, thereby improving thelifecycle prognostic model for the component. The analyses of results from applying these lifecycle prognostic algorithms to a heat exchanger fouling experiment are detailed.
Highlights
The field of prognostics for system reliability focuses on the determination of both component and system health and Remaining Useful Life (RUL) to provide safety, reliability, and financial benefits
A primary goal of prognostic models is to lessen plant down time and the resulting loss of revenue, which can lead to a reduction in the number of total nuclear power plant shutdowns (Meyer, Bond, & Ramuhalli, 2012)
While it was previously discussed that both linear and quadratic fits were used for the General Path Model (GPM), initial modeling attempts revealed that using a quadratic parametric fit is more accurate than using a linear parametric fit; results will be confined to quadratic fit models
Summary
The field of prognostics for system reliability focuses on the determination of both component and system health and RUL to provide safety, reliability, and financial benefits. A primary goal of prognostic models is to lessen plant down time and the resulting loss of revenue, which can lead to a reduction in the number of total nuclear power plant shutdowns (Meyer, Bond, & Ramuhalli, 2012) To this end, the development of online prognostic models estimating RUL of components can lead to more efficient maintenance scheduling, and when used for on-line monitoring, can reduce sudden loss of operations from unexpected component failure (Coble, Ramuhalli, Bond, Hines, & Upadhyaya, 2012). Between 2008 and 2010, the North American Electric Reliability Corporation (NERC) stated that condenser associated performance issues were responsible for the removal of over 9.1 million megawatt hours from the energy grid (Fayard, 2011) The paper ends by presenting the results of a developed lifecycle prognostic model and concluding remarks
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