Improved Heat Exchanger Lifecycle Prognostic Methods for Enhanced Light Water Reactor Sustainability

Zach Welz, Alan Nam, Michael Sharp, J Wesely Hines, and Belle Upadhyaya
Publication Target: 
IJPHM
Publication Issue: 
Special Issue Nuclear Energy PHM
Submission Type: 
Full Paper
Supporting Agencies (optional): 
DOE, LRF
AttachmentSizeTimestamp
ijphm_15_018.pdf1.59 MBJune 15, 2015 - 9:17am

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 the lifecycle prognostic model for the component. The analyses of results from applying these lifecycle prognostic algorithms to a heat exchanger fouling experiment are detailed.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
018
Page Count: 
13
Submission Keywords: 
Condition Monitoring; Prognostics; Heat Exchanger Fouling; Bagging; Bayesian Updating; Empirical Methods
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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