An Online Fault Diagnosis Method for Nuclear Power Plant Based on Combined Artificial Neural Network

Author(s):  
Ren Yu ◽  
Feng Liu
2005 ◽  
Vol 127 (3) ◽  
pp. 230-236 ◽  
Author(s):  
Min-Rae Lee ◽  
Joon-Hyun Lee ◽  
Jung-Teak Kim

The analysis of acoustic emission (AE) signals produced during object leakage is promising for condition monitoring of the components. In this study, an advanced condition monitoring technique based on acoustic emission detection and artificial neural networks was applied to a check valve, one of the components being used extensively in a safety system of a nuclear power plant. AE testing for a check valve under controlled flow loop conditions was performed to detect and evaluate disk movement for valve degradation such as wear and leakage due to foreign object interference in a check valve. It is clearly demonstrated that the evaluation of different types of failure modes such as disk wear and check valve leakage were successful by systematically analyzing the characteristics of various AE parameters. It is also shown that the leak size can be determined with an artificial neural network.


2010 ◽  
Vol 39 (1/2/3) ◽  
pp. 159 ◽  
Author(s):  
Chun Ling Xie ◽  
Jen Yuan Chang ◽  
Xiao Cheng Shi ◽  
Jing Min Dai

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