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A Study on Bagging Neural Network for Predicting Defect Size of Steam Generator Tube in Nuclear Power Plant  

Kim, Kyung-Jin (Department of Electrical Engineering, Soongsil University)
Jo, Nam-Hoon (Department of Electrical Engineering, Soongsil University)
Publication Information
Journal of the Korean Society for Nondestructive Testing / v.30, no.4, 2010 , pp. 302-310 More about this Journal
Abstract
In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.
Keywords
Eddy Current Testing(ECT); Steam Generator(SG); Bagging; Weighted Average Method; Single Neural Network(SNN); Bagging Neural Network(BNN);
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Times Cited By KSCI : 2  (Citation Analysis)
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