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Prediction of Defect Size of Steam Generator Tube in Nuclear Power Plant Using Neural Network  

Han, Ki-Won (Department of Electrical Engineering, Soongsil University)
Jo, Nam-Hoon (Department of Electrical Engineering, Soongsil University)
Lee, Hyang-Beom (Department of Electrical Engineering, Soongsil University)
Publication Information
Journal of the Korean Society for Nondestructive Testing / v.27, no.5, 2007 , pp. 383-392 More about this Journal
Abstract
In this paper, we study the prediction of depth and width of a defect in steam generator tube in nuclear power plant using neural network. To this end, we first generate eddy current testing (ECT) signals for 4 defect patterns of SG tube: I-In type, I-Out type, V-In type, and V-Out type. In particular, we generate 400 ECT signals for various widths and depths for each defect type by the numerical analysis program based on finite element modeling. From those generated ECT signals, we extract new feature vectors for the prediction of defect size, which include the angle between the two points where the maximum impedance and half the maximum impedance are achieved. Using the extracted feature vector, multi-layer perceptron with one hidden layer is used to predict the size of defects. Through the computer simulation study, it is shown that the proposed method achieves decent prediction performance in terms of maximum error and mean absolute percentage error (MAPE).
Keywords
Eddy Current Testing (ECT); Steam Generator (SG); Neural Network; Multi-layer Perceptron; Feature Extraction;
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Times Cited By KSCI : 1  (Citation Analysis)
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