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A Study on the Classification of Steam Generator Tube Defects Using an Improved Feature Extraction  

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.29, no.1, 2009 , pp. 27-35 More about this Journal
Abstract
In this paper, we study the classification of steam generator tube defects using an improved feature extraction. We consider 4 axisymmetric defect patterns of tube: I-In type, I-Out type, V-In type, and V-Out type. Through numerical analysis program based on finite element modeling, 400 ECT signals are generated by varying width and depth of each defect type. From those generated ECT signals, we propose new feature vectors that include an angle between the two points where the Maximum impedance and half the Maximum impedance, and angles between Maximum impedance point and 10%, 20%, 30%, 40% of Maximum impedance points. Also, multi-layer perceptron with one hidden layer is used to classify the defect patterns. Through the computer simulation study, it is shown that the proposed method achieves an improved defect classification performance in terms of Maximum Error and mean square Error.
Keywords
Eddy Current Testing(ECT); Steam Generator(SG) Tube; Neural Network; Feature Extraction;
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Times Cited By KSCI : 1  (Citation Analysis)
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