| Title |
Performance improvement of Classification of Steam Generator Tube Defects in Nuclear Power Plant Using Neural Network |
| Authors |
조남훈(Jo, Nam-Hoon) ; 한기원(Han, Ki-Won) ; 송성진(Song, Sung-Jin) ; 이향범(Lee, Hyang-Beom) |
| Keywords |
Eddy Current Testing (ECT) ; Steam Generator (SG) ; Neural Network ; Pattern Recognition ; Feature Extraction ; Back-propagation |
| Abstract |
In this paper, we study the classification of defects at steam generator tube in nuclear power plant using eddy current testing (ECT). We consider 4 defect patterns of SG 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. In order to improve the classification performance, we propose new feature extraction technique. After extracting new features from the generated ECT signals, multi-layer perceptron is used to classify the defect patterns. Through the computer simulation study, it is shown that the proposed method achieves 100% classification success rate while the previous method yields 91% success rate. |