개선된 특징 추출을 이용한 원전SG 세관 결함 패턴 분류에 관한 연구

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)
  • 발행 : 2009.02.28

초록

본 논문에서는 개선된 특징추출을 이용한 원자력 발전소 증기발생기 세관의 결함 형태 분류에 대한 연구를 수행한다. 본 논문에서는 4가지 축대칭 결함, 즉 I-In 형태, I-Out 형태, V-In 형태, V-Out 형태 결함을 고려한다. 유한요소법에 기초한 수치해석 프로그램을 이용하여 결함의 폭과 깊이를 변화시켜가면서 400개의 와전류탐상시험(ECT) 신호를 생성하였다. 이와 같이 생성된 ECT 신호로부터 새로운 특징을 제안하였는데, 여기에는 최대 임피던스 값을 갖는 점과 최대 임피던스 값의 1/2의 값을 갖는 점 사이의 위상각과 최대임피던스 값을 갖는 점과 최대 임피던스 값의 10%, 20%, 30%, 40%를 갖는 점사이의 위상각들이 포함된다. 또한, 결함형태를 분류하기 위하여 은닉층이 하나인 다층퍼셉트론을 사용하였다. 컴퓨터 모의실험 연구를 통하여 제안된 방법이 최대오차와 평균제곱오차 측면에서 향상된 결함 분류 성능을 얻는다는 것을 보였다.

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.

키워드

참고문헌

  1. E. E. Kriezis, T. D. Tsiboukis, S. M. Panas and J. A. Tegopoulos, 'Eddy currents: theory and applications,' Proceedings of the IEEE, Vol. 80, No. 10, pp. 1559-1589, (1992) https://doi.org/10.1109/5.168666
  2. H. B. Lee and D H. Kim, 'Impedance calculation for a plate with crack in eddy current NDT using 3D indirect BIEM,' IEEE Transactions on Magnetics, Vol. 36, No. 5, pp. 3131-3133, (2000) https://doi.org/10.1109/20.908709
  3. K. Miya, 'Recent advancement of electromagnetic nondestructive inspection technology in Japan,' IEEE Transactions on Magnetics, Vol. 38, No. 2, pp. 321-326, (2002) https://doi.org/10.1109/20.996088
  4. M. Rebican, Z. Chen, N. Yusa, L. Janousek and K. Miya, 'Shape reconstruction of multiple cracks from ECT signals by means of a Stochastic method,' IEEE Transactions on Magnetics, Vol. 42, No. 4, pp. 1079-1082, (2006) https://doi.org/10.1109/TMAG.2006.870967
  5. H. Fukutomi, T. Takagi, J. Tani, M. Hashimoto, J. Shimone and Y. Harada, 'Numerical evaluation of ECT impedance signal due to minute cracks,' IEEE Trans. on Magnetics, Vol. 33, No. 2, Part 2, pp. 2123-2126, (1997) https://doi.org/10.1109/20.582750
  6. C. Gangzhu, A. Yamaguchi and K. Miya, 'A novel signal processing technique for eddy-current testing of steam generator tubes,' IEEE Trans. on Magnetics, Vol. 34, No. 3, pp. 642-648, (1998) https://doi.org/10.1109/20.668059
  7. S. J. Song and Y. K. Shin, 'Eddy current flaw characterization in tubes by neural networks and finite element modeling,' NDT & E International, Vol. 33, pp. 233-243, (2000) https://doi.org/10.1016/S0963-8695(99)00046-8
  8. 조남훈, 이향범, 한기원, 송성진 '신경회로망을 이용한 원전SG 세관 결함패턴 분류성능 향상기법', 전기학회논문지, Vol. 56, No. 7, pp. 1224-1230, July, (2007)
  9. H. Haoyu and T. Takagi, 'Inverse analyses for natural and multicracks using signals from a differential transmit-receive ECT probe,' IEEE Trans. on Magnetics, Vol. 38, No. 2, Part 1, pp. 1009-1012, (2002) https://doi.org/10.1109/20.996259
  10. M. Rebican, N. Yusa, Z. Chen, K. Miya, T. Uchimoto and T. Takagi, 'Reconstruction of multiple cracks in an ECT round-robin test,' International Journal of Applied Electromagnetics and Mechanics, Vol. 19, No. 1-4, pp. 399-404, (2004)
  11. M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, PWS Pub. Co, (1995)
  12. S. Haykin, Neural Networks, Prentice-Hall, New Jersey, (1999)