Bagging 방법을 이용한 원전SG 세관 결함패턴 분류성능 향상기법

Classification Performance Improvement of Steam Generator Tube Defects in Nuclear Power Plant Using Bagging Method

  • 이준표 (숭실대 공대 전기공학부) ;
  • 조남훈 (숭실대 공대 전기공학부)
  • 발행 : 2009.12.01

초록

For defect characterization in steam generator tubes in nuclear power plant, artificial neural network has been extensively used to classify defect types. In this paper, we study the effectiveness of Bagging for improving the performance of neural network for the classification of tube defects. Bagging is a method that combines outputs of many neural networks that were trained separately with different training data set. By varying the number of neurons in the hidden layer, we carry out computer simulations in order to compare the classification performance of bagging neural network and single neural network. From the experiments, we found that the performance of bagging neural network is superior to the average performance of single neural network in most cases.

키워드

참고문헌

  1. E.E. Kriezis, T.D. Tsiboukis, S.M. Panas, 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 Trans. Magnetics, vol. 36, no. 5, pp. 3131-3133, 2000 https://doi.org/10.1109/20.908709
  3. 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 Trans. Magnetics, vol. 42, no. 4. pp. 1079-1082, 2006 https://doi.org/10.1109/TMAG.2006.870967
  4. H. Fukutomi, T. Takagi, J. Tani. M. Hashimoto, J. Shirnone, and Y. Harada, 'Numerical evaluation of ECT impedance signal due to minute cracks,' IEEE Trans. Miagnetics, vol. 33, no. 2, part 2, pp. 2123-2126, 1997 https://doi.org/10.1109/20.582750
  5. C. Gangzhu, A. Yamaguchi, and K. Miya, 'A novel signal processing technique for eddy-current testing of steam generator tubes,' IEEE Trans. Magnetics. vol. 34, no. 3, pp. 642-648, 1998 https://doi.org/10.1109/20.668059
  6. 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. 2000 https://doi.org/10.1016/S0963-8695(99)00046-8
  7. H. Haoyu. and T. Takagi, 'Inverse analyses for natural and multicracks using signals from a differential transmit-receive ECT probe,' IEEE Trans. Magnetics, vol. 38, no. 2, part 1, pp, 1009-1012, 2002 https://doi.org/10.1109/20.996259
  8. 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
  9. N.H. Jo and H.B. Lee, 'A novel feature extraction for eddy current testing of steam generator tubes,' NDT & E International, vol. 42, pp. 658-663, 2009 https://doi.org/10.1016/j.ndteint.2009.05.006
  10. M.P, Perrone, and L.N. Cooper, 'When networks disagree: Ensemble methods for hybrid networks,' artificial neural networks for speech and vision, 1993
  11. S. Haykin, Neural Networks. New Jersey:Prentice-Hall. 1999
  12. L. Breiman, 'Bagging predictors.' Machine Learning, vol. 24, no. 2, pp. 123-140, 1996 https://doi.org/10.1023/A:1018054314350
  13. D. Opitz and R. Maclin. 'Popular ensemble methods : an empirical study,' Journal of Artificial Intelligence Research, vol. 11, pp, 169-198, 1999
  14. L.I. Kuncheva, Combining Pattern Classifiers, John Wiley & Sons Inc, 2004