Eddy current flaw characterization in tubes by neural networks and finite element modeling

NDT and E International

Volumn 33, Issue 4, 2000, Pages 233-243

  Song, Sung Jin ^a   Shin, Young Kil ^b  

^a Sungkyunkwan University   (South Korea)

^b Kunsan National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

FINITE ELEMENT METHOD; NEURAL NETWORKS; PATTERN RECOGNITION; TUBES (COMPONENTS);

FLAW SIZING; STEAM GENERATOR TUBES;

EDDY CURRENT TESTING;

EID: 0033891170     PISSN: 09638695     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0963-8695(99)00046-8     Document Type: Article

References (25)

1. Singh G.P., Udpa S.S. The role of digital signal processing in NDT. NDT Int. 19:(3):1986;125-132.
2. Udpa L., Udpa S.S. Solution of inverse problems in eddy-current nondestructive evaluation (NDE). J Nondestructive Eval. 7:(1/2):1988;111-120.
3. Gramz M., Stepinski T. Eddy current imaging. Array sensors and flaw reconstruction. Res Nondestructive Eval. 5:1994;157-174.
4. Udpa S.S., Lord W. A Fourier descriptor classification scheme for differential probe signals. Mater Eval. 49:(9):1984;1136-1141.
5. Udpa L., Udpa S.S. Eddy current defect characterization using neural network. Mater Eval. 48:1990;342-347. 353.
6. Song S.J., Park H.J., Shin Y.K., Lee H.B. Flaw characterization in tubes by inversion of eddy current signal using neural networks trained by finite element model-based synthetic data. Thompson D.O., Chimenti D.E. Review of progress in quantitative nondestructive evaluation. 18A:1999;881-888 Plenum Press, New York.
7. Mucciardi A.N. Elements of learning control systems with applications to industrial processes. Proceedings of the IEEE Conference on Decision and Control, New Orleans, LA. 1972.
8. Mann J.M., Schmerr L.W., Moudler J.C. Neural network inversion of uniform field eddy current data. Mater Eval. 49:(1):1991;34-39.
9. Chapman C.E., Fahr A., Pelletier A., Hay D.R. Artificial intelligence in the eddy current inspection of aircraft engine components. Mater Eval. 49:(9):1991;1090-1094.
10. Forsyth D.S., Fahr A., Chapman C.E. An evaluation of artificial neural networks for the classification of eddy current signals. Thompson D.O., Chimenti D.E. Review of progress in quantitative nondestructive evaluation. 13A:1993;879-886 Plenum Press, New York.
11. Kang S.J. Fuzzy syntactic pattern recognition approach for extracting and classifying flaw pattern from an eddy-current signal waveform. J Electrical Engng Inf Sci. 2:(4):1997;59-65.
12. Ida N., Palanisamy R., Lord W. Eddy current probe design using finite element analysis. Mater Eval. 41:(11):1983;1389-1394.
13. 4 deposits in PWR steam generators. NDT Int. 18:(6):1985;331-338.
14. Shin YK. Numerical modeling of probe velocity effects for electromagnetic NDE. PhD Dissertation, Iowa State University, Ames, IA, 1992.
15. Specht D.F. Probabilistic neural networks for classification, mapping, or associative memory. Proceedings of the IEEE International Conference on Neural Networks. 1:1998;525-532.
16. Specht D.F. Probabilistic neural networks. Neural Networks. 3:1990;109-118.
17. Specht D.F. Probabilistic neural networks and polynomial adaline as complementary techniques for classification. IEEE Trans Neural Networks. 1:1990;111-121.
18. Specht D.F., Shapiro P.D. Generalization accuracy of probabilistic neural networks compared with back-propagation networks. Proceedings of the International Joint Conference on Neural Networks. 1:1991;887-892.
19. Rumelhart D.E., Hinton G.E., Williams R.J. Learning internal representations by error propagation. Rumelhart D.E., McClelland J.L. Parallel distributed processing. 1:1986;318-362 MIT Press, Cambridge, MA. Also edited by PDP Research Group.
20. Bishop C.M. Neural Networks for pattern recognition. 1995;Oxford University Press, Oxford, UK.
21. Burnett D.S. Finite element analysis. 1988;Addison-Wesley, Reading, MA.
22. Cheng D.K. Fundamentals of engineering electromagnetics. 1994;Addison-Wesley, Reading, MA.
23. Shin YK. Establishment of 3-D numerical analysis program for the prediction of eddy current testing signals. Technical Report 96-jung-02, Electrical Engineering and Science Research Institute, Seoul, South Korea, 1997.
24. Song SJ. Ultrasonic flaw classification and sizing. PhD Dissertation, Iowa State University, Ames, IA, 1991.
25. Song S.J., Schmerr L.W. Ultrasonic flaw classification in weldments using probabilistic neural networks. J Nondestructive Eval. 11:1992;69-77.