Automated Multichannel Signal Classification Systems for Ultrasonic Nondestrucitve Evaluation

Abstract:

Article Preview

A key requirement in most ultrasonic weld inspection systems is the ability for rapid automated analysis to identify the type of flaw. Incorporation of spatial correlation information from adjacent A-scans can improve performance of the analysis system. This paper describes two neural network based classification techniques that use correlation of adjacent A-scans. The first method relies on differences in individual A-scans to classify signals using a trained neural network, with a post-processing mechanism to incorporate spatial correlation information. The second technique transforms a group of spatially localized signals using a 2-dimensional transform, and principal component analysis is applied to the transform coefficients to generate a reduced dimensional feature vectors for classification. Results of applying the proposed techniques to data obtained from weld inspection are presented, and the performances of the two approaches are compared.

Info:

Periodical:

Key Engineering Materials (Volumes 321-323)

Edited by:

Seung-Seok Lee, Joon Hyun Lee, Ik Keun Park, Sung-Jin Song, Man Yong Choi

Pages:

1266-1269

DOI:

10.4028/www.scientific.net/KEM.321-323.1266

Citation:

J. Kim et al., "Automated Multichannel Signal Classification Systems for Ultrasonic Nondestrucitve Evaluation", Key Engineering Materials, Vols. 321-323, pp. 1266-1269, 2006

Online since:

October 2006

Export:

Price:

$38.00

[1] J. Kim, Multistage adaptive noise cancellation and multidimensional signal processing for ultrasonic nondestructive evaluation, Ph. D. Dissertation, Iowa State University, Ames, IA, (2000).

[2] R. Polikar, L. Udpa, S. S. Udpa, and T. Taylor, Frequency invariant classification of ultrasonic weld inspection signals, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 45, (1998), pp.614-625.

DOI: 10.1109/58.677606

[3] P. Ramuhalli, L. Udpa and S. S. Udpa, An automatic signal classification system for ultrasonic weld inspection signals, Materials Evaluation, Vol. 58, (2000), pp.65-69.

DOI: 10.1007/978-1-4615-5339-7_97

[4] Neural Networks and Semi-Automatic Scanners for NDE Applications, EPRI TR-107119, EPRI Final Report, December (1996).

[5] M. Louys, J. L. Starck, S. Mei, F. Bonnarel, and F. Murtagh, Astronomical Image Compression, Astronomy and Astrophysics Supplement Series, Vol. 136, (1999), pp.579-590.

DOI: 10.1051/aas:1999235

[6] M. Vetterli and J. Kovacevic, Wavelets and Subband Coding, Englewood Cliffs, NJ: Prentice Hall, (1995).

[7] O. V. Vasilyev, D. A. Yuen and S. Paolucci, The Solution of PDEs Using Wavelet, Computers in Phys., Vol. 11, No. 5, (1997), pp.429-435.

[8] S. Mallat, A Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Pattern Anal. and Machine Intell., Vol. 11, No. 7, (1989), pp.674-693.

DOI: 10.1109/34.192463

[9] W. K. Pratt, Digital Image Processing, John Wiley & Sons, Inc, New York, (1991).

[10] S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan College Publishing Company, New York, (1994).

[11] E. Oja. Subspace Methods of Pattern Recognition, Pattern Recognition and Image Processing Series. Vol. 6, John Wiley & Sons, (1983).

In order to see related information, you need to Login.