Automated Multichannel Signal Classification Systems for Ultrasonic Nondestrucitve Evaluation


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.



Key Engineering Materials (Volumes 321-323)

Edited by:

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




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




[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).

Fetching data from Crossref.
This may take some time to load.