DocumentCode :
1932688
Title :
Application of wavelet basis function neural networks to NDE
Author :
Hwang, K. ; Mandayam, S. ; Udpa, S.S. ; Udpa, L. ; Lord, W.
Author_Institution :
Dept. of Electr. Eng. & Comput. Eng., Iowa State Univ., Ames, IA, USA
Volume :
3
fYear :
1996
fDate :
18-21 Aug 1996
Firstpage :
1420
Abstract :
This paper presents a novel approach for training a multiresolution, hierarchical wavelet basis function neural network. Such a network can be employed for characterizing defects in gas pipelines which are inspected using the magnetic flux leakage method of nondestructive testing. The results indicate that significant advantages over other neural network based defect characterization schemes could be obtained, in that the accuracy of the predicted defect profile can be controlled by the resolution of the network. The centers of the basis functions are calculated using a dyadic expansion scheme and a hybrid learning method. The performance of the network is demonstrated by predicting defect profiles from experimental magnetic flux leakage signals
Keywords :
flaw detection; magnetic leakage; neural nets; nondestructive testing; wavelet transforms; NDE; defect inspection; dyadic expansion; gas pipeline; hybrid learning; magnetic flux leakage signal; multiresolution hierarchical wavelet basis function neural network; nondestructive testing; training; Application software; Interpolation; Inverse problems; Magnetic flux leakage; Magnetic sensors; Neural networks; Pipelines; Saturation magnetization; Signal generators; Signal resolution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1996., IEEE 39th Midwest symposium on
Conference_Location :
Ames, IA
Print_ISBN :
0-7803-3636-4
Type :
conf
DOI :
10.1109/MWSCAS.1996.593230
Filename :
593230
Link To Document :
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