Wavelet basis function neural network for eddy current signal characterization

被引：0
|
作者：
Lim, JE [1 ]
Xiang, P [1 ]
Ramuhalli, P [1 ]
Udpa, S [1 ]
Udpa, L [1 ]
机构：
[1] Iowa State Univ, Dept Elect & Comp Engn, Mat Assessment Res Grp, Ames, IA 50010 USA
来源：
ELECTROMAGNETIC NONDESTRUCTIVE EVALUATION (VI) | 2002年 / 23卷
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A wavelet basis function (WBF) neural network is proposed to estimate the defect profile from eddy current inspection data of steam generator tubes. The calibration and phase gating schemes are first employed. Calibration is used to optimize the frequency and sensitivity settings, which are used to classifiy the actual defects relative to the reference defects. Phase gating is employed to improve the visibility of the defect after calibration. A WBF neural network is subsequently employed to map the signal appropriately to obtain the geometrical profile of the flaw. Test results showing the effectiveness of the approach are presented. A calibration curve method is also evaluated for a comparative study of results obtained with the WBF neural network.
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页码：245 / 252
页数：8
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