Wavelet-based permeability compensation technique for characterizing magnetic flux leakage images

被引:40
作者
Mandayam, S
Udpa, L
Udpa, SS
Lord, W
机构
[1] Dept. of Elec. and Comp. Engineering, Iowa State University, Ames
关键词
magnetic flux leakage; defect characterization; wavelet neural networks;
D O I
10.1016/S0963-8695(96)00075-8
中图分类号
TB3 [工程材料学];
学科分类号
0805 [材料科学与工程]; 080502 [材料学];
摘要
The magnetic flux leakage method, used for nondestructive evaluation of ferromagnetic objects, generates greyscale images that are representative of the integrity of the specimen. Defective areas typically appear as bright regions in the image. Unfortunately, the task of defect characterization becomes more challenging due to the effects of variations in the test parameters associated with the experiment. One such test parameter is the permeability of the test object. Conventional invariant pattern recognition algorithms are not capable of performing invariance transformations to compensate for such variations. This pa per describes a novel technique that uses wavelet basis functions to provide selective invariant features and eliminate image intensity variations from undesirable changes in operational variables. The performance of the invariance transformation is demonstrated by applying the method to magnetic flux leakage images obtained using a finite element simulation of in-line inspection of natural gas transmission pipelines. (C) 1997 Elsevier Science Ltd.
引用
收藏
页码:297 / 303
页数:7
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