2D defect reconstruction from MFL signals by a genetic optimization algorithm

被引：6
作者：
Han, W [1 ]
Que, P [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Inst Automat Detect, Shanghai, Peoples R China
来源：
RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING | 2005年 / 41卷 / 12期
关键词：
D O I：
10.1007/s11181-006-0037-0
中图分类号：
TB3 [工程材料学];
学科分类号：
0805 ; 080502 ;
摘要：
The Diagnetic-flux-leakage (MFL) method has established itself as the most widely used inline inspection technique for the evaluation of gas and oil pipelines. An important problem in MFL nondestructive evaluation is the signal inverse problem, wherein the defect profile and its parameters are determined using the information contained in the measured signals. This paper proposes a genetic-algorithm-based inverse algorithm for reconstructing a 2D defect from MFL signals. In the algorithm, a radial-basis-function neural network is used as a forward model and a genetic algorithm is used to solve the optimization problem in the inverse problem. Experimental results are presented to demonstrate the effectiveness of the proposed inverse algorithm.
引用
收藏
页码：809 / 814
页数：6
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