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

被引：0
|
作者：
W. Han
P. Que
机构：
[1] Shanghai Jiaotong University,Institute of Automatic Detection
来源：
Russian Journal of Nondestructive Testing | 2005年 / 41卷
关键词：
Neural Network; Genetic Algorithm; Inverse Problem; Optimization Algorithm; Structural Material;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The magnetic-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：5
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