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首页> 外文期刊>Applied Computational Electromagnetics Society journal >3-D Defect Profile Reconstruction from Magnetic Flux Leakage Signals in Pipeline Inspection Using a Hybrid Inversion Method
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3-D Defect Profile Reconstruction from Magnetic Flux Leakage Signals in Pipeline Inspection Using a Hybrid Inversion Method

机译：使用混合反演方法从管道检测中的磁通量泄漏信号中重建3D缺陷轮廓

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摘要

In this paper, we propose a hybrid inversion approach to reconstruct the profile of arbitrary three-dimensional (3-D) defect from magnetic flux leakage (MFL) signals in pipeline inspection. The region of pipe wall immediately around the defect is represented by an array of partial cylinder cells, and a reduced forward FE model is developed to predict MFL signals for any given defect. The neural network (NN) method is used at first to give a coarse prediction of the defect profile, and the prediction is then utilized as one original solution of the genetic algorithm (GA) to search for the global optimum estimate of the defect profile. To demonstrate the accuracy and efficiency of the proposed inversion technique, we reconstruct defects from both simulated and experimental MFL signals. In both cases, reconstruction results indicate that the hybrid inversion method is rather effective in view of both efficiency and accuracy.
机译：在本文中，我们提出了一种混合反演方法，可以根据管道检测中的磁通量泄漏（MFL）信号重建任意三维（3-D）缺陷的轮廓。缺陷周围紧邻的管壁区域由部分圆柱单元表示，并开发了简化的正向有限元模型来预测任何给定缺陷的MFL信号。首先使用神经网络（NN）方法给出缺陷轮廓的粗略预测，然后将该预测用作遗传算法（GA）的一个原始解决方案，以搜索缺陷轮廓的全局最优估计。为了证明所提出的反演技术的准确性和效率，我们从模拟和实验MFL信号中重建缺陷。在两种情况下，重建结果均表明，从效率和准确性两方面来看，混合反演方法相当有效。

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