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首页> 外文期刊>IEEE Transactions on Instrumentation and Measurement >A Heuristic Algorithm for the Reconstruction and Extraction of Defect Shape Features in Magnetic Flux Leakage Testing
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A Heuristic Algorithm for the Reconstruction and Extraction of Defect Shape Features in Magnetic Flux Leakage Testing

机译：一种启发式算法，用于重建和提取磁通漏电检测中的缺陷形状特征

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摘要

The first step to quantifying complex defects is knowing its geometry, shape, and orientation. This is evidently challenging in electromagnetic nondestructive testing (ENDT), especially for subsurface complex defect detection, in cases where no information about defect or material is known or given. In this article, we propose a heuristic approach for the visualization, verification, and validation of complex defects in magnetic flux leakage (MFL) testing. This method is based on MFL experiment using magneto-optical images (MOIs) that are obtained from four different magnetization patterns. Using the proposed magnetization patterns, images of complex defect are captured with the aid of a charge-coupled device (CCD) camera, based on the interaction of magnetic field distribution and detected defects, from different angles and directions. An enhanced ant colony algorithm (EACA) is then used to reconstruct complex defect shapes from captured images using a mean image approach. The reconstructed image (mean image) reveals the defect shape with high precision for which the EACA is able to extract important defect features such as specific edges that might be hidden or blurred. This approach based on results has proved to provide promising solution to visually verifying complex defects in MFL, leading to a more simplified and faster way to characterize these defects as compared with conventional methods, using their shapes and orientation.
机译：定量复杂缺陷的第一步是了解其几何形状，形状和方向。在电磁非破坏性测试（ENDT）中，这显然是挑战，特别是对于缺陷或材料的信息是已知或给出的情况。在本文中，我们提出了一种启发式方法，用于磁通量泄漏（MFL）测试中的复杂缺陷的可视化，验证和验证。该方法基于使用从四种不同的磁化图案获得的磁光图像（摩尼斯）的MFL实验。使用所提出的磁化模式，借助于磁场分布的相互作用，从不同的角度和方向借助于电荷耦合器件（CCD）相机，借助于电荷耦合器件（CCD）相机，捕获复杂缺陷的图像。然后使用增强的蚁群算法（EACA）来使用平均图像方法来重建来自捕获图像的复杂缺陷形状。重建图像（平均图像）揭示了具有高精度的缺陷形状，因为EACA能够提取诸如可能隐藏或模糊的特定边缘的重要缺陷特征。基于结果的这种方法已经证明，提供了有希望的解决方案，以便在MFL中可视地验证复杂缺陷，从而与传统方法相比，使用它们的形状和方向来表征这些缺陷的更简单和更快的方法。

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