Pipeline magnetic flux leakage (MFL) detection is an efficient and energy-saving nondestructive testing (NDT) method. However, under the high-speed detector, MFL signals become distorted with the influence of velocity effects, which adversely affects the accuracy of defect size inversion. The essential cause is the distorted signal multiplicity by velocity effects. In response to this issue, a high-precision defect size inversion method is proposed for the first time, which is called knowledge-guided contrastive fusion network (KCF-Net). First, MFL and eddy current (EC) mechanisms are analyzed, which are concluded that the sensitivity of EC signals to speed is much lower than that to defect sizes, so that EC and MFL abstract features are mined to improve the sensitivity of defect sizes. Moreover, MFL mechanism representations are mined to supervise neural networks to enhance the interpretability of the network. MFL and EC knowledge including abstract features and mechanism representations is fused to highlight the disparities between undistorted and distorted signals and enrich available information. Then, joint decision-making is proposed to eliminate the instability of fusion knowledge and enhance the universality and effectiveness of defect inversion. Finally, the experiments prove the effectiveness of KCF-Net. The length, width, and depth inversion MAEs of measured signals reach 2.2676, 1.6185, and 0.5664, respectively.

中文翻译：

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考虑速度效应的管道缺陷高精度尺寸反演方法


管道漏磁（MFL）检测是一种高效、节能的无损检测（NDT）方法。然而，在高速探测器下，MFL信号会因速度效应的影响而发生畸变，从而影响缺陷尺寸反演的精度。根本原因是速度效应造成的信号多重性失真。针对这一问题，首次提出了一种高精度缺陷尺寸反演方法，称为知识引导对比融合网络（KCF-Net）。首先，分析了MFL和涡流（EC）机制，得出EC信号对速度的敏感性远低于对缺陷尺寸的敏感性，因此挖掘EC和MFL抽象特征以提高缺陷尺寸的敏感性。此外，挖掘 MFL 机制表示来监督神经网络，以增强网络的可解释性。 MFL 和 EC 知识（包括抽象特征和机制表示）融合在一起，以突出未失真和失真信号之间的差异，并丰富可用信息。然后，提出联合决策来消除融合知识的不稳定性，增强缺陷反演的普适性和有效性。最后通过实验证明了KCF-Net的有效性。测量信号的长度、宽度和深度反演MAE分别达到2.2676、1.6185和0.5664。