Abstract Ferromagnetic steels are widely used in engineering structures such as rail track, oil/gas pipeline and steel hanging bridge. Cracks resulted from manufacturing processes or previous loading will seriously undermine the safety of the engineering structures and even lead to catastrophic industrial accidents. Accurate and quantitative characterization the cracks in ferromagnetic steels are therefore of vital importance. In this paper, the cracks in ferromagnetic steels are detected by the pulsed eddy current (PEC) technique. Firstly, the physical mechanism of the relative magnetic permeability of the ferromagnetic steel on the detection signal of PEC is interpreted from a microscopic level of magnetic domain wall movement. The relationship of the crack width/depth and the detection signal of PEC is then investigated and verified by numerical simulations and experimental study. Finally, the cracks are inversely characterized by using Genetic Algorithm (GA) based Back-Propagation (BP) neural network (NN) considering the nonlinearity of the crack geometric parameters with the detection signal of PEC. The prediction results indicated that the proposed algorithm can characterize the crack depth and width within the relative error of 10%. The proposed approach combining PEC and GA based BPNN has been verified to quantitatively detect cracks in ferromagnetic steel.

中文翻译：

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基于GA-BP神经网络模型的脉冲涡流技术铁磁钢裂纹表征

摘要 铁磁钢广泛应用于轨道、油气管道、钢吊桥等工程结构中。制造过程或前期装载产生的裂缝将严重破坏工程结构的安全性，甚至导致灾难性的工业事故。因此，准确定量地表征铁磁钢中的裂纹至关重要。在本文中，铁磁钢中的裂纹是通过脉冲涡流 (PEC) 技术检测的。首先，从磁畴壁运动的微观层面解释了铁磁钢相对磁导率对PEC检测信号的物理机制。然后通过数值模拟和实验研究来研究和验证裂纹宽度/深度与PEC检测信号的关系。最后，考虑裂纹几何参数与PEC检测信号的非线性，采用基于遗传算法(GA)的反向传播(BP)神经网络(NN)对裂纹进行逆表征。预测结果表明，该算法能够在10%的相对误差内表征裂纹深度和宽度。所提出的结合 PEC 和基于 GA 的 BPNN 的方法已被证实可以定量检测铁磁钢中的裂纹。考虑到裂纹几何参数与PEC检测信号的非线性，采用基于遗传算法(GA)的反向传播(BP)神经网络(NN)对裂纹进行逆表征。预测结果表明，该算法能够在10%的相对误差内表征裂纹深度和宽度。所提出的结合 PEC 和基于 GA 的 BPNN 的方法已被证实可以定量检测铁磁钢中的裂纹。考虑到裂纹几何参数与PEC检测信号的非线性，采用基于遗传算法(GA)的反向传播(BP)神经网络(NN)对裂纹进行逆表征。预测结果表明，该算法能够在10%的相对误差内表征裂纹深度和宽度。所提出的结合 PEC 和基于 GA 的 BPNN 的方法已被证实可以定量检测铁磁钢中的裂纹。