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首页> 外文期刊>Journal of Nondestructive Evaluation >Pulsed Eddy Current Data Analysis for the Characterization of the Second-Layer Discontinuities
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Pulsed Eddy Current Data Analysis for the Characterization of the Second-Layer Discontinuities

机译：脉冲涡流数据分析，用于表征二层不连续性

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摘要

Pulsed eddy current (PEC) technique has been applied as a viable method to detect hidden discontinuities in metallic structures. Conventionally, selected time-domain features are employed to characterize the PEC data, such as peak value, lift-off point of intersection, rising point, crossing time, and differential time to peak. The research presented in this paper continues the effort in a previous study on detecting the radial cracks starting from the fastener hole in second layer of a two-layer mock-up aircraft structure. A large diameter excitation coil with ferrite core is used to induce a strong pulse, and the magnetic field generated by eddy current is detected by Hall sensors. Instead of analyzing the limited time-domain features, we propose using machine learning methods to interpret the raw data without feature extraction. Thus, the second-layer discontinuities can be characterized presumably with all the information contained in a waveform. An automated detection framework is proposed in this paper and the experimental results demonstrate the effectiveness of the proposed method.
机译：脉冲涡流（PEC）技术已被应用为可行的方法，以检测金属结构中隐藏的不连续性。传统上，采用所选的时域特征来表征PEC数据，例如峰值，交叉点的升降点，上升点，交叉时间和差分时间到峰值。本文提出的研究继续在先前研究检测从双层模拟飞机结构的第二层中的紧固件孔开始的径向裂缝的努力。使用具有铁氧体磁芯的大直径激励线圈用于诱导强脉冲，并且由霍尔传感器检测涡电流产生的磁场。我们建议使用机器学习方法来解释无需特征提取的原始数据来解释原始数据。因此，可以通过波形中包含的所有信息来表征第二层不连续性。本文提出了一种自动检测框架，实验结果证明了该方法的有效性。

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