基于希尔伯特变换和功率谱估计的薄缺陷厚度太赫兹检测
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作者单位:

1.吉林大学仪器科学与电气工程学院,吉林 长春 130061;2.中国科学院重庆绿色智能技术研究院,重庆 400714

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基金项目:

山东省自然科学基金项目(ZR2020KF007)


Terahertz detection of thin defects thickness based on Hilbert transform and power spectrum estimation
Author:
Affiliation:

1.Jilin University, College of Instrumentation & Electrical Engineering, Changchun, 130061, China;2.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China

Fund Project:

Supported by the Natural Science Foundation of Shandong Province (ZR2020KF007)

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    摘要:

    提出将希尔伯特变换和功率谱估计相结合的光谱分析算法,对太赫兹反射时域波形进行处理,并将该算法应用于太赫兹时域光谱成像,将缺陷厚度和图像灰度关联,实现同时对玻璃纤维层压板内部缺陷厚度、位置和形状的成像检测。实验结果表明:将多重信号分类谱估计、自回归谱估计和希尔伯特变换结合时,能成功区分厚度为0.08 mm缺陷上下表面反射脉冲,反射脉冲的时间分辨率小于0.5 ps,缺陷厚度的检测误差不高于0.03 mm。

    Abstract:

    A spectral analysis algorithm based on the combination of Hilbert transform (HT) and power spectrum estimation has been proposed, and the terahertz reflection time domain waveform was processed. At the same time, the algorithm was applied to terahertz time domain spectroscopy imaging, defect thickness was correlated with image gray level, and the thickness, position and shape of defects in glass fiber laminate can be detected by imaging simultaneously. The experimental results show that when the multi-signal classification (MUSIC) spectrum estimation and auto regressive (AR) spectrum estimation are combined with Hilbert transform, the reflected pulses between upper and lower surfaces of defect with thickness of 0.08 mm can be successfully distinguished, the time resolution of reflected pulses is higher than 0.5 ps, and the detection error of defect thickness is no more than 0.03 mm.

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  • 收稿日期:2021-04-17
  • 最后修改日期:2022-01-17
  • 录用日期:2021-07-16
  • 在线发布日期: 2022-01-17
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