基于超分辨率超声图像的缺陷量化方法
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作者单位:

(国防科技大学 空天科学学院, 湖南 长沙 410073)

作者简介:

樊程广(1985—),男,河南新乡人,讲师,博士,E-mail:chengguangfan@nudt.edu.cn

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中图分类号:

TG115.28+5

基金项目:

国家自然科学基金资助项目(61601489)


Defect quantification based on super-resolved ultrasonic image
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(College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China)

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    摘要:

    以相位相干多信号分类(phase-coherent multiple signal classification, PC-MUSIC)方法为例,研究基于超分辨率超声图像的缺陷量化方法。利用全矩阵采集方法从被测对象获取超声阵列数据,对数据进行时域预处理,提取缺陷散射信号;利用PC-MUSIC方法处理缺陷散射信号,获取超分辨率超声图像;分析超声图像特征,提取横向强度曲线,定义-6 dB主瓣宽度作为缺陷的评估长度。搭建实验系统,选择铝试块作为被测对象,在其内部加工1个长度为10 mm的刻槽作为缺陷。实验结果表明,在信号子空间维度选择合适的情况下,PC-MUSIC方法能够准确评估缺陷长度,误差在10%以内。

    Abstract:

    PC-MUSIC (phase-coherent multiple signal classification) was introduced to study the defect quantification based on the super-resolved ultrasonic image. The ultrasonic array data can be collected via full matrix capture process, and pre-processed in time domain to extract the scattered signals related with defect. The scattered signals were post-processed by PC-MUSIC to obtain the ultrasonic image with super resolution. The ultrasonic image was analyzed to extract the lateral cross section, the -6 dB main lobe width of which was defined as the assessed length of defect. The experimental system had been built, and a block of Al with a 10mm-long slot which had been machined was chosen as the tested object. It is shown that the PC-MUSIC can accurately assess the length of defect with an error less than 10% when the proper dimension of signal subspace.

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引用本文

樊程广,余孙全,高斌,等.基于超分辨率超声图像的缺陷量化方法[J].国防科技大学学报,2022,44(5):187-192.
FAN Chengguang, YU Sunquan, GAO Bin, et al. Defect quantification based on super-resolved ultrasonic image[J]. Journal of National University of Defense Technology,2022,44(5):187-192.

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  • 收稿日期:2021-06-30
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  • 在线发布日期: 2022-09-28
  • 出版日期: 2022-10-28
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