管道缺陷检测全聚焦数据处理及成像方法研究
DOI:
CSTR:
作者:
作者单位:

1.内蒙古科技大学信息工程学院,内蒙古 包头 014010;2.内蒙古自治区光热与风能发电重点实验室,内蒙古 包头 014010;3.内蒙古科技大学机械工程学院,内蒙古 包头 014010

作者简介:

通讯作者:

中图分类号:

TB559

基金项目:

国家自然科学基金项目(62161042)


Pipeline defect detection full focus data processing and imaging method research
Author:
Affiliation:

1.School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010,China;2. Key Laboratory of Solar Thermal and Wind Power Generation of Inner Mongolia Autonomous Region, Baotou 014010,China;3. School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010,China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    管道缺陷检测是保证管道系统安全运行的必要手段,本文采用全聚焦成像的方法对管道缺陷进行成像与识别。利用有限元软件ABAQUS对基于L(0,1)模态导波的管道缺陷检测方法进行数值模拟研究,利用中值滤波、希尔伯特变换及信号包络锐化方法对信号进行预处理,然后用处理后的信号构建全矩阵数据,最后利用全聚焦算法实现缺陷成像,最终显示成像结果。实验结果表明,将信号进行预处理后再成像,可有效提高分辨率,实现缺陷的高精度可视化。

    Abstract:

    Pipeline defect detection is a necessary means to ensure the safe operation of pipeline system, and this paper uses the method of full focus imaging to image and identify pipeline defects. The finite element software ABAQUS is used to conduct numerical simulation of the pipeline defect detection method based on L(0,1) modal guided wave, and the signal is preprocessed by median filtering, Hilbert transformation and signal envelope sharpening method, and then the processed signal is used to construct the full matrix data, and finally the defect imaging is realized by the full focusing algorithm, and the imaging results are finally displayed. Experimental results show that the signal is pre-processed and then imaged, which can effectively improve the resolution and achieve high-precision visualization of defects.

    参考文献
    相似文献
    引证文献
引用本文

李 靖,李忠虎,张鑫宇,王金明.管道缺陷检测全聚焦数据处理及成像方法研究[J].电子测量技术,2022,45(17):153-158

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2024-04-02
  • 出版日期:
文章二维码