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Ultrasonic Time-of-Flight Diffraction Imaging Enhancement for Pipeline Girth Weld Testing via Time-Domain Sparse Deconvolution and Frequency-Domain Synthetic Aperture Focusing.

作者信息

Wu Eryong, Han Ye, Yu Bei, Zhou Wei, Tian Shaohua

机构信息

Donghai Laboratory, Zhoushan 316021, China.

Ocean Research Center of Zhoushan, Zhejiang University, Zhoushan 316021, China.

出版信息

Sensors (Basel). 2025 Mar 20;25(6):1932. doi: 10.3390/s25061932.

DOI:10.3390/s25061932
PMID:40293111
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11946518/
Abstract

Ultrasonic TOFD imaging, as an important non-destructive testing method, has a wide range of applications in pipeline girth weld inspection and testing. Due to the limited bandwidth of ultrasonic transducers, near-surface defects in the weld are masked and cannot be recognized, resulting in poor longitudinal resolution. Affected by the inherent diffraction effect of scattered acoustic waves, defect images have noticeable trailing, resulting in poor transverse resolution of TOFD imaging and making quantitative defect detection difficult. In this paper, based on the assumption of the sparseness of ultrasonic defect distribution, by constructing a convolutional model of the ultrasonic TOFD signal, the Orthogonal Matching Pursuit (OMP) sparse deconvolution algorithm is utilized to enhance the longitudinal resolution. Based on the synthetic aperture acoustic imaging model, in the wavenumber domain, backpropagation inference is implemented through phase transfer technology to eliminate the influence of diffraction effects and enhance transverse resolution. On this basis, the time-domain sparse deconvolution and frequency-domain synthetic aperture focusing methods mentioned above are combined to enhance the resolution of ultrasonic TOFD imaging. The simulation and experimental results indicate that this technique can outline the shape of defects with fine detail and improve image resolution by about 35%.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/0d7ae4816319/sensors-25-01932-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/55fdd35ecca4/sensors-25-01932-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/c8acbe161f17/sensors-25-01932-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/43a716058c3c/sensors-25-01932-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/7afb8eb21d06/sensors-25-01932-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/54612b8dd1b0/sensors-25-01932-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/200e38742c37/sensors-25-01932-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/cf144ec6e420/sensors-25-01932-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/35c88fe7dd44/sensors-25-01932-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/1df0970c6e5c/sensors-25-01932-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/c4c9159525de/sensors-25-01932-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/660461faafde/sensors-25-01932-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/461f8a45501c/sensors-25-01932-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/a1c3a0485e39/sensors-25-01932-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/0d7ae4816319/sensors-25-01932-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/55fdd35ecca4/sensors-25-01932-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/c8acbe161f17/sensors-25-01932-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/43a716058c3c/sensors-25-01932-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/7afb8eb21d06/sensors-25-01932-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/54612b8dd1b0/sensors-25-01932-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/200e38742c37/sensors-25-01932-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/cf144ec6e420/sensors-25-01932-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/35c88fe7dd44/sensors-25-01932-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/1df0970c6e5c/sensors-25-01932-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/c4c9159525de/sensors-25-01932-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/660461faafde/sensors-25-01932-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/461f8a45501c/sensors-25-01932-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/a1c3a0485e39/sensors-25-01932-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2854/11946518/0d7ae4816319/sensors-25-01932-g014.jpg

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本文引用的文献

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Application of Sparse Synthetic Aperture Focusing Techniques to Ultrasound Imaging in Solids Using a Transducer Wedge.使用换能器楔块将稀疏合成孔径聚焦技术应用于固体中的超声成像
IEEE Trans Ultrason Ferroelectr Freq Control. 2024 Feb;71(2):280-294. doi: 10.1109/TUFFC.2023.3343295. Epub 2024 Jan 26.
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关于超声混响特性成像和矢量多普勒测量的 L2-和 L1-范数正则化的思考。
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Ultrasound Defect Localization in Shell Structures with Lamb Waves Using Spare Sensor Array and Orthogonal Matching Pursuit Decomposition.基于备用传感器阵列和正交匹配追踪分解的兰姆波超声壳体结构缺陷定位
Sensors (Basel). 2021 Dec 4;21(23):8127. doi: 10.3390/s21238127.
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3-D ultrasonic image reconstruction in frequency domain using a virtual transducer model.基于虚拟换能器模型的频域三维超声图像重建。
Ultrasonics. 2022 Jan;118:106573. doi: 10.1016/j.ultras.2021.106573. Epub 2021 Sep 3.
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