| 王志,李忠虎,张鑫宇,王金明,杨立清.基于压缩感知和稀疏矩阵的管道缺陷超声全聚焦快速成像方法[J].电子测量与仪器学报,2026,40(1):247-255 |
| 基于压缩感知和稀疏矩阵的管道缺陷超声全聚焦快速成像方法 |
| Compressed sensing and sparse matrix-based rapid total focusing ultrasoundimaging method for pipeline defects |
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| DOI： |
| 中文关键词: 相控阵 超声检测 压缩感知 稀疏矩阵 全聚焦成像 |
| 英文关键词:phased array ultrasonic guided wave compressive sensing sparse matrix total focusing imaging |
| 基金项目:国家自然科学基金（62161042）、内蒙古自然科学基金（2023MS06006，2024LHMS06004）、内蒙古自治区直属高校基本科研业务费项目（2024QNJS031）资助 |
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| Author | Institution |
| Wang Zhi | School of Automation and Electrical Engineering, Inner Mongolia University of Science & Technology, Baotou 014010,China |
| Li Zhonghu | School of Automation and Electrical Engineering, Inner Mongolia University of Science & Technology, Baotou 014010,China |
| Zhang Xinyu | School of Mechanical Engineering, Inner Mongolia University of Science & Technology, Baotou 014010，China |
| Wang Jinming | School of Automation and Electrical Engineering, Inner Mongolia University of Science & Technology, Baotou 014010,China |
| Yang Liqing | School of Automation and Electrical Engineering, Inner Mongolia University of Science & Technology, Baotou 014010,China |
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| 中文摘要: |
| 针对管道缺陷超声导波全聚焦成像信号存储和传输数据量大及检测效率低等问题，研究基于压缩感知和稀疏矩阵的全聚焦成像方法。首先使用6种贪婪算法对管道回波数据进行压缩重构，分析重构算法对仿真信号重构精度的影响，选出最优重构算法并证明了压缩感知算法可以突破奈奎斯特定理的约束。然后计算稀疏度选择最优稀疏基，并通过分析与最优稀疏基的非相干性和管道的曲率效应来构造观测矩阵，与随机高斯矩阵相比，曲率加权的测量矩阵能够降低管道曲率效应的影响，提高信号重构精度及全聚焦成像质量。最后使用最优方案对经压缩重构得到的全矩阵数据和稀疏矩阵数据分别进行单缺陷全聚焦成像和双缺陷全聚焦成像。结果表明，基于压缩感知的稀疏矩阵全聚焦成像算法可以在保证精度的同时减少60%全聚焦成像和压缩感知信号处理时间，可以有效提高成像速度和检测效率，同时降低对检测系统硬件性能的需求。 |
| 英文摘要: |
| To address the issues of large data volume for storage and transmission of ultrasonic guided wave full focus imaging signals for pipeline defects and low detection efficiency, a full focus imaging method based on compressive sensing and sparse matrix is studied. Firstly, six types of greedy algorithms are employed to perform compression and reconstruction on pipeline echo data. The study analyzes the influence of reconstruction algorithms on the reconstruction accuracy of simulated signals, selects the optimal reconstruction algorithm, and verifies that compressed sensing algorithms can break the constraints of the Nyquist theorem. Then, the sparsity is calculated to determine the optimal sparse basis. The measurement matrix is constructed by analyzing both the incoherence with the optimal sparse basis and the curvature effect of the pipeline. Compared with the random Gaussian matrix, the curvature-weighted measurement matrix can reduce the impact of the pipeline’s curvature effect, thereby improving the signal reconstruction accuracy and the quality of total focusing imaging. Finally, the optimal scheme is applied to conduct single-defect total focusing imaging and double-defect total focusing imaging respectively on the full-matrix data and sparse-matrix data obtained through compression and reconstruction. The results show that the sparse matrix total focusing imaging algorithm based on compressed sensing can reduce the total time for total focusing imaging and compressed sensing signal processing by 60% while ensuring accuracy. This method effectively improves imaging speed and detection efficiency, and simultaneously reduces the requirements for the hardware performance of the detection system. |
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