相位加权的矢量全聚焦超声阵列成像方法研究

发布时间：2018-03-27 07:38

本文选题：矢量方向　切入点：相位特征　出处：《声学学报》2017年04期

【摘要】：常规矢量全聚焦成像仅利用检测信号的幅值信息,其成像质量受噪声、栅瓣和旁瓣等的影响大。综合利用检测信号的幅值和相位信息,本文提出两种相位加权的矢量全聚焦成像方法。首先,对全矩阵数据的相位信息进行分析,提取出两种相位特征参数:相位一致因子(Phase Coherence Factor:PCF)和极性一致因子(Sign Coherence Factor:SCF);然后,将全阵列划分为若干子阵列,分别利用两种相位特征参数对各个子阵列的成像幅值进行加权,求取加权幅值特征向量;最后,对所有子阵列的加权特征向量进行合成,得到两种加权的矢量全聚焦成像,并从中提取出裂纹方向及尺寸等特征信息。将三种矢量全聚焦成像方法应用于不同缺陷检测仿真及实验验证,结果表明,3种方法均可以实现缺陷方向识别与长度定量测量;但相位加权矢量全聚焦成像效果明显优于常规矢量全聚焦成像结果,其成像信噪比及分辨率更高,缺陷角度及长度测量结果更准确。本文研究工作为缺陷无损评价提供了可行的技术手段。
[Abstract]:Conventional vector full-focus imaging uses only the amplitude information of the detection signal, and its imaging quality is greatly affected by noise, gate lobe and sidelobe, etc. In this paper, two phase-weighted vector full-focus imaging methods are proposed. Firstly, the phase information of the full matrix data is analyzed, and two kinds of phase characteristic parameters are extracted: phase consistency factor (Coherence Coherence: PCF) and polarity consistency factor (sign Coherence Factor: SCF); then, The whole array is divided into several sub-arrays, and the imaging amplitudes of each sub-array are weighted by two kinds of phase characteristic parameters, and the weighted amplitude eigenvectors are obtained. Finally, the weighted Eigenvectors of all sub-arrays are synthesized. Two kinds of weighted vector full focus imaging are obtained, and the characteristic information of crack direction and size are extracted from them. The three vector full focus imaging methods are applied to different defect detection simulation and experimental verification. The results show that all of the three methods can realize defect direction recognition and length quantitative measurement, but the phase-weighted vector full-focus imaging is superior to conventional vector full-focus imaging, and its signal-to-noise ratio and resolution are higher. The results of defect angle and length measurement are more accurate.
【作者单位】：北京工业大学机械工程与应用电子技术学院;北京工业大学北京市精密测控技术与仪器工程技术研究中心;
【基金】：国家自然科学基金项目(11572010,11272017) 国家重点研发计划项目(2016YFF0203002)资助
【分类号】：TB559

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