基于EEMD和低秩稀疏分解的超声缺陷回波检测方法

周航锐; 孙坚; 徐红伟; 缪存坚; 宋鑫

doi:10.3969/j.issn.1000-1158.2022.01.12

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计量学报 ›› 2022, Vol. 43 ›› Issue (1) : 77-84. DOI: 10.3969/j.issn.1000-1158.2022.01.12

力学计量

基于EEMD和低秩稀疏分解的超声缺陷回波检测方法

周航锐¹, 孙坚¹, 徐红伟¹, 缪存坚², 宋鑫²

作者信息 +

Ultrasonic Defect Echoes Identification Based on EEMD and Low-Rank Sparse Decomposition

ZHOU Hang-rui¹, SUN Jian¹, XU Hong-wei¹, MIAO Cun-jian², SONG Xin²

Author information +

文章历史 +

摘要

针对应用超声对金属材料微小缺陷检测时缺陷回波容易被噪声干扰的问题,提出了一种基于集合经验模态分解(EEMD)和低秩稀疏分解相结合的检测方法,以避免传统基于经验模态分解(EMD)的去噪方法难以消除结构噪声的问题。首先对缺陷检测信号进行EEMD得到一系列本征模态函数(IMF),采用基于概率密度函数的相似性测量方法选取相关模态,同时舍弃非相关模态以实现初步降噪;然后基于短时傅里叶变换(STFT)计算相关模态重构信号的幅度谱,执行低秩稀疏分解算法提取幅度谱中的稀疏成份实现进一步降噪;最后对稀疏成份进行逆STFT得到纯净的缺陷回波信号。分别对仿真和实测信号进行处理,结果表明该方法在缺陷回波检测方面是有效的。

Abstract

In order to detect the minor defect echoes of metal materials from noisy signal in ultrasonic nondestructive testing, a ultrasonic defect echoes identification method based on ensemble empirical mode decomposition (EEMD) and low-rank sparse decomposition was proposed. First, the EEMD was performed on the defect detection signal to obtain a series of intrinsic mode functions (IMF). The similarity measurement method based on probability density function was used to select irrelevant IMFs and these IMFs were discarded to achieve preliminary noise reduction. Then a denoising method based on short-time Fourier transform (STFT) and low-rank sparse decomposition algorithm was used for further noise suppression of the reconstructed signal. Finally, the inverse STFT was performed to obtain denoised defect echo signal in time domain. The simulated and measured signals are processed separately, and the results show that the method is effective in defect echo detection.

导出引用

周航锐, 孙坚, 徐红伟, 缪存坚, 宋鑫. 基于EEMD和低秩稀疏分解的超声缺陷回波检测方法[J]. 计量学报. 2022, 43(1): 77-84 https://doi.org/10.3969/j.issn.1000-1158.2022.01.12

ZHOU Hang-rui, SUN Jian, XU Hong-wei, MIAO Cun-jian, SONG Xin. Ultrasonic Defect Echoes Identification Based on EEMD and Low-Rank Sparse Decomposition[J]. Acta Metrologica Sinica. 2022, 43(1): 77-84 https://doi.org/10.3969/j.issn.1000-1158.2022.01.12

中图分类号： TB95

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