State Key Lab of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China.
CRRC Zhuzhou Institute Co. Ltd., Zhuzhou 412001, China.
Sensors (Basel). 2019 Jan 9;19(2):236. doi: 10.3390/s19020236.
The detection of flaw echoes in backscattered signals in ultrasonic nondestructive testing can be challenging due to the existence of backscattering noise and electronic noise. In this article, an empirical mode decomposition (EMD) methodology is proposed for flaw echo enhancement. The backscattered signal was first decomposed into several intrinsic mode functions (IMFs) using EMD or ensemble EMD (EEMD). The sample entropies (SampEn) of all IMFs were used to select the relevant modes. Otsu's method was used for interval thresholding of the first relevant mode, and a window was used to separate the flaw echoes in the relevant modes. The flaw echo was reconstructed by adding the residue and the separated flaw echoes. The established methodology was successfully employed for simulated signal and experimental signal processing. For the simulated signals, an improvement of 9.42 dB in the signal-to-noise ratio (SNR) and an improvement of 0.0099 in the modified correlation coefficient (MCC) were achieved. For experimental signals obtained from two cracks at different depths, the flaw echoes were also significantly enhanced.
在超声无损检测中，由于背散射噪声和电子噪声的存在，回波信号的缺陷检测具有一定的挑战性。本文提出了一种基于经验模态分解（EMD）的方法来增强缺陷回波。首先，使用 EMD 或集合经验模态分解（EEMD）将背散射信号分解为几个固有模态函数（IMF）。然后，使用样本熵（SampEn）选择相关模态。使用 Otsu 方法对第一个相关模态进行区间阈值处理，并使用窗口分离相关模态中的缺陷回波。通过添加残差和分离的缺陷回波来重建缺陷回波。所建立的方法成功地应用于模拟信号和实验信号处理。对于模拟信号，信噪比（SNR）提高了 9.42dB，修正相关系数（MCC）提高了 0.0099。对于从两个不同深度的裂纹获得的实验信号，缺陷回波也得到了显著增强。
