基于经验与变分混合分解的超声回波信号噪声消除方法

超声缺陷检测结果易受超声回波信号中复杂噪声的干扰,为了提高超声缺陷检测的准确度,提出一种基于混合分解的 超声回波信号噪声消除方法。 采用经验模态分解算法结合相关系数指标对超声回波信号进行预处理,得到消除低频噪声分量 的超声回波预处理信号。 基于变分模态分解将该预处理信号分解为一系列窄带本征模态函数,引入互信息指标估计变分模态 分解的最优模态数量,并根据窄带本征模态函数与预处理信号的相关系数提取有用的模态分量,实现对超声回波信号去噪结果 的重构。 通过仿真和实测超声回波信号验证了本文方法的去噪性能,并与现有方法进行了对比。 结果表明,本文方法可同时消 除超声回波信号中的高频和低频噪声,在不同信噪比条件下 EMD、VMD 和本文方法去噪结果的 SNR 均值分别为 10. 01、9. 48 和 16. 09 dB,验证了本文方法对于超声回波信号噪声消除的优越性。

Noise elimination method of ultrasonic echo signal based on empirical and variational hybrid decomposition

Ultrasonic defect detections are easily disturbed by complex noise in ultrasonic echo signals. To improve the accuracy of ultrasonic defect detection, an ultrasonic echo signal noise elimination method based on the hybrid decomposition is proposed. The fusion of empirical mode decomposition and correlation coefficient is used to preprocess the ultrasonic echo signal, and the preprocessing signal is obtained to eliminate the low-frequency noise components. Based on variational mode decomposition, the preprocessed signal is decomposed into several band-limited intrinsic mode functions ( BLIMFs), and the mutual information is introduced to estimate the optimal mode number. The useful modes are extracted according to the correlation coefficient of the BLIMFs to the preprocessed signal, and the denoising results are reconstructed. The denoising performance of the proposed method is evaluated by simulation and measured ultrasonic echo signals. Compared with two existing methods, experimental results show that the proposed method can simultaneously eliminate high-frequency and low-frequency noises in ultrasonic echo signals. Under different SNR conditions, the mean SNR of the EMD, VMD and the proposed method are 10. 01 dB, 9. 48 dB, and 16. 09 dB, respectively, which proves the superiority of the proposed method for noise elimination of the ultrasonic echo signals.