基于自适应第二代小波的超声回波信号特征识别

摘要

To identify the features of a hydraulic system's ultrasonic echo sisnal corrupted by noise, we develop a new method for constructing adaptive second generation wavelet.This new method is explained in sections 1,2 and 3.Their core consists of: ( 1 ) based on the second generation wavelet,we use the size of kurtosis of ultrasonic echo signal as objective function to select its prediction operator and updating operator, each of which can adaptively match its features at each level; thus, the ultrasonic echo signal is predicted and updated; (2) according to the level of noise of the ultrasonic echo signal decomposed by using the adaptive second generation wavelet at each level,we select the de-noising threshold value to de-noise and recover the ultrasonic echo signal and extract its features in time domain.Section 4 and 5 apply our method to the feature extraction of ultrasonic echo signal; the results, given in Figs.1 through 7, and their analysis show preliminarily that our method for constructing the adaptive second generation wavelet can effectively extract the features of ultrasonic echo signal by identifying its arrival time point and the interval between two time points, indicating that our method outperforms the traditional wavelet transform methods.%文章针对强噪声背景下液压系统压力超声检测回波信号特征识别问题,构造了一种识别该类信号时域特征的自适应第二代小波方法.该方法以第二代小波为基础,以超声回波信号的峭度大小为目标函数,选择每层自适应匹配超声回波信号特征的第二代小波预测器和更新器,利用选择的预测器和更新器对信号进行预测和更新运算;根据每层自适应第二代小波分解的超声回波信号噪声水平,选取自适应匹配超声回波信号噪声特点的降噪阈值,对信号进行降噪和恢复,提取信号时域特征.该方法成功地准确识别出超声回波信号回波至点的时刻和时间间隔特征信息.结果表明,第二代小波自适应阈值方法对强噪声背景下超声回波信号特征的识别效果优于经典小波方法.