油液磨粒超声回波信号的双树复小波去噪研究
Study on the De-noising of Ultrasonic Echo Signal for Oil Wear Debris Using the Dual-tree Complex Wavelet Transform

超声回波信号反映了润滑油中磨粒的大量信息。为了提取淹没在强噪声环境下的超声回波信号，提出了一种基于双树复小波变换（DT-CWT）的油液磨粒超声散射回波信号去噪新方法。利用双树复小波变换具有近似平移不变性和有效去噪等优点，首先对超声散射回波信号进行双树复小波分解，然后对分解得到的高频系数进行阈值处理，最后进行双树复小波重构。结果表明：分解层数为6层时，去噪后信号的信噪比更高、均方误差更小、相似系数更大、幅值最大偏差更小。双树复小波变换硬阈值去噪效果比传统小波去噪效果明显好。
The ultrasonic echo signal reflecting the debris lubricant contains a lots of information. In order to extract the ultrasonic echo signal submerged in strong background noise, a new de-noising method of ultrasonic scattering echo signal for oil wear debris based on the Dual-Tree Complex Wavelet Transform (DT-CWT) is proposed. Firstly, for the DT-CWT having the approximate shift-invariant and effective de-noising, the ultrasonic scattering echo signals are decomposed by using the DT-CWT, and then the threshold processing of high frequency coefficients is performed, finally, the signal is reconstructed by using the DT-CWT. The simulated and experimental results show that the SNR of the de-noising signal is higher, the RMSE is smaller, the NCC is higher, and the maximum amplitude difference (MAD) is smaller at a decomposition order of six. The DT-CWT de-noising method by using a hard threshold is more obvious than the traditional wavelet de-noising method