油液磨粒超声回波信号双树复小波自适应降噪最优分解层数的研究  

Research of Optimal Decomposition Level in Adaptive De-noising Ultrasonic Echo Signal of Wear Debris in Oil Based on Dual-tree Complex Wavelet Transform

作　　者：李一宁[1] 张培林[1] 徐超[1] 杨玉栋[2] 张云强[1] 吕纯[1] 

机构地区：[1]军械工程学院七系,河北石家庄050003 [2]武汉军械士官学校四系,湖北武汉430075

出　　处：《机床与液压》2015年第19期205-209,共5页Machine Tool & Hydraulics

基　　金：国家自然科学基金资助项目(50705097;51305454)

摘　　要：磨粒超声回波信号受到各种因素的影响,从而存在噪声,分解层数的选取对降噪效果影响很大。为此,提出了一种基于粒子群优化算法(PSO)的最优分解层数选取方法,将得到的最优分解层数代入双树复小波域,采用一种渐近半软阈值函数与一种自适应阈值选择方法相结合,对含噪磨粒回波信号进行双树复小波阈值降噪,选取信噪比(SNR)和均方根误差(RMSE)两个参数评价降噪结果。仿真与实验结果表明:通过粒子群优化算法选取的分解层数得到的信噪比最高,油液磨粒超声回波信号自适应降噪方法对磨粒超声回波信号具有显著的降噪效果,明显提高了信噪比,降低了均方根误差,还原了信号的波形特征,为后续的特征提取与智能识别打下了良好的基础。The ultrasonic echo signal of wear debris is influenced by many matters,thereby noises are existed and selection of decomposition level has great influence on the effect of noise de-noising. Therefore,a kind of algorithm to select the optimal decomposition level based on particle swarm optimization( PSO) method was put forward. The obtained optimal decomposition level was substituted in the dual-tree complex wavelet field,and a method was used which combined the asymptotic semi-soft threshold function with adaptive threshold selection. Dual-tree complex wavelet transform( DTCWT) threshold was utilized to de-noise for the noisy ultrasonic echo signal. According to the parameters of signal-to-noise ratio( SNR) and root mean square error( RMSE),the result of de-noising was evaluated. Simulated and experimental results show that the SNR is the highest which is obtained by the decomposition level selected by PSO,the adaptive de-noising method for ultrasonic echo signal of wear debris in oil has obvious effect on signal de-noising,and it obviously improves the SNR and reduces the RMSE as well as restore the waveform features of the signal,which lays a good foundation for the further feature extraction and intelligent recognition.

关 键 词：在线磨粒检测 分解层数 自适应降噪 双树复小波变换 粒子群优化算法 

分 类 号：TP391.42[自动化与计算机技术—计算机应用技术]