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侵彻弹体频率特性分析及过载信号处理
引用本文:赵海峰,张亚,李世中,郭燕.侵彻弹体频率特性分析及过载信号处理[J].中国机械工程,2015,26(22):3034-3039.
作者姓名:赵海峰  张亚  李世中  郭燕
作者单位:1.中北大学,太原,030051 2.南京信息职业技术学院,南京,210023 3.渥太华大学,渥太华,K1N  6N5
基金项目:国家自然科学基金资助项目(51275547);江苏省第二批中青年骨干教师和校长境外研修计划资助项目(2012-13)
摘    要:为解决硬目标侵彻过载信号降噪问题,提出融合总体经验模态分解(EEMD)和小波变换(WT)的联合滤波方法。首先对实测信号进行总体经验模态分解,获得信号的本征模态函数(IMF)分量,然后计算各分量功率谱并与原信号比较,得出信号的有效分解尺度和弹体的过载响应频率,接着对高频IMF分量采用小波阈值降噪,最后将降噪后的高频分量与分解后的低频分量组合重构获得侵彻特征信号。实验证明,这一方法可以有效提取弹体响应频率,消除侵彻过程中弹体的高频振动信号和外部噪声,且处理后的加速度曲线具有更高的信噪比,积分所得速度和位移时程曲线也与实验结果相近。

关 键 词:侵彻过载  总体经验模态分解  小波变换  本征模态函数  功率谱  

Frequency Characteristics Analyses of Penetrating Missile and Penetration Overload Signal Processing
Zhao Haifeng,Zhang Ya,Li Shizhong,Guo Yan.Frequency Characteristics Analyses of Penetrating Missile and Penetration Overload Signal Processing[J].China Mechanical Engineering,2015,26(22):3034-3039.
Authors:Zhao Haifeng  Zhang Ya  Li Shizhong  Guo Yan
Affiliation:1.North University of China,Taiyuan,030051 2.Nanjing College  of  Information Technology,Nanjing,210023 3.University of  Ottawa,Ottawa,K1N 6N5
Abstract:In order to solve the hard target penetration overload signals de-noising problem,this paper proposed a joint filtering method based on EEMD and WT. IMF components could be  got.Secondly,the original signals EEMD decomposition scale could  be drawn through comparing the power spectrum of each components with the original signals'.Then,the high-frequency components of IMF were filtered based on the WT  threshold.Finally,the signals were  reconstructed by using the low-frequecy IMF components and the filtered high-frequency IMF components. Experiments show that proposed method can effectively extract the response frequency of missile body, eliminate high-frequency vibration and noise in the penetration.  The results of the proposed method can get better signal to noise ratio(SNR)  than  that  of  WT.And the integrated velocity and displacement time-history curves are close to the experiments.
Keywords:penetration overload  ensemble empirical mode decomposition(EEMD)  wavelet   transform(WT)  intrinsic mode function(IMF)  power spectrum  
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