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侵彻弹体频率特性分析及过载信号处理 被引量:7

Frequency Characteristics Analyses of Penetrating Missile and Penetration Overload Signal Processing
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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。 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. Firstly,the measured signals were decomposed by EEMD method, and the IMF components could be got. Secondly,the original signals EEMD decomposition scale could be drawn through comparing the power spectrum of each compo- nents 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.
出处 《中国机械工程》 EI CAS CSCD 北大核心 2015年第22期3034-3039,共6页 China Mechanical Engineering
基金 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)
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 penetration overload ensemble empirical mode decomposition(EEMD) wavelet trans-form(WT) intrinsic mode function(IMF) power spectrum
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参考文献11

  • 1徐鹏.高g值冲击测试及弹载存储测试装置本征特性研究[D].太原:中北大学,2006.
  • 2Franco R J, Platzbecker M R. Miniature Penetrator (MINPEN) Acceleration Recorder Development Test [R]. New Mexico: Sandia National Laboratories, 1998.
  • 3Forrestal M J, Frew D J, Hickerson J P, et al. Pene- tration of Concrete Targets with Deceleration-timeMeasurements[J]. International Journal of Impact Engineering, 2003,28(5) :479-497.
  • 4Rothacher T. Giger B: High G Ballistic Flight Data Reeorder[C]//18th International Symposium on Ballistic. San Antonia, 1999 : 379-386.
  • 5王成华,陈佩银,徐孝诚.侵彻过载实测数据的滤波及弹体侵彻刚体过载的确定[J].爆炸与冲击,2007,27(5):416-419. 被引量:32
  • 6Donoho D L. De-noising by Soft-thresholding[J]. J. IEEE Transactions on Information Theory, 1995,41 (3) :6123627.
  • 7Wu Zhaohua, Huang N E. Ensemble Empirical Mode Decomposition: a Noise Assisted Data Analysis Method[J]. Advances in Adaptive Data Analysis, 2009,1(1) : 1-41.
  • 8吴虎胜,吕建新,吴庐山,王茂生.基于EMD和SVM的柴油机气阀机构故障诊断[J].中国机械工程,2010,21(22):2710-2714. 被引量:19
  • 9梁曲生,张西宁,沈玉绨.机械故障诊断理论与方法[M].西安:西安交通大学出版社,2009.
  • 10郝慧艳,李晓峰,孙运强,刘明杰.侵彻过程弹体结构响应频率特性的分析方法[J].振动.测试与诊断,2013,33(2):307-310. 被引量:11

二级参考文献23

  • 1王成华,史利平,徐孝诚.混凝土靶侵彻计算的半经验法[J].强度与环境,2007,34(2):31-37. 被引量:4
  • 2Liu B, Ling S F. On the Selection of Informative Wavelets for Machinery Diagnosis[J]. Mechanical Systems & Signal Processing, 1999, 13 (1): 145- 162.
  • 3Huang N E,Shen Z,Long S R,et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non--stationary Time Series Analysis[J]. Proc. R. Soc. Lond. A,1998,454:903-995.
  • 4Vapnik V N. The Nature of Statistical Learning Theory[M]. New York:Spring Verag, 1995.
  • 5Forrestal M J, Luk V K. Penetration into soil targets[J]. International Journal of Impact Engineering, 1992,12 (3) :427-444.
  • 6Forrestal M J. Penetration into dry porous rock[J]. Journal of Solids Structures, 1986,22(12):1 485-1 500.
  • 7Forrestal M J, Altman B S, Cargile Z D, et al. An empirieal equation for penetration depth of ogive-nose projeetiles into eonerete targets[J]. International Journal of Impaet Engineering, 1994,15(4) :395-404.
  • 8贺李平,龙凯,肖介平.ANSYS 13.0与HyperMesh11.0联合仿真有限元分析[M].北京:机械工业出版杜,2012.
  • 9Bai Li, Liu Mingjie, Li Xiaofeng,et al. Research on composition and formation mechanism of penetration acceleration signal[C]//2010 International Conference on Intelligent Computation Technology and Automa- tion. Changsha: Changsha University of Science and Technology, 2010: 1118-1121.
  • 10Wu Zhaohua, Huang N E. Ensemble empirical mode decomposition: a noise assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1(1):1-41.

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