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爆破振动信号的HHT时频能量谱分析 被引量:22

The HHT time-frequency power spectrum analysis of the blasting vibration signal
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摘要 采用希尔伯特-黄变换对爆破振动信号进行时频能量谱分析。经验模态分解中采用了极值点对称延拓消除端点效应,采用分段三次Hemite保形插值算法插值拟合消除欠包络现象以及筛分终止局部准则;对于传统HHT计算瞬时频率的负峰问题采用了标准化和直接正交法解决。采用了LabView平台实现,提高了计算效率和实用性。 The short-time Fourier transform can not meet the different requirements of the high and low frequency changes of signal and the wavelet transform can be limited by the wavelet base we select. Therefore, we use HHT to analyse Che blasting vibration signal through the time-frequency power spectrum. In the EMD, we use the symmetric extending of extreme point to eliminate the effect of endpoint. We use the piecewise cubic Hermite interpolating to eliminate the phenomenon due to the envelope, and adopt the local criterion of sifting termination. For the problem of negative peak in cal- culating the instantaneous frequency in the traditional Hilbert transform, we adopt the normalized Hilbert and the direct quadrature method to solve. In all the HHT analysis, we use the LabView plat- form to achieve which can improve computing efficiency and practicality, and we prove that HHT is useful for the analysis of the blasting vibration signal.
出处 《爆炸与冲击》 EI CAS CSCD 北大核心 2012年第5期535-541,共7页 Explosion and Shock Waves
关键词 振动与波 时频能量谱 希尔伯特-黄变换 爆破振动信号 经验模态分解 vibration and waves time-frequency power spectrum) Hilbert-Huang transform blasting vibration signal empirical mode decomposition
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参考文献4

  • 1Huang N E, WU Zhao-hua, Long S R, et al. On instantaneous frequency[J]. Advances in Adaptive Data Analysis, 2009,1 (2) : 177-229.
  • 2Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition method and the Hilbert spectrum for non- stationary time series analvsis[J].Proceedinzs of the Royal Society of London: A, 1998,454:903-995.
  • 3WU Fang-ji, QU Liang-sheng. An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery[J].Journal of Sound and Vibration, 2008,314 (3) :586-602.
  • 4Niazy R K, Beckmann C F, Brady J M, et al. Performance evaluation of ensemble empirical mode decomposition [J]. Advances in Adaptive Data Analysis, 2009,, 1(2):231-242.

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