基于EEMD分形与二次型SPWV分布的爆破振动信号分析

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摘要通过EEMD(Ensemble Empirical Mode Decomposition)分解和分形盒维数组合算法，结合爆破振动信号统计自相似特性，提出了利用广义自相似性因子来识别爆破振动信号主分量的EEMD分形盒维数广义自相似性方法，并用机车运行振动信号对该方法的精确度进行了验证。将爆破振动原始信号和优势分量重组信号分别采用多重分形算法，定量描述了爆破振动信号在无标度区内的多重分形特征。并对比分析了原始信号和优势分量重组信号的Cohen类二次型SPWV(Smooth Pseudo Wigner-Ville)分布时频特性。结果表明：EEMD分形盒维数广义自相似性方法能够精确识别振动信号的主分量。EEMD优势分量重组多重分形与重组二次型SPWV分布相结合分析方法，可以深刻揭示爆破振动信号中包含的局部时频特性，更能有效改善模态混叠并消除交叉项干扰，提高信号时频聚集性。

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	杨仁树1
	付晓强1
	张世平2
	苏洪1

关键词 ：爆破振动, EEMD分解, 分形盒维度, SPWV分布

Abstract：By combining of EEMD (Ensemble Empirical Mode Decomposition) and fractal box dimension algorithm, also the blasting vibration signal statistical self-similar characteristics,the paper puts forward the EEMD fractal box dimension generalized self-similarity method and uses generalized self-similarity factor to identify the blasting vibration signal principal component.The accuracy of the method is verified by the locomotive vibration signal.A multi-fractal algorithm is used to analyze the original blasting signal and the dominant component restructuring signal,it describes the multi-fractal characteristics of blasting vibration signals in non scale zone quantitatively.Getting the Cohen quadratic SPWV (Smooth Pseudo Wigner-Ville) time-frequency distribution characteristics of original signal and dominant components restructuring signal.The result shows that:the EEMD fractal box dimension generalized self-similarity of method can accurately identify the vibration signal principal component.the combination method of EEMD advantage component restructuring signal multi-fractal and the quadratic SPWV distribution analysis, reveals the local time-frequency characteristics contained in blasting vibration signal,can more effectively improve the modal aliasing and remove cross-term interference,strengthen signal time-frequency aggregation.

收稿日期: 2015-10-15 出版日期: 2016-11-15

引用本文:

杨仁树1，付晓强1，张世平2,苏洪1. 基于EEMD分形与二次型SPWV分布的爆破振动信号分析[J]. 振动与冲击, 2016, 35(22): 41-47.
YANG Ren-shu1,FU Xiao-qiang1,ZHANG Shi-ping2， SU Hong1. Analysis of Blasting Vibration Signal Based on EEMD fractal and Quadratic Time-Frequency SPWV Distribution. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(22): 41-47.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2016/V35/I22/41

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