基于EMD改进算法的爆破振动信号去噪

易文华 刘连生 闫雷 董斌斌

易文华, 刘连生, 闫雷, 董斌斌. 基于EMD改进算法的爆破振动信号去噪[J]. 爆炸与冲击, 2020, 40(9): 095201. doi: 10.11883/bzycj-2019-0471
引用本文: 易文华, 刘连生, 闫雷, 董斌斌. 基于EMD改进算法的爆破振动信号去噪[J]. 爆炸与冲击, 2020, 40(9): 095201. doi: 10.11883/bzycj-2019-0471
YI Wenhua, LIU Liansheng, YAN Lei, DONG Binbin. Vibration signal de-noising based on improved EMD algorithm[J]. Explosion And Shock Waves, 2020, 40(9): 095201. doi: 10.11883/bzycj-2019-0471
Citation: YI Wenhua, LIU Liansheng, YAN Lei, DONG Binbin. Vibration signal de-noising based on improved EMD algorithm[J]. Explosion And Shock Waves, 2020, 40(9): 095201. doi: 10.11883/bzycj-2019-0471

基于EMD改进算法的爆破振动信号去噪

doi: 10.11883/bzycj-2019-0471
基金项目: 国家自然科学基金(51404111);江西省自然科学基金(20192BAB206017);江西理工大学清江优秀人才支持计划(JXUSTQJYX2016007)
详细信息
    作者简介:

    易文华(1996- ),男,硕士研究生,yiwenhua0918@163.com

    通讯作者:

    刘连生(1979- ),男,博士,教授,lianshengliu@jxust.edu.cn

  • 中图分类号: O389

Vibration signal de-noising based on improved EMD algorithm

  • 摘要: 为了解决振动信号经验模态分解(empirical mode decomposition, EMD)滤波去噪效果不佳的问题,提出一种自适应性正交经验模态分解(principal empirical mode decomposition, PEMD)的信号去噪方法。该算法融合了EMD分解的自适应性和主成分分析(principal component analysis,PCA)的完全正交性特点,对信号EMD分解过程中产生的模态混叠现象进行消除,得到了最佳的去噪效果。分析表明:PEMD在仿真模拟试验中相比于传统EMD算法和集总经验模态分解(ensemble empirical mode decomposition, EEMD) 算法,信噪比分别提高了1.15 dB和0.38 dB,且均方根误差最小;频域上PEMD对仿真信号频率(30 Hz)识别的灵敏度最高,30 Hz之外的噪声滤除效果最好。在爆破振动试验中,PEMD和EEMD去除噪声毛刺的效果较为理想,且PEMD在0~300 Hz的中低频振动信号保存效果最好,300 Hz以上的高频噪声滤除效果最好。
  • 图  1  PEMD算法流程图

    Figure  1.  PEMD algorithm flow chart

    图  2  仿真信号IMF分量与频谱

    Figure  2.  IMF component and spectrum of simulation signal

    图  3  正交信号与仿真信号频谱对比

    Figure  3.  Spectrum comparison between orthogonal signal and simulated signal

    图  4  IMF分量自相关函数特性曲线

    Figure  4.  Characteristic curves of IMF component autocorrelation function

    图  5  EMD、EEMD和PEMD去噪信号时域对比

    Figure  5.  Comparison of EMD, EEMD and PEMD de-noising signal time domain

    图  6  EMD、EEMD和PEMD去噪信号频谱对比

    Figure  6.  Comparison of EMD, EEMD and PEMD de-noising signal spectrum

    图  7  地质地形及监测点布置图

    Figure  7.  Geological topography and layout of the monitoring site

    图  8  EMD、EEMD和PEMD去噪信号时域对比

    Figure  8.  Comparison of EMD, EEMD and PEMD de-noising signal time domain

    图  9  EEMD与PEMD去噪信号频谱对比

    Figure  9.  Comparison of EEMD and PEMD de-noising signal spectrum

    表  1  主成分变量信息贡献率

    Table  1.   Principal component variable information contribution rate

    主成分变量${y_1}$${y_2}$${y_3}$${y_4}$${y_5}$${y_6}$${y_7}$${y_8}$${y_9}$
    信息贡献率/%14.8813.6311.9711.4410.4910.439.809.168.21
    下载: 导出CSV

    表  2  去噪效果评价指标

    Table  2.   Evaluation index of de-noising effect

    去噪算法 γ$\sigma $
    EMD 2.832.04
    EEMD 3.601.87
    PEMD 3.981.79
    下载: 导出CSV

    表  3  不同测点的爆破参数

    Table  3.   Blasting parameters of different measuring points

    测点最大段药量/kg水平距离/m高程差/m
    13 17219020
    23 17228640
    33 17231150
    43 17233060
    53 17235080
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-16
  • 修回日期:  2020-03-10
  • 网络出版日期:  2020-07-25
  • 刊出日期:  2020-09-01

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