• ISSN 1001-1455  CN 51-1148/O3
  • EI、Scopus、CA、JST收录
  • 力学类中文核心期刊
  • 中国科技核心期刊、CSCD统计源期刊
Volume 40 Issue 4
Apr.  2020
Turn off MathJax
Article Contents
JIA Bei, LING Tianlong, HOU Shijun, LIU Dianshu, WANG Xiao. Application of variable mode decomposition in the removal of blasting signal trend items[J]. Explosion And Shock Waves, 2020, 40(4): 045201. doi: 10.11883/bzycj-2019-0092
Citation: JIA Bei, LING Tianlong, HOU Shijun, LIU Dianshu, WANG Xiao. Application of variable mode decomposition in the removal of blasting signal trend items[J]. Explosion And Shock Waves, 2020, 40(4): 045201. doi: 10.11883/bzycj-2019-0092

Application of variable mode decomposition in the removal of blasting signal trend items

doi: 10.11883/bzycj-2019-0092
  • Received Date: 2019-03-26
  • Rev Recd Date: 2019-11-26
  • Publish Date: 2020-04-01
  • Accurate removal of signal trend items is of practical importance for improving the accuracy of blasting vibration signal analysis. Here, aiming at the defects of EMD identification, such as mode aliasing and terminal effect, a method based on variational mode decomposition (VMD) to remove signal trend term is proposed. The principle of identifying the trend term of blasting signals by VMD method is described in details, and the simulation experiment was carried out. The results show that the influence of the trend term frequency on the decomposition effect is relatively small. When the trend term frequency is between 1 and 5 Hz, the effect of the frequency on the decomposition effect remains basically the same. The amplitude has a significant influence on the decomposition effect. Furthermore, the amplitude is smaller, the decomposition effect of the VMD method is worse. When the amplitude of the trend term exceeds 1/3 of the maximum amplitude of the original blasting signal, the VMD method has a better decomposition effect. Finally, the VMD method and the EMD method are applied to process the measured blasting vibration signal containing the trend term. Compared with the EMD method, the signals processed by the VMD method are basically consistent and have no terminal effect, and have wider applicability in the field of blasting signal trend item removal.
  • loading
  • [1]
    韩亮, 刘殿书, 辛崇伟, 等. 深孔台阶爆破近区振动信号趋势项去除方法 [J]. 爆炸与冲击, 2018, 38(5): 1006–1012. DOI: 10.11883/bzycj-2016-0194.

    HAN L, LIU D S, XIN C W, et al. Trend removing methods of vibration signals of deep hole bench blasting in near field [J]. Explosion and Shock Waves, 2018, 38(5): 1006–1012. DOI: 10.11883/bzycj-2016-0194.
    [2]
    何鹏举, 冯亮. 加速度信号随机噪声及趋势项实时消除方法研究 [J]. 电子设计工程, 2013, 21(14): 18–22. DOI: 10.3969/j.issn.1674-6236.2013.14.006.

    HE P J, FENG L. Study on the real-time elimination method of random noise and trend terms in acceleration signal [J]. Electronic Design Engineering, 2013, 21(14): 18–22. DOI: 10.3969/j.issn.1674-6236.2013.14.006.
    [3]
    肖立波, 任建亭, 杨海峰. 振动信号预处理方法研究及其MATLAB实现 [J]. 计算机仿真, 2010, 27(8): 330–333. DOI: 10.3969/j.issn.1006-9348.2010.08.081.

    XIAO L B, REN J T, YANG H F. Study on vibration signal pre-processing method based on MATLAB [J]. Computer Simulation, 2010, 27(8): 330–333. DOI: 10.3969/j.issn.1006-9348.2010.08.081.
    [4]
    龙源, 谢全民, 钟明寿, 等. 爆破震动测试信号预处理分析中趋势项去除方法研究 [J]. 工程力学, 2012, 29(10): 63–68. DOI: 10.6052/j.issn.1000-4750.2011.02.0093.

    LONG Y, XIE Q M, ZHONG M S, et al. Research on trend removing methods in preprocessing analysis of blasting vibration monitoring signals [J]. Engineering Mechanics, 2012, 29(10): 63–68. DOI: 10.6052/j.issn.1000-4750.2011.02.0093.
    [5]
    李夕兵, 张义平, 刘志祥, 等. 爆破震动信号的小波分析与HHT变换 [J]. 爆炸与冲击, 2005, 25(6): 528–535. DOI: 10.11883/1001-1455(2005)06-0528-08.

    LI X B, ZHANG Y P, LIU Z X, et al. Wavelet analysis and Hilbert Huang transform of blasting vibration signal [J]. Explosion and Shock Waves, 2005, 25(6): 528–535. DOI: 10.11883/1001-1455(2005)06-0528-08.
    [6]
    张庆华, 王太勇, 徐燕申. 小波分析在压缩机噪声信号去除趋势项处理中的应用 [J]. 中国制造业信息化, 2003(2): 114–116. DOI: 10.3969/j.issn.1672-1616.2003.02.034.

    ZHANG Q H, WANG T Y, XU Y S. Application of wavelet analysis in noise signal of compressor [J]. Machine Design and Manufacturing Engineering, 2003(2): 114–116. DOI: 10.3969/j.issn.1672-1616.2003.02.034.
    [7]
    张胜, 凌同华, 曹峰, 等. 模式自适应连续小波去除趋势项方法在爆破振动信号分析中的应用 [J]. 爆炸与冲击, 2017, 37(2): 255–261. DOI: 10.11883/1001-1455(2017)02-0255-07.

    ZHANG S, LING T H, CAO F, et al. Application of removal trend method of patter adapted continuous wavelet to blast vibration signal analysis [J]. Explosion and Shock Waves, 2017, 37(2): 255–261. DOI: 10.11883/1001-1455(2017)02-0255-07.
    [8]
    凌同华, 张胜, 陈倩倩, 等. 模式自适应小波构造与添加及其在爆破振动信号分析中的应用 [J]. 振动与冲击, 2014, 33(12): 53–57. DOI: 10.13465/j.cnki.jvs.2014.12.009.

    LING T H, ZHANG S, CHEN Q Q, et al. Pattern adapted wavelet construction and addition and its application in blast vibration signal analysis [J]. Journal of Vibration and Shock, 2014, 33(12): 53–57. DOI: 10.13465/j.cnki.jvs.2014.12.009.
    [9]
    张慧娟, 李冬. 相关系数判决的EMD在振动数据趋势项提取中的应用 [J]. 舰船电子工程, 2018, 38(5): 159–163. DOI: 10.3969/j.issn.1672-9730.2018.05.038.

    ZHANG H J, LI D. Application of EMD of correlation coefficient judgment in the extraction of vibration data trend [J]. Ship Electronic Engineering, 2018, 38(5): 159–163. DOI: 10.3969/j.issn.1672-9730.2018.05.038.
    [10]
    郭小红, 徐小辉, 赵树强. 基于经验模态分解的外弹道降噪方法及应用 [J]. 宇航学报, 2008(4): 1272–1275. DOI: 10.3873/j.issn.1000-1328.2008.04.034.

    GUO X H, XU X H, ZHAO S Q. Control allocation strategy for composite control of new unmanned-air vehicle [J]. Journal of Astronautics, 2008(4): 1272–1275. DOI: 10.3873/j.issn.1000-1328.2008.04.034.
    [11]
    DRAGOMIRETSKIY K,ZOSSO D. Variational mode decomposition [J]. Transactions on Signal Processing, 2013, 10(1109): 1–15. DOI: 10.1109/TSP.2013.2288675.
    [12]
    刘宏波. 基于改进VMD-HT的电力系统低频振荡模态辨识[D]. 哈尔滨: 哈尔滨工业大学, 2018.
    [13]
    吴文轩, 王志坚, 张纪平, 等. 基于峭度的VMD分解中k值的确定方法研究 [J]. 机械传动, 2018, 42(8): 153–157.

    WU W X, WANG Z J, ZHANG J P, et al. Research of the method of determining k value in VMD based on kurtosis [J]. Journal of Mechanical Transmission, 2018, 42(8): 153–157.
    [14]
    马洪斌, 佟庆彬, 张亚男. 优化参数的变分模态分解在滚动轴承故障诊断中的应用 [J]. 中国机械工程, 2018, 29(4): 390–397. DOI: 10.3969/j.issn.1004-132X.2018.04.003.

    MA H B, ZHAI Q B, ZHANG Y N. Application of optimization parameters VMD to fault diagnosis of rolling bearings [J]. China Mechanical Engineering, 2018, 29(4): 390–397. DOI: 10.3969/j.issn.1004-132X.2018.04.003.
    [15]
    蒲子玺, 殷红, 张楠, 等. 基于峭度准则VMD及平稳小波的轴承故障诊断 [J]. 机械设计与研究, 2017, 33(1): 67–71.

    PU Z X, YIN H, ZHANG N, et al. Bearing fault diagnosis using VMD and stationary wavelet method based on kurtosis criterion [J]. Machine Design and Research, 2017, 33(1): 67–71.
    [16]
    马增强, 李亚超, 刘政, 等. 基于变分模态分解和Teager能量算子的滚动轴承故障特征提取 [J]. 振动与冲击, 2016, 35(13): 134–139. DOI: 10.13465/j.cnki.jvs.2016.13.022.

    MA Z Q, LI Y C, LIU Z, et al. Rolling bearings' fault feature extraction based on variational mode decomposition and Teager energy operator [J]. Journal of Vibration and Shock, 2016, 35(13): 134–139. DOI: 10.13465/j.cnki.jvs.2016.13.022.
    [17]
    刘长良, 武英杰, 甄成刚. 基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断 [J]. 中国电机工程学报, 2015, 35(13): 3358–3365. DOI: 10.13334/j.0258-8013.pcsee.2015.13.020.

    LIU C L, WU Y J, ZHEN C G. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering [J]. Proceedings of the CSEE, 2015, 35(13): 3358–3365. DOI: 10.13334/j.0258-8013.pcsee.2015.13.020.
    [18]
    赵昕海, 张术臣, 李志深, 等. 基于VMD的故障特征信号提取方法 [J]. 振动、测试与诊断, 2018, 38(1): 11–19+202. DOI: 10.16450/j.cnki.issn.1004-6801.2018.01.002.

    ZHAO X H, ZHANG S C, LI Z C, et al. Application of new denoising method based on VMD in fault feature extraction [J]. Journal of Vibration, Measurement & Diagnosis, 2018, 38(1): 11–19+202. DOI: 10.16450/j.cnki.issn.1004-6801.2018.01.002.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(1)

    Article Metrics

    Article views (5830) PDF downloads(98) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return