变分模态分解在爆破信号趋势项去除中的应用

贾贝; 凌天龙; 侯仕军; 刘殿书; 王潇

doi:10.11883/bzycj-2019-0092

变分模态分解在爆破信号趋势项去除中的应用

doi: 10.11883/bzycj-2019-0092

贾贝^1,,
凌天龙¹,
侯仕军¹,
刘殿书^1, ,,
王潇²

1.
中国矿业大学（北京）力学与建筑工程学院，北京 100083
2.
中大爆破公司，北京 102299

详细信息

作者简介:
贾　贝（1993－　），男，博士研究生，m13520710717_1@163.cn

通讯作者:
刘殿书（1960－　），男，博士，教授，博士生导师，lds@cumtb.edu.cn

中图分类号: O389; TD235
计量
- 文章访问数: 5832
- HTML全文浏览量: 2359
- PDF下载量: 98
- 被引次数: 0
出版历程
- 收稿日期: 2019-03-26
- 修回日期: 2019-11-26
- 刊出日期: 2020-04-01

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

1.
School of Resource and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
2.
Beijing Zhongda Blasting Engineering Co. Ltd., Beijing 102299, China

摘要

摘要: 爆破工程中，信号趋势项的准确去除对提高爆破振动信号分析的精度具有重要意义。针对经验模态分解（empirical mode decomposition，EMD）识别法存在的模态混叠和端头效应等缺陷，提出了基于变分模态分解（variational mode decomposition，VMD）去除信号趋势项的方法，即VMD法。叙述了VMD法识别爆破信号趋势项原理，并进行了仿真实验，结果表明：趋势项频率对分解效果的影响相对较小，当趋势项频率处于1～5 Hz之间时，频率对分解效果的影响基本保持不变；振幅对分解效果影响显著，且振幅越小，VMD法的分解效果越差。当趋势项振幅超过原始爆破信号最大振幅的1/3时，VMD法分解效果较好。最后，应用VMD法和EMD法对含有趋势项的实测爆破振动信号进行处理，认为相比于EMD法，VMD法处理后的信号基本一致且不存在端点效应，在爆破信号趋势项去除领域中具有更加广泛的适用性。
- 隧道工程 /
- 爆破振动 /
- 变模态分解（VMD） /
- 趋势项
Abstract: 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.
- tunnel engineering /
- blasting vibration /
- variational mode decomposition (VMD) /
- trend term

HTML全文

图 1 k=2的VMD分解结果

Figure 1. VMD decomposition results of k=2

下载: 全尺寸图片幻灯片

图 2 爆破信号趋势项去除示意图

Figure 2. No trend blasting vibration signal

下载: 全尺寸图片幻灯片

图 3 信号1的VMD法的分解指标分布图

Figure 3. The decomposition effect of VMD method of signal 1

下载: 全尺寸图片幻灯片

图 4 信号2的VMD法的分解指标分布图

Figure 4. VMD decomposition effect of signal 2

下载: 全尺寸图片幻灯片

图 5 VMD法与EMD法的消势结果

Figure 5. VMD and EMD methods

下载: 全尺寸图片幻灯片

表 1 EMD法与VMD法去除趋势项后的信号的波峰数值

Table 1. Peak value of the signal after removing the trend term by EMD method and VMD method

信号	消势方法	质点峰值振速/（m·s⁻¹）
信号	消势方法	波峰1	波峰2	波峰3	波峰4	波峰5	波峰6
1	EMD	0.071	0.055	0.045	0.056	0.055	0.091
1	VMD	0.070	0.053	0.046	0.056	0.052	0.090
2	EMD	0.076	0.063	0.059	0.048	0.125	0.067
2	VMD	0.075	0.064	0.059	0.047	0.123	0.068
3	EMD	0.065	0.047	0.043	0.040	0.042	0.034
3	VMD	0.065	0.046	0.044	0.040	0.041	0.035
4	EMD	0.076	0.022	0.029	0.032	0.030	0.013
4	VMD	0.075	0.022	0.028	0.034	0.032	0.013

下载: 导出CSV

参考文献(18)

[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.