[1]王海龙①②,李云赫①②,赵岩③.k值优化VMD-小波包分析联合降噪方法在隧道爆破信号中的应用[J].爆破器材,2021,50(05):50-57.[doi:10.3969/j.issn.1001-8352.2021.05.009]
　WANG Hailong,LI Yunhe,ZHAO Yan.Application of Denoising Method of k?Value Optimized VMD Combined with Wavelet Packet Analysis in Tunnel Blasting Signal[J].EXPLOSIVE MATERIALS,2021,50(05):50-57.[doi:10.3969/j.issn.1001-8352.2021.05.009]
点击复制

k值优化VMD-小波包分析联合降噪方法在隧道爆破信号中的应用()
分享到：

《爆破器材》[ISSN:1001-8352/CN:32-1163/TJ]

卷:
50
期数:
2021年05
页码:
50-57
栏目:
爆破技术
出版日期:
2021-10-05

文章信息/Info

Title:
Application of Denoising Method of k?Value Optimized VMD Combined with Wavelet Packet Analysis in Tunnel Blasting Signal
文章编号:
5590
作者:
王海龙①②; 李云赫①②; 赵岩③
①河北建筑工程学院 土木工程学院(河北张家口, 075000)
②河北省土木工程诊断、改造与抗灾重点试验室(河北张家口, 075000)
③中国矿业大学(北京) 力学与建筑工程学院(北京, 100083)
Author(s):
WANG Hailong①②;  LI Yunhe①②; ZHAO Yan③
①School of Civil Engineering, Hebei University of Architecture (Hebei Zhangjiakou, 075000)
②Hebei Key Laboratory for Diagnosis, Reconstruction and Anti-Disaster of Civil Engineering (Hebei Zhangjiakou, 075000)
③School of Mechanics and Civil Engineering, China University of Mining and Technology (Beijing) (Beijing, 100083)
关键词:
隧道爆破振动信号; VMD; 小波包分析; 降噪
Keywords:
tunnel blasting vibration signal;  VMD;  wavelet packet analysis;  denoising
分类号:
TU751.9
DOI:
10.3969/j.issn.1001-8352.2021.05.009
文献标志码:
A
摘要:
针对隧道爆破振动测试信号存在噪声干扰的问题，引入一种基于k值优化的变分模态分解(variational mode decomposition，简称VMD)联合小波包分析的降噪方法。首先，引入分解能量差值参数λ，对比爆破振动信号在不同k值条件下经VMD得到的模态分量总能量；基于等能量分解原理对模态数k进行优化分析，并在最佳模态数k下对信号进行VMD处理；在相关系数和方差贡献率双指标下筛选出含噪分量,并用小波包分析手段进行降噪处理；最后，将经降噪处理后的含噪分量与优势分量重构,得到纯净的爆破振动信号。引入的方法兼具VMD及小波包分析的优点，并克服了信号分解过分或分解层数不足的缺陷。结果表明:与现有方法相比，k值优化的VMD-小波包分析联合降噪方法信噪比高，均方根差小，降噪效果良好，并且该法可有效保留原始信号中的细节特征，可以应用于类似隧道爆破信号的降噪处理。
Abstract:
Aiming at the problem of noise interference in tunnel blasting vibration test signals, a noise reduction method based onk-value optimization of VMD (variational mode decomposition) combined with wavelet packet analysis was introduced. First, decomposition energy difference parameter λwas introduced to compare the total energy of the modal components of blasting vibration signals obtained by VMD with differentkvalues. Based on the principle of equal energy decomposition, the modal numberkwas optimized and analyzed, and VMD of the signal was carried out under the optimal mode number k. Noisy components were screened out under the dual indicators of correlation coefficient and variance contribution rate, and wavelet packet analysis was used in denoising. Finally, the noise components and dominant components after noise reduction were reconstructed to obtain a pure blasting vibration signal. The method introduced in this paper combines the advantages of VMD and wavelet packet analysis, and overcomes the defects of excessive signal decomposition or insufficient decomposition layers. The results show that compared with existing methods, the denoising method of k-value optimized VMD combined with wavelet packet analysis has a high signal-to-noise ratio, a small root mean square error, and a good noise reduction effect. This method can effectively retain the detailed features in the original signal, and it can be applied to noise reduction of what is similar to tunnel blasting signals.

参考文献/References:

［1］王海龙,赵岩,王海军,等.基于CEEMDAN-小波包分析的隧道爆破信号去噪方法［J］.爆炸与冲击: 2021,41（5）:055202.
WANG H L, ZHAO Y, WANG H J,et al. De-noising method of tunnel blasting signal based on CEEMDAN decomposition-wavelet packet analysis ［J］. Explosion and Shock: 2021:41（5）：055202.
［2］张晓鸽. 基于振动信号分析的滚动轴承故障诊断仪的设计与实现［D］.重庆：重庆大学,2013.
ZHANG X G. Design and implementation of fault diagnosis instrument for rolling bearing based on vibration signal analysis ［D］. Chongqing:Chongqing University, 2013.
［3］曹洁梅.基于短时傅里叶变换的岩石声波信号分析方法研究［J］.路基工程,2018(6):27-30,34.
CAO J M. Study on method analying signal of rock based on short-time Fourier transform［J］. Subgrage Engineering, 2018(6): 27-30,34.
［4］陈泽,丁琳琳,罗浩,等.基于改进小波分解和ELM的矿山微震事件识别方法［J］.煤炭学报:2020,45(增刊2）:637-648.
CHEN Z, DING L L, LUO H, et al. Mine microseismic event classification based on improved wavelet decomposition and ELM ［J］. Journal of China Coal Society， 2020，45（Suppl.2）:637-648.
［5］HASSAN A R , SUBASI A , ZHANG Y C . Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise［J］. Knowledge-Based Systems,2019,191(3):105333.
［6］陈旭升,张献州,蒋英豪,等.局部均值分解的新小波阈值去噪法及其应用［J］.测绘科学:2021,46(2):48-54.
CHEN X S, ZHANG X Z, JIANG Y H, et al. New wavelet threshold de-noising method based on local mean decomposition and its application ［J］. Science of Surveying and Mapping，2021,46(2):48-54.
［7］赵明生,梁开水,罗元方,等.EEMD在爆破振动信号去噪中的应用［J］.爆破,2011,28(2):17-20,59.
ZHAO M S, LIANG K S, LUO Y F, et al. Application of EEMD in blasting vibration signal de-noising［J］. Blasting, 2011, 28(2): 17-20,59.
［8］邵东辉.基于CEEMD低通的隧道爆破振动信号去噪［J］.工程爆破,2017,23(6):5-10.
SHAO D H. Denoising of tunnel blasting vibration signal based on CEEMD lowpass ［J］. Engineering Blasting, 2017, 23(6): 5-10.
［9］王奉涛,柳晨曦,张涛,等.基于k值优化VMD的滚动轴承故障诊断方法［J］.振动、测试与诊断,2018,38(3):540-547.
［10］FU J J, CAI F Y, GUO Y H, et al. An improved VMD-based denoising method for time domain load signal combining wavelet with singular spectrum analysis［J］. Mathematical Problems in Engineering, 2020(1):1-14.
［11］刘玉桥,邓红卫,吴路波,等.基于VMD联合小波阈值去噪法的微震监测信号去噪研究［J］.矿业研究与开发,2020,40(2):98-103.
LIU Y J, DENG H W, WU L B, et al. Study on signal denoising of microseismic monitoring based on combined variational mode decomposition and wavelet threshold method［J］. Mining Research and Development, 2020, 40(2): 98-103 .
［12］SIMAR L, ZELENYUK V. Improving finite sample approximation by central limit theorems for estimates from data envelopment analysis［J］. European Journal of Operational Research,2020,284(3):1002-1015.
［13］余腾,胡伍生,吴杰,等.基于小波阈值去噪与EMD分解方法提取润扬大桥振动信息［J］.振动与冲击,2019,38(12):264-270.
YU T, HU W S, WU J, et al. Extraction of Runyang Bridge vibration information based on a fusion method of wavelet threshold denoising and EMD decomposition［J］. Journal of Vibration and Shock, 2019, 38(12): 264-270 .
［14］HUANG D, CUI S, LI X Q. Wavelet packet analysis of blasting vibration signal of mountain tunnel［J］. Soil Dynamics and Earthquake Engineering 2019,117:72-80.
［15］贾贝,凌天龙,侯仕军,等.变分模态分解在爆破信号趋势项去除中的应用［J］.爆炸与冲击,2020,40(4):123-131.
JIA B, LING T L, HOU S J, et al. Application of va-riational mode decomposition in the removal of blasting signal trend item ［J］. Explosion and Shock, 2020, 40(4): 123-131.
［16］施晶,朱明,沈华,等.基于VMD和小波阈值的水听器信号去噪方法［J］.传感技术学报,2020,33(7):1003-1012.
SHI J, ZHU M, SHEN H, et al. Denoising method of hydrophone signal based on VMD and wavelet threshold ［J］. Chinese Journal of Sensors and Actuators, 2020, 33(7): 1003-1012.
［17］李火坤,刘世立,魏博文,等.基于方差贡献率的泄流结构多测点动态响应融合方法研究［J］.振动与冲击,2015,34(19):181-191.
LI H K, LIU S L, WEI B W, et al. Muti-point dynamic response fusion method for a flood discharge structure based on variance dedication rate［J］. Journal of Vibration and Shock, 2015, 34(19): 181-191.
［18］LI H, LIU T, WU X, et al. An optimized VMD method and its applications in bearing fault diagnosis［J］. Measurement, 2020, 166(6): 108185.

相似文献/References:

[1]王海龙①,李帅①,赵岩②,等.CEEMDAN小波包联合降噪的优化方法[J].爆破器材,2021,50(04):48.[doi:10.3969/j.issn.1001-8352.2021.04.009]
　WANG Hailong,LI Shuai,ZHAO Yan,et al.Optimization Method of CEEMDAN-Wavelet Packet Joint Noise Reduction[J].EXPLOSIVE MATERIALS,2021,50(05):48.[doi:10.3969/j.issn.1001-8352.2021.04.009]
[2]张佳①,赵岩②.基于CEEMDAN法的隧道爆破信号趋势项去除[J].爆破器材,2021,50(06):58.[doi:10.3969/j.issn.1001-8352.2021.06.010]
　ZHANG Jia,ZHAO Yan.Removal of Trend Items of Tunnel Blasting Signals Based on CEEMDAN[J].EXPLOSIVE MATERIALS,2021,50(05):58.[doi:10.3969/j.issn.1001-8352.2021.06.010]

备注/Memo

备注/Memo:
收稿日期：2021-04-02
基金项目：国家自然科学基金项目(51878242)；河北建筑工程学院校级基金项目（XY202002）
第一作者：王海龙（1965-），男，教授，博导，主要从事隧道工程的安全性评价与超前支护机理研究。E-mail:wanghailong-65@163.com
通信作者：李云赫（1996-），女，硕士研究生，主要从事隧道工程的安全性评价与超前支护机理研究。E-mail:983645604@qq.com
更新日期/Last Update: 2021-10-05