基于改进的MEEMD的隧道掘进爆破振动信号去噪优化分析

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (10) : 74-81.

论文

周红敏1，赵事成1，赵文清2，王双2，郝广伟2，张宪堂1

作者信息 +

Vibration signal denoising optimization analysis in tunnel excavation based on improved MEEMD

ZHOU Hongmin1，ZHAO Shicheng1，ZHAO Wenqing2，WANG Shuang2，HAO Guangwei2，ZHANG Xiantang1

Author information +

文章历史 +

摘要

爆破振动信号受现场条件限制，多为复杂含噪信号，对降噪方法的性能提出更高要求。为了获得真实振动特征，建立了一种基于改进的总体平均经验模态算法（modified ensemble empirical mode decomposition, MEEMD）的联合去噪方法。首先，将原始信号进行MEEMD分解得到本征模态分量（intrinsic mode function,IMF），结合相关系数和样本熵（sample entropy,SE）-Hurst指数进行IMF分类。然后，针对含噪IMF分量中的残留噪声，使用最小均方自适应滤波（least mean square,LMS）进行降噪，达到信号去噪的目的。算法对比结果表明：仿真试验中MEEMD-LMS相较互补集合经验模态分解（complementary ensemble empirical mode decomposition,CEEMD）、快速总体平均经验模式分解（fast ensemble empirical mode decomposition,FEEMD）等方法表现出更优的降噪性能，隧道掘进爆破的实例分析中MEEMD-LMS相较MEEMD对高频噪声的降噪效果更好，低频段频谱更清晰，具备良好的适用性。

Abstract

Blasting vibration signals are mostly contain noise limited by field conditions, higher requirements are put forward for the performance of noise reduction methods. In order to obtain the real vibration characteristics, the study establishes a joint denoising method based on the improved modified ensemble empirical mode decomposition (MEEMD） algorithm. Firstly, the original signal was decomposed with MEEMD to obtain the intrinsic mode function (IMF), and the IMF was classified with combining the correlation coefficient and sample entropy (SE）-Hurst index. Then, the least mean square (LMS）was used to filter the residual noise in the noisy IMF component to finish signal denoising. The algorithm comparison results show that MEEMD-LMS has better noise reduction performance than complementary ensemble empirical mode decomposition (CEEMD） and fast ensemble empirical mode decomposition (FEEMD）in the simulation experiment. In the case analysis of tunnel excavation blasting, MEEMD-LMS has better noise reduction effect on high frequency noise than MEEMD, and the low frequency spectrum is clearer. The noise reduction model has good applicability.

导出引用

周红敏1，赵事成1，赵文清2，王双2，郝广伟2，张宪堂1. 基于改进的MEEMD的隧道掘进爆破振动信号去噪优化分析[J]. 振动与冲击, 2023, 42(10): 74-81

ZHOU Hongmin1，ZHAO Shicheng1，ZHAO Wenqing2，WANG Shuang2，HAO Guangwei2，ZHANG Xiantang1. Vibration signal denoising optimization analysis in tunnel excavation based on improved MEEMD[J]. Journal of Vibration and Shock, 2023, 42(10): 74-81

参考文献

[1] 梁琨，王树欣，张宪堂，等.大跨度小净距隧道爆破振动响应研究[J]. 爆破，2021, 38(2): 67-72+159.
LIANG Kun, WANG Shu-xin, ZHANG Xian-tang, et al. Response of large-span tunnel to blasting vibration of small clear spacing tunnel excavation [J]. Blasting, 2021, 38(2): 67-72+159.
[2] 陈吉辉，仇文革，赵旭伟，等.基于小波包技术地铁隧道分区爆破振动特性研究[J].振动与冲击，2022, 41(6): 222-228+255.
CHEN Ji-hui, QIU Wen-ge, ZHAO Xu-wei, et al. Vibration characteristics analysis of the metro tunnel subarea blasting based on wavelet packet technique [J]. Journal of Vibration and Shock, 2022, 41(6): 222-228+255.
[3] 易文华，刘连生，闫雷，等.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): 77-87.
YI Wen-hua, LIU Lian-sheng, YAN Lei, et al. Vibration signal denoising based on improved EMD algorithm [J]. Explosion and Shock Waves, 2020, 40(9): 77-87.
[4] 曹晓立，高文学，吕洪涛，等. 爆破振动信号的Hilbert-Huang变换分析与应用研究[J]. 兵工学报，2016, 37(S2): 107-113.
CAO Xiao-li, GAO Wen-xue, LV Hong-tao, et al. Analysis of blasting vibration signal by HHT transform [J]. Acta Armamentarii, 2016, 37(S2): 107-113.
[5] 叶红宇，卓越，杨小林. 隧道爆破振动信号EEMD分解后小波包降噪法研究[J]. 铁道建筑，2018, 58(7): 83–86.
YE Hong-yu, ZHUO Yue, YANG Xiao-lin. Research on wavelet packet denoising of tunnel blasting vibration signals after EEMD decomposition [J]. Railway Engineering, 2018, 58(7): 83-86.
[6] 吴石，张轩瑞，刘献礼. 基于CEEMD和GWO-SVR的铣削振动信号前瞻预测[J]. 振动与冲击，2022, 41(11): 199-209+234.
WU Shi, ZHANG Xuan-rui, LIU Xian-li. Forward-looking prediction of milling vibration signal based on CEEMD and GWO-SVR [J]. Journal of Vibration and Shock, 2022, 41(11): 199-209+234.
[7] 郑近德，程军圣，杨宇.改进的EEMD算法及其应用研究[J].振动与冲击，2013, 32(21): 21-26+46.
ZHENG Jin-de, CHENG Jun-sheng, YANG Yu. Modified EEMD algorithm and its applications [J]. Journal of Vibration and Shock, 2013, 32(21): 21-26+46.
[8] 陈友广，陈云，谢鲲鹏. 基于MEEMD-SDP图像特征和DRN的行星齿轮箱故障诊断[J]. 机电工程，2022, 39(5): 662-667.
CHEN You-guang, CHEN Yun, XIE Kun-peng. Planetary gearbox fault diagnosis based on MEEMD-SDP image features and DRN [J]. Journal of Mechanical & Electrical Engineering, 2022, 39(5): 662-667.
[9] 黄涛涛. 基于改进MEEMD的滚动轴承故障诊断方法研究[D]. 浙江师范大学，2021.
HUANG Tao-tao. Research on fault diagnosis method of rolling bearing based on improved MEEMD [D]. Zhejiang Normal University, 2021.
[10] 彭亚雄，刘广进，苏莹，等.基于变分模态分解算法的隧道爆破振动信号光滑降噪模型[J].振动与冲击，2021, 40(24): 173-179.
PENG Ya-xiong, LIU Guang-jin, SU Ying, et al. A smooth denoising model of tunnel blasting vibration signal based on VMD [J]. Journal of Vibration and Shock, 2021, 40(24): 173-179.
[11] 赵彤，梁家碧，夏天翔，等.基于LMS自适应滤波算法的电力变压器有源降噪系统[J].高电压技术，2016, 42(7): 2299-2307.
ZHAO Tong, LIANG Jia-bi, XIA Tian-xiang，et al. Active noise control system based on LMS adaptive filter algorithm for transformer power noise reduction [J]. High voltage engineering [J]. 2016, 42(7): 2299-2307.
[12] 范文健，毛万鑫,吴疆.车辆加速度信号的EMD和IIR滤波联合降噪方法[J]. 振动与冲击，2021, 40(20): 307-312.
FAN Wen-jian, MAO Wan-xin, WU Jiang. Combined denoising method of vehicle acceleration signal based on EMD and IIR filtering [J]. Journal of Vibration and Shock, 2021, 40(20): 307-312.
[13] 蒋沅，上官彪，曾竞凯.基于EEMD的改进小波阈值算法在超声水表中的应用[J].振动与冲击，2022, 41(5): 208-213.
JIANG Yuan, SHANG Guan-biao, ZENG Jing-kai. Application of improved wavelet threshold algorithm based on EEMD in ultrasonic water meter [J]. Journal of Vibration and Shock, 2022, 41(5): 208-213.
[14] 于淼，张耀鲁，徐泽辰，等.基于MEEMD-HHT的分布式光纤振动传感系统信号特征提取方法 [J].红外与激光工程，2021,50(7):207-218.
YU Miao, Zhang Yao-lu, Xu Ze-chen, et al.Signal feature extraction method based on MEEMD-HHT for distributed optical fiber vibration sensing system[J]. Infrared and Laser Engineering, 2021, 50(7): 207-218.
[15] 江友华，朱毅轩，江相伟，等.基于改进LMS自适应滤波的局部放电噪声抑制方法[J].科学技术与工程，2022, 22(3): 1039-1047.
JIANG You-hua, ZHU Yi-xuan, JIANG Xiang-wei, et al. Partial discharge noise suppression method based on improved LMS adaptive filtering [J]. Science Technology and Engineering, 2022, 22(3): 1039-1047.
[16] 蒋雄杰，罗雨颋，王印松，等.基于Hurst指数的火电机组控制系统性能评价方法[J].控制工程，2021, 28(9):1850-1855.
JIANG Xiong-jie, LUO Yu-ting, WANG Yin-song, et al. Performance assessment method of thermal power unit control system based on Hurst exponent[J]. Control Engineering of China, 2021, 28(9): 1850-1855.
[17] 刘晓悦，张泽明，赵立国，等.基于CEEMDAN样本熵和SSA-ELM的风机齿轮箱故障诊断[J]. 组合机床与自动化加工技术，2022(9):126-130+134.
LIU Xiao-yue, ZHANG Ze-ming, ZHAO Li-guo, et al. Fault diagnosis of wind turbine gearbox based on CEEMDAN sample entropy and SSA-ELM [J]. Modular Machine Tool ＆ Automatic Manufacturing Technique, 2022(9):126-130+134.
[18] 龚云，信杰，南守琎. 一种引入Hurst指数的MEMS陀螺仪去噪模型 [J]. 大地测量与地球动力学，2022,42(5):457-461.
GONG Yun, XIN Jie, NAN Shou-jin. A denosing model of MEMS gyroscope with Hurst exponent [J]. Modular Machine Tool ＆ Automatic Manufacturing TechniqueJournal of Geodesy and Geodynamics, 2022,42(5):457-461.
[19] 苟倩倩，赵明生，张光熊，等. 基于MEEMD分解的楔形掏槽爆破振动信号分析[J]. 矿业研究与开发，2019, 39(10): 11-15.
GOU Qian-qian, ZHAO Ming-sheng, ZHANG Guang-xiong, et al. Analysis on blasting vibration signal in wedge-shaped cut based on MEEMD Decomposition [J]. Mining Research and Development, 2019, 39(10): 11-15.
[20] 李聪改，曹锐，武政，等. 基于样本熵的时间序列非线性检测方法研究[J]. 计算机工程与设计，2014, 35(11): 4017-4020+4048.
LI Cong-gai, CAO Rui, WU Zheng, et al. Nonlinear detection study of time series based on sample entropy [J]. Computer Engineering and Design, 2014, 35(11): 4017-4020+4048.
[21] 孙苗. 爆破地震波信号处理HHT改进算法及应用研究[D]. 中国地质大学，2021.
SUN Miao. Improved HHT algorithm for blasting seismic wave signal processing and its application [J]. Wuhan: China University of Geosciences (Wuhan), 2021.
[22] 孙振，谢事臻，张宪堂，等. 减震孔在小间距隧道爆破开挖中的降振效应[J]. 山东科技大学学报（自然科学版），2019, 38(1): 25-31+39.
SUN Zhen, XIE Shi-zhen, ZHANG Xian-tang, et al. Vibration reduction effect of damping holes in blasting excavation of tunnels with small spacing [J]. Journal of Shandong University of Science and Technology(Natural Science), 2019, 38(1): 25-31+39.