Distortion correction and fractal characteristics of vibration signals of a tunnel blasting
FU Xiaoqiang1,2,3，YU Jin2，LIU Jifeng1，YANG Renshu4，DAI Liangyu3
1.School of Civil Engineering, Sanming University, Sanming 365004, China;
2.Fujian Research Center for Tunneling and Urban Underground Space Engineering, Huaqiao University, Xiamen 361021, China;
3.Sanming Coffer Fine Chemical Industrial Co., Ltd., Sanming 365500, China;
4.School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China
摘要受测试环境影响，隧道爆破监测信号中普遍包含噪声和趋势项干扰。针对爆破信号干扰项消除难题，选取典型地铁隧道工程监测到的畸变爆破信号为分析对象，采用BEADS(baseline estimation and denoising with sparsity)算法实现了噪声和趋势项成分的提取，得到反映真实爆破信息的校正信号。利用多重分形去趋势波动分析(multi-fractal detrended fluctuation analyses,MF-DFA)捕捉到三个分量信号的混沌分形特征，并根据小波相关性凝聚谱对三个分量信号与原始信号的时频域相关性进行了精确表征。结果表明：隧道爆破信号高频噪声、低频趋势项和校正信号三者的混沌分形特征具有显著差异。校正信号吸引子轨迹形态为反复周期性有序波动且具有持续性和反持续性分形谱特征，其递归图具有周期模式；低频趋势项吸引子形态表现为近似直线且具有持续性分形谱特征，其递归图具有对角线分布突变模式；高频噪声吸引子形态为杂乱无章的随机波动且具有反持续性分形谱特征，其递归图具有漂移模式。在置信度为95%的小波影响锥范围内，校正信号、趋势项和噪声分量与原始信号分别具有持续正相关、局部负相关和无相关性特征。三类信号的有效分离和混沌分形特征提取为爆破信号成分的准确辨识和归类提供了客观表征和量化指标。
Abstract：Affected by the test environment, the monitoring vibration signals of tunnel blasting generally contain noise and trend interference components. To eliminate the interference items, the distorted blasting signals detected in typical tunnel were selected as the analysis objects. Baseline estimation and denoising with sparsity (BEADS) was used to extract noise and trend item, to obtain the calibrated signal that reflecting the true information. The chaotic fractal characteristics of three components are captured by multi-fractal detrended fluctuation analyses（MF-DFA）, and the time-frequency domain correlation between them and the original signal is accurately characterized according to the wavelet correlation aggregation spectrum. The chaotic fractal characteristics of high frequency noise, low frequency trend item and calibrated signal of tunnel blasting are significantly different. The trajectory of the calibrated signal attractor is characterized by a fractal spectrum with persistent and anti- persistent periodic fluctuation and its recursion graph has periodic pattern; The attractors of the low-frequency trend item have the shape of approximate straight line and the persistent fractal spectrum characteristic, and the recursion graph has the mutation pattern of diagonal distribution; The attractor of high frequency noise is characterized by random fluctuation and anti-persistent fractal spectrum, and its recursion graph has drift pattern. In the range of wavelet influence cone with confidence of 95%, the calibrated signal, trend item and noise have the characteristics of persistent positive correlation, local negative correlation, and no correlation with the original signal, respectively. The effective separation of three components and the extraction of chaotic fractal features provide objective characterization and quantization indexes for the identification and classification of blasting signal components.
付晓强1,2,3，俞缙2，刘纪峰1，杨仁树4，戴良玉3. 隧道爆破振动信号畸变校正与混沌多重分形特征研究[J]. 振动与冲击, 2022, 41(6): 76-85.
FU Xiaoqiang1,2,3，YU Jin2，LIU Jifeng1，YANG Renshu4，DAI Liangyu3. Distortion correction and fractal characteristics of vibration signals of a tunnel blasting. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(6): 76-85.
[1]王松青,张全峰,汪海波,等.武汉地铁区间隧道下穿建筑物爆破振动控制技术研究[J].工程爆破,2020,26(01):85-90.
WANG Songqing,ZHANG Quanfeng,WANG Haibo,et al. Research on blasting construction technology in subway tunnel beneath buildings in Wuhan[J].
Engineering Blasting,2020,26(01):85-90.
[2]杨仁树,付晓强,张世平,等.基于EEMD分形与二次型SPWV分布的爆破振动信号分析[J].振动与冲击,2016,35(22):41-47.
YANG Renshu,FU Xiaoqiang,et al.Analysis of blasting vibration signal based on EEMD fractal and quadratic time-frequency SPWV distribution[J].Journal of Vibration and Shock, 2016, 35(22):41-47.
[3]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,2020,40(08):1-15.
WANG Hailong,ZHAO Yan,WANG Haijun,et al.De-noising method of tunnel blasting signal based on CEEMDAN decomposition-wavelet packet analysis[J].Explosion and Shock Waves,2020,40(08):1-15.
[4]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(04):123-131.
JIA Bei,LING Tianlong,HOU Shijun,et al.Application of variable mode decomposition in the removal of blasting signal trend items[J].Explosion and Shock Waves,2020,40(04):123-131.
[5]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.
Zhang Sheng,Ling Tonghua,Cao Feng,et al. Application of removal trend method of pattern adapted continuous wavelet to blast vibration signal analysis[J].Explosion
and Shock Waves, 2017, 37(2):255-261.
[6] Liu J C, Gao W X. Vibration Signal Analysis of Water Seal Blasting Based on Wavelet Threshold Denoising and HHT Transformation[J]. Advances in Civil Engineering, 2020, 2020(1):1-14.
[7] Zhao M S , Wang X G , Chi E A , et al. Influence of Distance from Blast Center on Time-Frequency Characteristics of Blast Vibration Signals[J]. Advanced Materials Research, 2014, 1033-1034:444-448.
[8]赵明生,张建华,易长平.基于单段波形叠加的爆破振动信号时频分析[J].煤炭学报,2010,35(08):1279-1282.
ZHAO Mingsheng,ZHANG Jianhua,YI Changping,et al.Time-frequency analysis based on single-stage addition of waveforms of blasting vibration signals[J]. Journal of China Coal Society,2010,35(08):1279-1282.
[9]单仁亮,宋永威,白瑶,等.基于小波包变换的爆破信号能量衰减特征研究[J].矿业科学学报,2018,3(02):119-128.
Shan Renliang,Song Yongwei,Bai Yao,et al.Research on the energy attenuation characteristics of blasting vibration signals based on wavelet packet transformation[J]. Journal of Mining Science and Technology,2018,3(02):119-128.
[10]单仁亮,白瑶,宋永威,等.冻结立井模型爆破振动信号的小波包分析[J].煤炭学报,2016,41(08):1923-1932.
SHAN Ren-liang,BAI Yao,SONG Yong-wei,et al.Wavelet packet analysis of blast vibration signals of freezing shaft model[J]. Journal of China Coal Society,2016,41(08):1923-1932.
[11]钟明寿,谢全民,龙源,等.碳酸盐岩中爆破振动信号能量局部特征的多重分形分析[J].振动与冲击,2016,35(13):94-98.
ZHONG Min-shou,XIE Quan-min,LONG Yuan,et al.Multifractal analysis for local characteristics of blasting vibration signal energy in carbonate rocks[J]. Journal of Vibration and Shock,2016,35(13):94-98.
[12]付晓强,杨仁树,崔秀琴,等.冻结立井爆破振动信号多重分形去趋势波动分析[J].振动与冲击,2020,39(06):51-58.
FU Xiaoqiang,YANG Renshu,CUI Xiuqin,et al.Multi-fractal detrended fluctuation analysis of the blasting vibration signal in a frozen shaft[J].Journal of Vibration and Shock,2020,39(06):51-58.
[13]Grinsted A , Moore J C , Jevrejeva S . Application of the cross wavelet transform and wavelet coherence to geophysical time series[J].Nonlinear Processes in Geophysics,2004, 11(5/6):561-566.
[14]关山,庞弘阳,宋伟杰,等.基于MF-DFA特征和LS-SVM算法的刀具磨损状态识别[J].农业工程学报,2018,34(14):61-68.
Guan Shan,Pang Hongyang,Song Weijie,et al.Cutting tool wear recognition based on MF-DFA feature and LS-SVM algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering,2018,34(14):61-68.
[15]田再克,李洪儒,孙健,等.基于改进MF-DFA的液压泵退化特征提取方法[J].振动.测试与诊断,2017,37(01):140-146+205.
TIAN zaike,LI Hongru,SUN Jian,et al.Degradation on Feature Extraction of Hydraulic Pump Based on Improved MF-DFA[J]. Journal of Vibration,Measure-
ment&Diagnosis[J]. 2017,37(01):140-146+205.
[16]李精明,魏海军,魏立队,等.摩擦振动信号的EEMD和多重分形去趋势波动分析[J].哈尔滨工程大学学报,2016,37(09):1204-1208+1214.
LI Jingming,WEI Haijun,WEI Lidui,et al.Ensemble empirical mode decomposition and multifractal detrended fluctuation analysis of frictional vibration signals[J]. Journal of Harbin Engineering University,2016,37(09):1204-1208+1214.
[17]李精明,魏海军,魏立队,等.摩擦振动信号的经验模式分解和多重分形研究[J].振动与冲击,2016,35(03):198-203.
LI Jingming,WEI Haijun,WEI Lidui,et al.Empirical mode decomposition and multifractal of frictional vibration signal[J]. Journal of Vibration and Shock,2016,
35(03):198-203.
[18]付晓强,雷振,崔秀琴,等.立井爆破振动信号混沌特征研究[J].煤矿安全,2019,50(11):63-66+71.
FU Xiaoqiang,LEI Zhen,CUI Xiuqin,et al.Study on Chaotic Characteristics of Blasting Vibration Signal in Vertical Shaft[J]. Safety in Coal Mines,2019,50(11):
63-66+71.
Lin J, Chen Q. Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion[J]. Mechanical Systems & Signal Processing, 2013, 38(2):515-533.
[19]孙迪,李国宾,魏海军,等.磨合过程摩擦振动混沌吸引子演变规律[J].振动与冲击,2015,34(06):116-121.
SUN Di,LI Guo-bin,WEI Hai-jun,et al.Evolvement rule of frictional vibration chaos attractors in running-in process[J]. Journal of Vibration and Shock,2015,34(06):116-121.
[20]张二华,单德山,李乔.基于多尺度递归图理论的桥梁微弱信号非线性非平稳检验[J].振动与冲击,2019,38(16):123-128.
ZHANG Erhua,SHAN Deshan,LI Qiao.Nonlinear and nonstationary detection of weak bridge signals based on the multiscale recurrence plot theory[J]. Journal of Vibration and Shock,2019,38(16):123-128.
[21]李晋,汤井田,蔡剑华,等.利用多尺度形态学和递归图分离辨识大地电磁微弱信号[J].中南大学学报(自然科学版),2016,47(11):3890-3898.
LI Jin,TANG Jingtian,CAI Jianhua,et al.Separation and identification of magnetotelluric weak data using multi-scale morphology and recurrence plot[J]. Journal of Central South University(Science and Technology),2016,47(11):3890
-3898.
[22]He W, He Y, Li B, et al. Feature extraction of analogue circuit fault signals via cross-wavelet transform and variational Bayesian matrix factorisation[J]. Science, Measurement & Technology, 2019, 13(2):318-327.
