冻结立井爆破近区井壁振动信号基线漂移校正和消噪方法

付晓强; 杨仁树; 刘纪峰; 张会芝; 张仁巍

doi:10.11883/bzycj-2019-0367

冻结立井爆破近区井壁振动信号基线漂移校正和消噪方法

doi: 10.11883/bzycj-2019-0367

付晓强^{1, 2,},
杨仁树³,
刘纪峰^{1, 2},
张会芝^{1, 2},
张仁巍^{1, 2}

1.
三明学院建筑工程学院，福建三明 365004
2.
三明学院工程材料与结构加固福建省高等学校重点实验室，福建三明 365004
3.
北京科技大学土木与资源工程学院，北京 100083

基金项目: 三明学院引进高层次人才科研启动经费（18YG13）

详细信息

作者简介:
付晓强（1984－　），男，博士，讲师，fuxiaoqiang1984@163.com

中图分类号: O389; TD235.1
计量
- 文章访问数: 3449
- HTML全文浏览量: 1258
- PDF下载量: 57
- 被引次数: 0
出版历程
- 收稿日期: 2019-09-23
- 修回日期: 2020-02-11
- 网络出版日期: 2020-08-25
- 刊出日期: 2020-09-01

Baseline drift correction and de-noising method of shaft lining vibration signal in near field of freezing vertical shaft blasting

FU Xiaoqiang^{1, 2
,},
YANG Renshu³,
LIU Jifeng^{1, 2},
ZHANG Huizhi^{1, 2},
ZHANG Renwei^{1, 2}

1.
School of Civil Engineering, Sanming University, Sanming 365004, Fujian, China
2.
Key Laboratory of Engineering Material & Structure Reinforcement in Fujian Province College, Sanming University, Sanming 365004, Fujian, China
3.
School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China

摘要

摘要: 冻结立井爆破过程中，近区监测信号中含有的基线漂零及噪声成分对其局部特征精细化提取影响显著。在对近区井壁振动信号有效采集基础上，通过互补总体经验模态分解（complementary ensemble empirical mode decomposition, CEEMD）方法、稀疏化基线估计消噪（baseline estimation and de-noising with sparsity, BEADS）方法和隐马尔可夫模型消噪（hidden Markov model de-noising, HMMD）方法等，解决了信号中基线漂移和随机噪声消除难题，并采用交叉小波变换对校正和消噪效果进行了相关性评价。实例分析结果表明：信号中缓变的基线成分遍历信号各个模态分量的整个过程，且主要集中于低频分量中，而噪声则集中在高频分量。组合分析方法对低频基线漂零和高频噪声的处理效果好，是一种高效且相对保幅的信号分析方法，可用于批量信号数据的预处理过程。
- 冻结立井 /
- 爆破振动 /
- 基线偏移 /
- 消噪分析 /
- 交叉小波变换 /
- 相关性分析
Abstract: In the process of freezing vertical shaft blasting, the baseline drift and noise in the near area monitoring signal have significant influence on the fine extraction of local characteristics. On the basis of effective acquisition of shaft lining vibration signals in near field of blasting, complementary ensemble empirical mode decomposition (CEEMD) method, baseline estimation and de-noising with sparsity (BEADS) method and hidden Markov model de-noising (HMMD) method and so on are used to solve the problem of baseline drift and random noise elimination in the signal, and the correlation evaluation of correction and noise elimination effect is carried out by cross wavelet transform (CWT). The analysis results show that: the slowly changing baseline component in the signal exists the whole process of each modal component, and it is mainly concentrated in the low frequency component, while the noise is concentrated in the high frequency component. The combined analysis method can deal with low frequency baseline drift and high frequency noise effectively. It is an efficient and relatively amplitude-preserving signal analysis method, and can be used to preprocess of batch blasting vibration signal data.
- freezing vertical shaft /
- blasting vibration /
- baseline drift /
- de-noising analysis /
- cross wavelet transform /
- correlation analysis

HTML全文

图 1 基线校正及消噪流程

Figure 1. Baseline correction and noise reduction process

下载: 全尺寸图片幻灯片

图 2 传感器井壁预埋法

Figure 2. Pre-embedding method of vibration instrument

下载: 全尺寸图片幻灯片

图 3 炮眼布置（单位：mm）

Figure 3. Borehole layout (unit: mm)

下载: 全尺寸图片幻灯片

图 4 爆破近区井壁振动信号

Figure 4. Vibration signal of shaft lining near blasting area

下载: 全尺寸图片幻灯片

图 5 各IMF分量筛分迭代次数关系

Figure 5. The relationship of iterations number and shift number

下载: 全尺寸图片幻灯片

图 6 信号CEEMD分解各分量及残余项R

Figure 6. Component of CEEMD decomposition and residual signal

下载: 全尺寸图片幻灯片

图 7 模态分量处理结果

Figure 7. Results of baseline correction processing

下载: 全尺寸图片幻灯片

图 8 各分量基线成分

Figure 8. Baseline of each component

下载: 全尺寸图片幻灯片

图 9 罚函数值与迭代次数关系

Figure 9. Relation of cost function and the iteration number

下载: 全尺寸图片幻灯片

图 10 基线校正后信号

Figure 10. Baseline corrected signal

下载: 全尺寸图片幻灯片

图 11 CEEMD重构信号

Figure 11. Reconstructed signals of CEEMD

下载: 全尺寸图片幻灯片

图 12 消噪后的校正信号

Figure 12. Baseline corrected signal after de-noising

下载: 全尺寸图片幻灯片

图 13 信号功率谱密度（w_f）

Figure 13. Power spectral density (w_f) of signal

下载: 全尺寸图片幻灯片

图 14 信号相关性凝聚谱

Figure 14. Correlation condensation spectra of signals

下载: 全尺寸图片幻灯片

表 1 模态分量与原信号相关度

Table 1. Correlation between components and original signals

IMF 相关性系数 IMF 相关性系数 IMF 相关性系数

1 0.4202 5 0.5023 9 0.1083
2 0.4484 6 0.1419 10 0.3707
3 0.3754 7 0.1333 11 0.4540
4 0.4124 8 0.1364 12 0.2940

下载: 导出CSV

表 2 不同方法消噪性能指标

Table 2. Indexes of noise reduction performance

去噪方法评价指标
SNR RMSE CC PE

MD 3.442 0.581 0.816 1.758
SVD 20.983 0.379 0.896 1.175
WED 21.397 0.374 0.921 0.492
HMMD 43.371 0.056 0.992 0.244

下载: 导出CSV

参考文献(22)

[1]	杨仁树, 付晓强, 杨立云, 等. 冻结立井爆破冻结壁成形控制与井壁减振研究 [J]. 煤炭学报, 2016, 41(12): 2975–2985. DOI: 10.13225/j.cnki.jccs.2016.1487. YANG R S, FU X Q, YANG L Y, et al. Research on the shaping control of frozen wall and blasting vibrationmitigation of shaft wall effect in mine vertical shaft [J]. Journal of China Coal Society, 2016, 41(12): 2975–2985. DOI: 10.13225/j.cnki.jccs.2016.1487.
[2]	程桦, 林键, 姚直书, 等. 我国西部地区孔隙型含水基岩段立井单层井壁外荷载研究 [J]. 岩石力学与工程学报, 2019, 38(3): 542–550. DOI: 10.13722/j.cnki.jrme.2018.1155. CHENG H, LIN J, YAO Z S, et al. Study on the external load of a single layer wall of the vertical shaft in the pore type water-bearing bedrock section in Western China [J]. Chinese Journal of Rock Mechanics and Engineering, 2019, 38(3): 542–550. DOI: 10.13722/j.cnki.jrme.2018.1155.
[3]	马芹永, 袁璞, 张经双, 等. 立井直眼微差爆破模型试验振动测试与分析 [J]. 振动与冲击, 2015, 34(6): 172–176. DOI: 10.13465/j.cnki.jvs.2015.06.033. MA Q Y, YUAN P, ZHANG J S, et al. Blasting vibration measurement and analyses of millisecondblasting models for vertical shaft blasting [J]. Journal of Vibration and Shock, 2015, 34(6): 172–176. DOI: 10.13465/j.cnki.jvs.2015.06.033.
[4]	张军, 潘泽鑫, 郑玉新, 等. 振动信号趋势项提取方法研究 [J]. 电子学报, 2017, 45(1): 22–28. DOI: 10.3969/j.issn.0372-2112.2017.01.004. ZHANG J, PAN Z X, ZHENG Y X, et al. Research on vibration signal trend extraction [J]. Acta Electronica Sinica, 2017, 45(1): 22–28. DOI: 10.3969/j.issn.0372-2112.2017.01.004.
[5]	张景元, 何玉珠. 基于形态学的自动驾驶仪振动信号基线漂移去噪 [J]. 北京航空航天大学学报, 2018, 44(5): 907–913. DOI: 10.13700/j.bh.1001-5965.2017.0371. ZHANG J Y, HE Y Z. Removing baseline drift in vibration signal of autopilot based on morphology [J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 907–913. DOI: 10.13700/j.bh.1001-5965.2017.0371.
[6]	刘艳丽, 赵为松, 李海坤, 等. 基于形态滤波的脉搏波信号基线漂移消除方法研究 [J]. 合肥工业大学学报(自然科学版), 2011, 34(4): 525–528. DOI: 10.3969/j.issn.1003-5060.2011.04.011. LIU Y L, ZHAO W S, LI H K, et al. Research on removing baseline wandering of pulse wave signal based on morphologicalfilter [J]. Journal of Hefei University of Technology, 2011, 34(4): 525–528. DOI: 10.3969/j.issn.1003-5060.2011.04.011.
[7]	谢芳娟, 朱淑云. 基于空域追踪算法的基线漂移信号噪声修正 [J]. 沈阳工业大学学报, 2016, 38(6): 692–696. DOI: 10.7688/j.issn.1000-1646.2016.06.17. XIE F J, ZHU S Y. Baseline drift noise correction based on null space pursuit algorithm [J]. Journal of Shenyang University of Technology, 2016, 38(6): 692–696. DOI: 10.7688/j.issn.1000-1646.2016.06.17.
[8]	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 pattern adapted continuouswavelet to blast vibration signal analysis [J]. Explosion and Shock Waves, 2017, 37(2): 255–261. DOI: 10.11883/1001-1455(2017)02-0255-07.
[9]	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.
[10]	王志亮, 陈贵豪, 黄佑鹏. EEMD修正爆破加速度零漂信号中的最优白噪声系数 [J]. 爆炸与冲击, 2019, 39(8): 084201. DOI: 10.11883/bzycj-2019-0154. WANG Z L, CHEN G H, HUANG Y P. Optimal white noise coefficient in EEMD corrected zero drift signal of blastingacceleration [J]. Explosion and Shock Waves, 2019, 39(8): 084201. DOI: 10.11883/bzycj-2019-0154.
[11]	YEH J R, SHIEH J S, HUANG N E. Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method [J]. Advances in Adaptive Data Analysis, 2010, 2(2): 135–156. DOI: 10.1142/S1793536910000422.
[12]	乐友喜, 杨涛, 曾贤德. CEEMD与KSVD字典训练相结合的去噪方法 [J]. 石油地球物理勘探, 2019, 54(4): 729–736, 721. DOI: 10.13810/j.cnki.issn.1000-7210.2019.04.001. YUE Y X, YANG T, ZENG X D. Seismic denoising with CEEMD and KSVD dictionary combined training [J]. Oil Geophysical Prospecting, 2019, 54(4): 729–736, 721. DOI: 10.13810/j.cnki.issn.1000-7210.2019.04.001.
[13]	杨仁树, 付晓强, 杨国梁, 等. 基于CEEMD与TQWT组合方法的爆破振动信号精细化特征提取 [J]. 振动与冲击, 2017, 36(3): 38–45. YANG R S, FU X Q, YANG G L, et al. Precise feature extraction of blasting vibration signals based on combined method ofCEEMD and TQWT [J]. Journal of Vibration and Shock, 2017, 36(3): 38–45.
[14]	NING X R, SELESNICK I W, DUVAL L. Chromatogram baseline estimation and denoising using sparsity (BEADS) [J]. Chemometrics and Intelligent Laboratory Systems, 2014, 139: 156–167. DOI: 10.1016/j.chemolab.2014.09.014.
[15]	张进, 冯志鹏, 卢文秀, 等. 交叉小波变换在水轮机非平稳信号分析中的应用 [J]. 中国电机工程学报, 2010, 30(23): 84–89. DOI: 10.13334/j.0258-8013.pcsee.2010.23.017. ZHANG J, FENG Z P, LU W X, et al. Application of cross-wavelet transform to hydraulic turbine nonstationary signal analysis [J]. Proceeding of the CSEE, 2010, 30(23): 84–89. DOI: 10.13334/j.0258-8013.pcsee.2010.23.017.
[16]	程知, 何枫, 靖旭, 等. 基于集合经验模态分解和奇异值分解的激光雷达信号去噪 [J]. 光子学报, 2017, 46(12): 1201003. DOI: 10.3788/gzxb20174612.1201003. CHENG Z, HE F, JING X, et al. Denoising lidar signal based on ensemble empirical mode decomposition and singular value decomposition [J]. Acta Photonica Sinica, 2017, 46(12): 1201003. DOI: 10.3788/gzxb20174612.1201003.
[17]	ROSSO O A, BLANCO S, YORDANOVA J, et al. Wavelet entropy: a new tool for analysis of short duration brain electricalsignals [J]. Journal of Neuroscience Methods, 2001, 105(1): 65–75. DOI: 10.1016/s0165-0270(00)00356-3.
[18]	王东, 王新晴, 梁升, 等. 基于数学形态学的流量传感器信号去噪研究 [J]. 传感技术学报, 2012, 25(8): 1097–1101. DOI: 10.3969/j.issn.1004-1699.2012.08.016. WANG D, WANG X Q, LIANG S, et al. Study on signal denoising of flow sensor based on mathematical morphology [J]. ChineseJournal of Sensors and Actuators, 2012, 25(8): 1097–1101. DOI: 10.3969/j.issn.1004-1699.2012.08.016.
[19]	CROUSE M S, NOWAK R D, BARANIUK R G. Wavelet-based statistical signal processing using hidden Markov models [J]. IEEE Transactions on Signal Processing, 1998, 46(4): 886–902. DOI: 10.1109/78.668544.
[20]	张毅刚, 郁惟镛, 黄成军, 等. 基于小波包及隐式马尔科夫模型的局放信号去噪 [J]. 上海交通大学学报, 2004, 38(8): 1269–1272. DOI: 10.3321/j.issn:1006-2467.2004.08.010. ZHANG Y G, YU W Y, HUANG C J, et al. Wavelet package-based partial discharge denoising using hidden markov model [J]. Journal of Shanghai Jiaotong University, 2004, 38(8): 1269–1272. DOI: 10.3321/j.issn:1006-2467.2004.08.010.
[21]	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. DOI: 10.5194/npg-11-561-2004.
[22]	邵骏. 基于交叉小波变换的水文多尺度相关分析 [J]. 水力发电学报, 2013, 32(2): 22–26, 42. SHAO J. Multi-scale correlation analysis ofhydrological time series based on cross wavelet transform [J]. Journal of Hydroelectric Engineering, 2013, 32(2): 22–26, 42.