Trend removal method of blasting vibration signal based on SSA-VMD

PDF(4215 KB)

Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (11) : 304-312.

MO Hongyi1,2, XU Zhenyang1,2, LIU Xin1,2, ZHANG Jiuyang1,2, JIANG Xiahang1,2

Author information +

History +

Abstract

Aiming at the interference of trend term in blasting vibration signal, an improved VMD trend term removal method is proposed. This method uses SSA to optimize VMD parameters, and then decompose the signal to a set of IMF. The trend component is screened out by the method of mean ratio, Then the residual component is reconstructed to get the signal to remove the trend term. After simulation signal analysis, compared with EEMD, SSA-VMD reduces the root mean square error, relative norm and maximum error by about 73%, 49% and 82%, respectively. SSA-VMD extracts trend items more fully and identifies trend items more accurately. At the same time, the measured blasting vibration signals are analyzed by SSA-VMD method. The results show that: the method eliminates the phenomenon of zero drift of blasting vibration signals, and the signal waveform returns to the center of baseline, main frequency tends to be reasonable, improving the accuracy of signal spectrum analysis.

Key words

blasting vibration / SSA / VMD / trend term

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

MO Hongyi1,2, XU Zhenyang1,2, LIU Xin1,2, ZHANG Jiuyang1,2, JIANG Xiahang1,2. Trend removal method of blasting vibration signal based on SSA-VMD[J]. Journal of Vibration and Shock, 2023, 42(11): 304-312

References

[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(05): 1006-1012.
HAN L, LIU B S, XIN C W, LIANG S F, et al. Trend removing methods of vibration signals of deep hole bench blasting in near field [J]. Explosion and Shock Waves, 2018, 38(05): 1006-1012.
[2] 付晓强，杨仁树，刘纪峰，等. 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[J]. 爆炸与冲击, 2020, 40(09): 100-112.
FU X, YANG R S, LIU J F, et al. Baseline drift correction and de-noising method of shaft lining vibration signal in near field of freezing vertical shaft blasting [J]. Explosion and Shock Waves, 2020, 40(09): 100-112.
[3] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings Mathematical Physical & Engineering Sciences, 1998, 454(1971): 903-995.
[4] 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.
ZAHNG S, LING T H, CAO F, 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.
[5] 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.
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.
[6] 李晨，梁书锋，刘传鹏，等. 基于改进的集合经验模态分解的爆破振动信号趋势项消除方法[J]. 北京理工大学学报, 2021, 41(06): 636-641.
LI C, LIANG S F, LIU C P, et al. Trend Removing Method of Blasting Vibration Signals Based on MEEMD [J]. Journal of Beijing Institute of Technology, 2021, 41(06): 636-641.
[7] 陈亮，刘宏立，郑倩，等. 基于EEMD-SVD-PE的轨道波磨趋势项提取[J]. 哈尔滨工业大学学报, 2019, 51(5): 177-183.
CHEN L, LIU H L, ZHENG Q, et al. An EEMD-SVD-PE approach to extract the trend of track irregularity [J]. Journal of Harbin Institute of Technology, 2019, 51(5): 177-183.
[8] 胡剑超，练继建，马斌，等. 基于CEEMD和小波包阈值的组合降噪及泄流结构的模态识别方法[J]. 振动与冲击, 2017, 36(17): 9-17.
HU J C, LIAN J J, MA B, et al. A de-noising and modal identification combined method based on CEEMD and wavelet packet threshold for flood discharge structures [J]. Journal of Vibration and Shock, 2017, 36(17): 9-17.
[9] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition [J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544．
[10] 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. The application of variational mode decomposition in blasting signal trend term removal [J]. Explosion and impact, 2020, 40(4): 123-131.
[11] 谷然，陈捷，洪荣晶，等. 基于改进自适应变分模态分解的滚动轴承微弱故障诊断[J]. 振动与冲击, 2020, 39(08): 1-7+22.
GU R, CHEN J, HONG R J, et al. Weak fault diagnosis of rolling bearings based on improved adaptive variational mode decomposition [J]. Vibration and impact, 2020, 39(08): 1-7+22.
[12] 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 Y X, LIU G J, SU Y, et al. Smooth noise reduction model of tunnel blasting vibration signal based on variational mode decomposition algorithm [J]. Vibration and shock, 2021, 40(24): 173-179.
[13] 梁尔祝，徐淼，谷传宝，莫宏毅，徐振洋. 基于SA-GA模糊熵的VMD算法在爆破振动信号分解中的应用[J]. 金属矿山, 2022(02): 75-82.
LIANG E Z, XU M, GU C B, MO H Y, XU Z Y. The application of VMD algorithm based on SA-GA fuzzy entropy in blasting vibration signal decomposition [J]. Metal mine, 2022(02): 75-82.
[14] 薛建凯. 一种新型的群智能优化技术的研究与应用[D]. 东华大学, 2020.
XUE J K. Research and application of a new swarm intelligence optimization technology [D]. Donghua University, 2020.
[15] CHEN G, Li Q Y, Li D Q, et al. Main frequency band of blast vibration signal based on wavelet packet transform [J]. Applied Mathematical Modelling, 2019, 74: 569-585.
[16] HUANG D, GUI S, LI X Q. Wavelet packet analysis of blasting vibration signal of mountain tunnel [J]. SoilDynamics and Earthquake Enginnering, 2019, 117: 72-80.
[17] SONG X L, GAO W X, Li J Y, et al. Analysis of Fractal Features of Blasting Vibration Signal Based on EEMD Decomposition[J]. IOP Conference Series: Earth and Environmental Science, 2021, 719(3).