Time-Frequency Analysis of Noisy Vibration Signal Based on Improved Hilbert-Huang Transform

doi:10.16183/j.cnki.jsjtu.2022.255

Abstract

Abstract:

Aimed at the phenomenon of modal confusion and lack of practical significance of instantaneous frequency caused by Hilbert-Huang transform (HHT) noisy vibration signal time-frequency analysis, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is obtained by improving empirical mode decomposition (EMD), and in combination with multiscale permutation entropy (MPE) to suppress EMD modal confusion. At the same time, improved normalized Hilbert transform(INHT) is obtained by improving Hilbert transform, and finally the CEEMDAN·MPE-INHT time-frequency analysis algorithm is formed. In order to verify the accuracy of time-frequency analysis of CEEMDAN·MPE-INHT noisy vibration signals, a comparative study of time-frequency analysis of EMD-HT, EEMD-NHT, CEEMDAN-INHT and CEEMDAN·MPE-INHT noisy simulation vibration signals is conducted. The results show that CEEMDAN can control low frequency noise; MPE can suppress high frequency noise; INHT can make Hilbert transform not constrained by Bedrosian theorem. Finally, CEEMDAN·MPE-INHT algorithm is applied to the time-frequency analysis of noisy vibration signals in practical engineering. The time spectrum of intrinsic mode function (IMF) decomposed by CEEMDAN·MPE after INHT processing has a high resolution in time domain and frequency domain, which can improve the extraction accuracy of time-frequency characteristic parameters and help control the harm of vibration signals.

Key words: empirical mode decomposition, Hilbert transform, modal confusion, intrinsic mode function

CLC Number:

TU12
O382

SUN Miao, YANG Junkai, WU Li. Time-Frequency Analysis of Noisy Vibration Signal Based on Improved Hilbert-Huang Transform[J]. Journal of Shanghai Jiao Tong University, 2023, 57(12): 1648-1656.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Fig.4

Tab.1

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

References 21

[1]	LOH C H, HUANG N E, WU T C. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses[J]. Bulletin of the Seismological Society of America, 2001, 91(5): 1339-1357. doi: 10.1785/0120000715 URL
[2]	YUAN H P, LIU X L, LIU Y, et al. Analysis of acoustic wave frequency spectrum characters of rock mass under blasting damage based on the HHT method[J]. Advances in Civil Engineering, 2018, 2018(4): 1-8.
[3]	FAN Z N, LI P F, TAO Y Y, et al. Stochastic resonance in a special type of asymmetric nonlinear system driven by correlated noise[J]. Journal of Sichuan University (Natural Science Edition), 2020, 57(5): 927-935.
[4]	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(3): 903-995.
[5]	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(Sup.2): 112-118.
	CAO Xiaoli, GAO Wenxue, LV Hongtao, et al. Analysis of blasting vibration signal by HHT transform[J]. Acta Armamentarii, 2016(Sup.2): 112-118.
[6]	HUANG Y P, GEN J H, ZHONG G F, et al. Seismic attribute extraction based on HHT and its application in a marine carbonate area[J]. Applied Geophysics. 2011, 8(2): 125-133. doi: 10.1007/s11770-010-0279-z URL
[7]	梅比, 汪旭光, 杨仁树. 基于改进MP-WVD算法的核电厂建设爆破振动信号处理方法[J]. 爆炸与冲击, 2019, 39(4): 152-162.
	MEI Bi, WANG Xuguang, YANG Renshu. Blasting vibration signal analysis technology of construction of nuclear power plant based on improved MP-WVD algorithm[J]. Explosion and Shock Waves, 2019, 39(4): 152-162.
[8]	孙鹏昌, 卢文波, 雷振, 等. 单薄山体岩质高边坡爆破振动响应分析及安全控制[J]. 岩土工程学报, 2021, 43(5): 877-885.
	SUN Pengchang, LU Wenbo, LEI Zhen, et al. Blasting vibration response and control of high rock slopes of thin mountain[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(5): 877-885.
[9]	杨仁树, 付晓强, 杨国梁, 等. EMD和FSWT组合方法在爆破振动信号分析中的应用研究[J]. 振动与冲击, 2017, 36(2): 58-64.
	YANG Renshu, FU Xiaoqiang, YANG Guoliang, et al. Application of EMD and FSWT combination method in blasting vibration signal analysis[J]. Journal of Vibration and Shock, 2017, 36(2): 58-64.
[10]	程军圣, 王健, 桂林. 改进的EEMD方法及其在滚动轴承故障诊断中的应用[J]. 振动与冲击, 2018, 37(16): 51-56.
	CHENG Junsheng, WANG Jian, GUI Lin. An improved EEMD method and its application in rolling bearing fault diagnosis[J]. Journal of Vibration and Shock, 2018, 37(16): 51-56.
[11]	刘涛, 杜世昌, 黄德林, 等. 基于改进的集合经验模态方法振动信号分解[J]. 上海交通大学学报, 2016, 50(9): 1452-1459.
	LIU Tao, DU Shichang, HUANG Delin, et al. Vibration signal decomposition based on an improved ensemble empirical mode decomposition method[J]. Journal of Shanghai Jiao Tong University, 2016, 50(9): 1452-1459.
[12]	WU J, WU L, SUN M, et al. Analysis and research on blasting network delay of deep-buried diversion tunnel crossing fault zone based on EP-CEEMDAN-INHT[J]. Geotechnical and Geological Engineering, 2022, 40: 1363-1372. doi: 10.1007/s10706-021-01968-9
[13]	李清, 徐文龙, 张迪, 等. 爆破振动信号分析中模态混叠和虚假分量消除的改进方法[J]. 振动与冲击, 2019, 38(17): 197-204.
	LI Qing, XU Wenlong, ZHANG Di, et al. An improved method to eliminate modal aliasing and false component in blast vibration signals[J]. Journal of Vibration and Shock, 2019, 38(17): 197-204.
[14]	TORRES M, COLOMINAS M, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise[C]// IEEE International Conference on Acoustics. Prague, Czech Republic: IEEE, 2011: 4143-4147.
[15]	CHEN S J, SHANG P J, WU Y. Multivariate multiscale fractional order weighted permutation entropy of nonlinear time series[J]. Physica A: Statistical Mechanics and its Applications, 2019, 515: 217-231. doi: 10.1016/j.physa.2018.09.165 URL
[16]	冯云智, 唐彬峰, 赵宁. 改进多尺度排列熵及模糊算法的JTC状态检测[J]. 铁道科学与工程学报, 2021, 18(12): 3337-3346.
	FENG Yunzhi, TANG Binfeng, ZHAO Ning. JTC state detection based on improved multi-scale permutation entropy and fuzzy algorithm[J]. Journal of Railway Science and Engineering, 2021, 18(12): 3337-3346.
[17]	HUANG N E, WU Z, LONG S R. On instantaneous frequency[J]. Advances in Adaptive Data Analysis, 2009, 1(2): 177-229. doi: 10.1142/S1793536909000096 URL
[18]	WU Z H, HUANG N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41. doi: 10.1142/S1793536909000047 URL
[19]	SUN M, WU L, LI C J, et al. Smooth model of blasting seismic wave signal denoising based on two-stage denoising algorithm[J]. Geosystem Engineering, 2020, 23(4): 234-242. doi: 10.1080/12269328.2020.1778543 URL
[20]	李洪涛. 基于能量原理的爆破地震效应研究[D]. 武汉: 武汉大学, 2007.
	LI Hongtao. Study on effect of blast-induced seismic based on energy theory[D]. Wuhan: Wuhan University, 2007.
[21]	乔宪队. 爆破震动对邻近隧洞的动力响应分析[D]. 长沙: 中南大学, 2007.
	QIAO Xiandui. Dynamic response analysis of blasting vibration to adjacent tunnels[D]. Changsha: Central South University, 2007.

项目	扩建前楼山隧道	扩建后楼山隧道
断面形式	双向四车道	双向八车道
双洞最小距离/m	29~35	25~31
行车道宽度/m	2×3.75	2×3.5+2×3.75
侧向宽度/m	0.5	0.5
检修道宽/m	0.75	0.75
总宽度/m	10.25	17.25
隧道净空高/m	5	5