A study on parameter identification of optimal noise reduction smooth model for vortex-induced vibration response of riser based on EEMD

PDF(1699 KB)

Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (12) : 254-260.

LIU Zhihui1，2, XU Xingping2, NIU Huailei3, WANG Teng1

Author information +

History +

Abstract

In order to obtain the real characteristics of vortex-induced vibration, the overall ensemble empirical mode decomposition (EEMD) of the signals is carried out. A filtering algorithm is established using the decomposed intrinsic mode function. Considering the smoothness of the curve and the similarity between the reconstructed signal and the measured signal, the optimal noise reduction smooth model interval is established. Determining the effective intrinsic mode function according to the degree of existence. The synthetic signal is simulated and identified from the perspectives of correlation degree, spectrum analysis and energy spectrum analysis, and the accuracy of the algorithm is verified. The accuracy of the algorithm is verified from three angles: correlation degree, spectrum analysis and energy spectrum analysis. The results show that the optimized IMFs contains the information of vortex-induced vibration, and the main frequency in the downstream direction is twice as much as that in the transverse direction.

Key words

vortex-induced vibration / ensemble empirical mode decomposition (EEMD) / smoothness / similarity; / the degree of existence / effective intrinsic mode function

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

LIU Zhihui1，2, XU Xingping2, NIU Huailei3, WANG Teng1. A study on parameter identification of optimal noise reduction smooth model for vortex-induced vibration response of riser based on EEMD[J]. Journal of Vibration and Shock, 2022, 41(12): 254-260

References

[1]HUANG Norden E.，SHEN Zheng，LONG Steven 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..
[2]戴邵武，陈强强，丁宇. 基于改进EMD的排气温度裕度预测[J]. 兵器装备工程学报， 2020, 41(01):157-162.
DAI Shaowu, CHEN Qiangqiang, DING Yu. Prediction of Exhaust Gas Temperature Margin Based on Improved EMD [J]. Journal of Ordnance Equipment Engineering, 2020,41(01),157-162.
[3]党建，李骥，贾嵘，等. 基于EMD连续几何分布的水电机组振动信号降噪[J]. 水力发电学报，2020，39(04):46-54.
DANG Jian, LI Ji, JIA Rong, et al. Denoising vibration signals from hydroelectric generating units using EMD-based consecutive geometric distribution similarity measure algorithm[J]. Journal of Hydroelectric Engineering, 2020,39(04):46-54.
[4]池永为，杨世锡，焦卫东，等. 基于EMD-DCS的滚动轴承伪故障特征识别方法[J].振动与冲击，2020，39(09):9-16.
CHI Yongwei, YANG Shixi, JIAO Weidong, et al. EMD-DCS based pseudo-fault feature identification method for rolling bearings[J]. Journal of Vibration and Shock, 2020,39(09):9-16.
[5]马明明，张小凤，贺生平，等. 基于EMD分解和广义相位排列熵的相近金属材料分类[J]. 陕西师范大学学报自然科学版，2020, 48 (3):112-116.
MA Mingming, ZHANG Xiaofeng, HE Shengping, et al. Classification of metal materials with similar properties based on EMD decomposition and generalized phase permutation entropy[J]. Journal of Shaanxi Normal University(Natural Science Edition),2020, 48 (3):112-116.
[6]WU Zhaohua, HUANG Norden E.. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009,1(1):1-41.
[7]李夕兵，张义平，刘志祥，等．爆破震动信号的小波分析与 HHT变换[J]．爆炸与冲击，2005，25(6):528-535.
LI Xi-bing, ZHANG Yi-ping, LIU Zhi-xiang, et al. Wavelet analysis and Hilbert Huang transform of blasting vibration signal[J]. Explosion and Shock Waves, 2005, 25(6):528-535.
[8]关晓磊，颜景龙. 爆破振动信号的HHT时频能量谱分析[J]．爆炸与冲击，2012,32(5)：535-541．
GUAN Xiao-lei, YAN Jing-long. The HHT time-frequency power spectrum analysis of the blasting vibration signal[J]．Explosion and Shock Waves, 2012,32(5)：535-541．
[9]孙苗，吴立，袁青，等. 基于CEEMDAN的爆破地震波信号时频分析[J].华南理工大学学报（自然科学版），2020,48(3):76-82.
SUN Miao, WU Li, YUAN Qing. Time-Frequency Analysis of Blasting Seismic Signal Based on CEEMDAN[J]. Journal of South China University of Technology(Natural Science Edition), 2020,48(3):76-82.
[10]LI Zhixing, SHI Boqiang. Research of Fault Diagnosis Based on Sensitive Intrinsic Mode Function Selection of EEMD and Adaptive Stochastic Resonance[J]. Shock and Vibration, 2016, 2016:2841249.
[11]LEI Yaguo, ZUO Ming J.. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs[J]. Measurement Science and Technology, 2009, 20: 125701.
[12]YI Cai, WANG Dong, FAN Wei, et al. EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings[J]. Sensors (Basel) , 2018, 18(3):704.
[13]YI Cai, LIN Jianhui, ZHANG Weihua, et al. Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD[J]. Sensors (Basel) , 2015, 15(5): 10991–11011.
[14]PROSVIRIN Alexander E., ISLAM Manjurul, KIM Jaeyoung, et al. Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models[J]. Sensors (Basel), 2018,18(7):2040.
[15]徐方慧，王祝文，刘菁华，等. 联合EMD及小波阈值去噪在电成像测井数据中的应用[J].中国石油大学学报（自然科学版），2020，44(3):56-65.
XU Fanghui，WANG Zhuwen，LIU Jinghua，et al. Application of de-noising method on electrical imaging logging data based on joint EMD and wavelet threshold[J].Journal of China University of Petroleum(Edition of Natural Science)，2020，44(3):56-65.
[16]孙苗，吴立，周玉纯，等.水下钻孔爆破地震波信号的最优降噪光滑模型[J].华南理工大学学报（自然科学版），2019,47(8):31-37.
SUN Miao, WU Li, ZHOU Yuchun, et al. Optimal Denoising Smooth Model of Underwater Drilling Blasting Seismic Wave Signal[J]. Journal of South China University of Technology (Natural Science Edition), 2019,47(8):31-37.
[17]ZHENG Yi, YUE Jun, SUN Xiaofeng, et al. Studies of filtering effect on internal solitary wave flow field data in the South China Sea using EMD[J].Advanced Materials Research, 2012,1793(1041):1422-1425.
[18]张亮，张佳丽，张学峰，等. 基于希尔伯特-黄变换的潮汐分析方法研究[J]. 天津大学学报(自然科学与工程技术版)，2020, 53(7):725-735.
ZHANG Liang, ZHANG Jiali, ZHANG Xuefeng, et al. Study of the Analysis of Tides Based on the HHT Method [J].Journal of Tianjin University(Science and Technology), 2020, 53(7):725-735.
[19]王海青, 郭海燕, 刘晓春, 等. 海洋立管涡激振动抑振方法试验研究[J]. 中国海洋大学学报(自然科学版), 2009(s1):479-482.
WANG Hai-Qing,GUO Hai-Yan,LIU Xiao-Chun, et al. Experimental Investigation of Vortex-Induced Vibration Suppression of Marine Riser[J]. Periodical of Ocean University of China, 2009(s1):479-482.
[20]唐国强. 立管涡激振动数值模拟方法及物理模型实验[D]. 大连：大连理工大学，2011.
TANG Guoqiang. A Study on Numerical and Experimental Investigation into Vortex-Induced Vibration of Marine Risers[D]. Dalian：Dalian University of Technology.2011.