抑制模态混叠的HHT结构模态参数识别方法研究

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摘要针对传统经验模态分解法（EMD）的模态混叠问题，在分析模态混叠产生机理的基础上，提出基于互补总体平均经验模态分解（CEEMD）与信号调频（FM）结合的组合分解方法（FM-CEEMD），并通过仿真信号验证了FM-CEEMD分解方法的有效性。将FM-CEEMD分解取代传统EMD分解应用于希尔伯特-黄变换（HHT），得到抑制模态混叠的改进HHT结构模态参数识别方法。仿真试验与实际拱坝识别结果表明：提出的改进HHT法不仅能避免模态信息丢失、提高模态参数识别精度，同时也适用于实际水利工程模态识别研究。

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	练继建 1
	2
	荣钦彪 1
	2
	董霄峰 1
	2
	3
	王鸿振 1
	2
	刘卓 1
	2

关键词 ：改进HHT, 经验模态分解, 信号调频, 模态混叠, 参数识别

Abstract：Aiming at suppressing mode mixing in the traditional empirical mode decomposition (EMD) method, a combined method called as FM-CEEMD based on the complementary ensemble empirical mode decomposition (CEEMD) and signal frequency modulation (FM) methods was proposed considering the generating mechanism of mode mixing.The effectiveness of the combined decomposition method was verified by using simulation signals.The modified mode decomposition method was then applied to the Hilbert-Huang Transform (HHT), replacing the traditional EMD method, in order to obtain an improved HHT structural dynamic parameter identification method for suppressing mode mixing.The identified results both from the simulation experiment and an actual arch dam show the improved HHT method can not only avoid the loss of modal information to improve the parameter identification accuracy, but also be suitable to the researches on the structural modal identification of real hydraulic projects.

Key words： improved Hilbert-Huang Transform(HHT) empirical mode decomposition(EMD) signal frequency modulation(FM) mode mixing parameter identification

收稿日期: 2017-04-07 出版日期: 2018-09-15

引用本文:

练继建 1,2，荣钦彪 1,2，董霄峰 1,2,3，王鸿振 1,2，刘卓 1,2. 抑制模态混叠的HHT结构模态参数识别方法研究[J]. 振动与冲击, 2018, 37(18): 1-8.
Lian Ji-jian 1,2，Rong Qin-biao 1,2，Dong Xiao-feng 1,2,3，Wang Hong-zhen 1,2，Liu Zhuo 1,2. Structural model parameter identification method based on an improved HHT for suppressing mode mixing. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(18): 1-8.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I18/1

[1] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrun for nonlinear and non-stationary time Series analysis [J]. Proc Soc Lond A, 1998, 454: 903-995.
[2] Huang N E, Shen Z, Long S R. A new view of nonliner water waves: The Hilbert Spectrum [J]. Annual Review of Fluid Mechanics, 1999, 31: 417.
[3] HUANG N E, WU Z. A review on Hilbert-huang transform :method and its application to geophysical studies [J]. Reviews of Geophysics,2008,46(2).
[4] 练继建.水电站厂房结构研究[M].北京：中国水利水电出版社，2007.
LIAN Ji-jian, WANG Hai-jun, QIN Liang et al. Research on structure of hydropower plant [M]. Beijing: China Water Power Press, 2007.
[5] Dybala J, Zimroz R. Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal [J]. Applied Acoustics,2014,77:195-203.
[6] 孟宗,李姗姗.基于小波改进阈值去噪和HHT的滚动轴承故障诊断[J]. 振动与冲击,2013,(14):204-208+214.
MENG Zong, LI Shan-shan. Rolling bearing fault diagnosis based on improved wavelet threshold de-noising and HHT [J]. Journal of Vibration and Shock, 2013, (14):204-208+214.
[7] 龚敏,邱燚可可,孟祥栋,李永强,曾恭剑,唐胜. 基于HHT的雷管实际延时识别法在城市环境微差爆破中的应用[J]. 振动与冲击,2015,(10):206-212.
GONG Min, QIU Yi-keke, MENG Xiang-dong, et al. Identification method of detonator’s actual firing time delay based on HHT and its application in millisecond blasting under urban environment [J]. Journal of Vibration and Shock, 2015, (10):206-212.
[8] Ali J B, Fnaiech F, Saidi L, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals [J]. Applied Acoustics, 2015,89:16.
[9] 王婷. EMD算法研究及其在信号去噪中的应用[D]. 哈尔滨: 哈尔滨工程大学, 2010.
WANG Ting. Research on EMD algorithm and its application in signal de-noising [D]. Harbin: Harbin Engineering University, 2010.
[10] 孙伟峰, 彭玉华, 杨阳, 等. 经验模态分解频率分辨率的一种改进方法[J]. 计算机工程与应用, 2010, 46(1): 129-133.
SUN Wei-feng, PENG Yu-hua, YANG Yang, et al. Method for improving frequency resolution of empirical mode decomposition [J]. Computer Engineering and Applications, 2010, 46(1): 129-133.
[11] 肖瑛, 殷福亮. 解相关EMD: 消除模态混叠的新方法[J].振动与冲击, 2015, 34(4): 25-29.
XIAO Ying, YIN Fu-liang. Decorrelation EMD: A new method of eliminating mode mixing [J]. Journal of Vibration and Shock, 2015, 34(4): 25-29.
[12] 胡维平, 莫家玲, 杜明辉. 经验模态分解中的频域分辨率及其改进方法[J]. 华南理工大学学报(自然科学版), 2007, 35(5): 15-19.
HU Wei-ping, MO Jia-ling, DU Ming-hui. Frequency-domain resolution and its improvement during empirical mode decomposition [J]. Journal of South China University of Technology (Natural Science Edition), 2007, 35(5): 15-19.
[13] 黄天立, 邱发强, 楼梦麟. 基于改进HHT方法的密集模态结构参数识别[J]. 中南大学学报(自然科学版),2011,42(7): 2054-2062.
HUANG Tian-li, QIU Fa-qiang, LOU Meng-lin. Application of an improved HHT method for parameters identification of structures with closely spaced modes [J]. Journal of Central South University (Science and Technology), 2011, 42(7): 2054-2062.
[14] Wu Zhaohua, Huang N E.A Study of the characteristics of white noise using the empirical mode decomposition method[J]. Proc R Sco Lond A, 2004, 460:1597-1611.
[15] Wu Zhaohua, Huang N E. Ensemble empirical mode decomposition: a noise assisted data analysis method[J]. Advance in Adaptive Data Analysis, 2009, 1(1):1-41.
[16] 胡爱军,孙敬敬,向玲.经验模态分解中的模态混叠问题[J].振动.测试与诊断,2011,04:429-434+532-533.
HU Ai-jun, SUN Jing-jing, XING Ling. Mode mixing in empirical mode decomposition [J]. Journal of Vibration, Measurement & Diagnosis. 2011, 04:429-434+532-533.
[17] Yeh J R, Shieh J S, Huang N E. Complementary ensemble empirical mode decomposition :a novel noise enhanced data analysis method [J]. Advanced in Adaptive Data Analysis, 2010, 2(2):135-156.
[18] 周涛涛,朱显明,彭伟才,等. 基于CEEMD和排列熵的故障数据小波阈值降噪方法[J]. 振动与冲击,2015,(23):207-211.
ZHOU Tao-tao, ZHU Xian-ming, PENG Wei-cai, et al. A wavelet threshold denoising method for fault data based on CEEMD and permutation entropy [J]. Journal of Vibration and Shock, 2015,(23):207-211.
[19] 郑近德,程军圣,杨宇.改进的EEMD算法及其应用研究[J].振动与冲击,2013,(21):21-26+46.
ZHENG Jin-de, CHENG Jun-sheng, YANG Yu. Modified EEMD algorithm and its applications [J]. Journal of Vibration and Shock, 2013,(21):21-26+46.
[20] Rilling G, Flandrin P. One or two frequencies? The empirical mode decomposition answers[J]. IEEE Transactions on Signal Processing, 2008,56(1):85-95.
[21] 张欣,杜修力. 基于调频EMD的结构非线性辨识方法研究[J]. 工程力学,2011,(08):83-88.
ZHANG Xin, Du Xiu-li. Identification of nonlinear system by frequency modulated EMD method[J]. Engineering Mechanics,2011,(08):83-88.
[22] 张超, 袁彦霞. 频率调制经验模态分解在轴承故障诊断中的应用[J]. 振动与冲击,2014,(18):185-189.
ZHANG Chao, YUAN Yan-xia. Application of frequency modulated EMD in bearing fault diagnosis [J]. Journal of Vibration and Shock, 2014,(18):185-189.