基于VMD-SSI的结构模态参数识别

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (10) : 81-91.

论文

基于VMD-SSI的结构模态参数识别

殷红1，董康立2，彭珍瑞1

作者信息 +

Structural modal parameter identification based on VMD-SSI

YIN Hong1，DONG Kangli2，PENG Zhenrui1

Author information +

文章历史 +

摘要

将变分模态分解(VMD)和随机子空间法(SSI)结合，提出了基于VMD-SSI的结构模态参数识别新方法。针对VMD中的模态分层数 K 值确定困难的问题，提出模态重复比率准则，保证了模态信息的有效分解。依据模态重复比准则确定测量信号的最优分层数 K ；利用VMD方法进行信号并行分解，用奇异值分解(SVD)去噪，以提高模态参数的识别精度。用该研究提出的VMD-SSI方法识别模态固有频率和阻尼，用VMD方法辨识模态振型，将VMD-SSI法应用于外伸梁模型的模态参数识别，并利用统计理论分别检验识别的模态频率、模态阻尼和模态振型的精度。结果表明, VMD-SSI法识别模态参数的精度高于传统SSI法。

Abstract

In order to improve the modal parameter identification precision,a VMD-SSI modal identification method was proposed, which is based on variational mode decomposition(VMD) and stochastic subspace identification(SSI).A modal repetition ratio criterion was proposed for the optimization of mode number K , which ensures the effective decomposition of modal information.Firstly, the optimal K of measurement signal was determined according to the proposed modal repetition ratio criterion.Secondly, singular value decomposition (SVD) was used to denoise, which further improves the accuracy of modal identificati on.Thirdly, VMD-SSI method was proposed to realize the identification of structural modal parameters.Finally, the VMD-SSI method was applied to the modal identification of the overhanging beam model.The validity of the modal frequency, modal damping and mode shape was tested by statistical theory.The results show that the modal identification precision by VMD-SSI method is statistically higher than that by traditional SSI method.

导出引用

殷红1，董康立2，彭珍瑞1. 基于VMD-SSI的结构模态参数识别[J]. 振动与冲击, 2020, 39(10): 81-91

YIN Hong1，DONG Kangli2，PENG Zhenrui1. Structural modal parameter identification based on VMD-SSI[J]. Journal of Vibration and Shock, 2020, 39(10): 81-91

参考文献

[1] Zhang J, Maes K, De Roeck G, et al. Optimal sensor placement for multi-setup modal analysis of structures[J]. Journal of Sound and Vibration, 2017, 401: 214–232.

[2] Ewins D J. Modal Testing: Theory Practice and Application(second Edition)[M]. Research Studies Press, 2000.

[3] 曹树谦,张文德,萧龙翔.振动结构模态分析:理论、实验与应用(第2版) [M].天津:天津大学出版社,2014.

CAO Shuqian, ZHANG Wende, XIAO Longxiang. Modal analysis of vibrating structures: Theory, experiment and application (Second edition) [M]. Tianjin: Tianjin Jing University Press, 2014.

[4] Wang J , Law S S , Yang Q S . Sensor placement methods for an improved force identification in state space[J]. Mechanical Systems & Signal Processing, 2013, 41(1-2):254-267.

[5] 李帅.工程结构模态参数辨识与损伤识别方法研究[D].重庆大学博士学位论文,2013.

LI Shuai. Research on modal parameter identification and damage identification method of engineering structure [D]. Chongqing University, 2013.

[6] Huang N E , Shen Z, Long S R. A new view of nonlinear water waves: the Hilbert spectrum[J]. Annual Review of Fluid Mechanics, 1999, 31: 417-457.

[7] Rilling G, Flandrin P. One or two frequencies? The empirical mode decomposition answers[J]. IEEE Transactions on Signal Processing, 2008, 56 (1): 85-95.

[8] Dragomiretskiy K, Zosso D. Variational mode decomposition [J]. IEEE Transactions on Signal Processing, 2014, 62(3):531-544.

[9] Bagheri A, Ozbulut O E, Harris D K. Structural system identification based on variational mode decomposition [J]. Journal of Sound and Vibration, 2018, 417:182-197.

[10] 刘长良，武英杰，甄成刚. 基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J]. 中国电机工程学报, 2015,35(13):3358-3365.

LIU Changliang, WU Yingjie, ZHEN Chenggang. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering [J]. Proceedings of The Chinese Society for Electrical Engineering, 2015,35(13):3358-3365.

[11] 刘尚坤，唐贵基，王晓龙. 基于改进变分模态分解的旋转机械故障时频分析方法[J]. 振动工程学报, 2016, 29(6):1119-1126.

LIU Shangkun, TANG Guiji, WANG Xiaolong. Time frequency analysis method for rotary mechanical fault based on improved variational mode decomposition [J]. Journal of Vibration Engineering, 2016, 29(6):1119-1126.

[12] 唐贵基，王晓龙. IVMD融合奇异值差分谱的滚动轴承早期故障诊断[J]. 振动、测试与诊断, 2016,36(4):700-707.

TANG Guiji, WANG Xiaolong. An incipient fault diagnosis method for rolling bearing based on improved variational mode decomposition and singular value difference spectrum [J]. Journal of Vibration, Measurement & Diagnosis, 2016,36(4):700-707.

[13] 牟伟杰, 石林锁, 蔡艳平,等. 基于KVMD-PWVD与LNMF的内燃机振动谱图像识别诊断方法[J]. 振动与冲击，2017,36(2):45-51.

MOU Weijie, SHI Linsuo, CAI Yanping, et al. IC engine fault diagnosis method based on KVMD-PWVD and LNMF [J]. Journal of Vibration and Shock, 2017,36(2):45-51.

[14] Li Z P, Chen J L, Zi Y Y, et al. Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive [J]. Mechanical Systems and Signal Processing, 2017, 85:512-529.

[15] Lian J J, Liu Z, Wang H J, et al. Adaptive variational mode decomposition method for signal processing based on mode characteristic [J]. Mechanical Systems and Signal Processing, 2018, 107:53-77.

[16] 钱征文, 程礼, 李应红. 利用奇异值分解的信号降噪方法[J]. 振动、测试与诊断, 2011, 31(4):459-463.

QIAN Zhengwen, CHENG Li, LI Yinghong. Noise reduction method based on singular value decomposition [J]. Journal of Vibration, Measurement & Diagnosis, 2011, 31(4):459-463.

[17] Zhang X H, Xu Y L, Zhu S Y, et al. Dual-type sensor placement for multi-scale response reconstruction [J]. Mechatronics, 2014,24:376-384.

[18] 盛骤, 谢式千, 潘承毅. 概率论与数理统计(第四版)[M].北京:高等教育出版社,2010.