基于线调频自适应分解的时变系统瞬时模态参数识别 

张杰,史治宇,赵宗爽

振动与冲击 ›› 2020, Vol. 39 ›› Issue (22) : 103-109.

PDF(1489 KB)
PDF(1489 KB)
振动与冲击 ›› 2020, Vol. 39 ›› Issue (22) : 103-109.
论文

基于线调频自适应分解的时变系统瞬时模态参数识别 

  • 张杰,史治宇,赵宗爽
作者信息 +

Instantaneous modal parameter identification of time-varying systems based on adaptive chirplet decomposition

  • ZHANG Jie,SHI Zhiyu,ZHAO Zongshuang
Author information +
文章历史 +

摘要

本文提出了基于线调频小波时频分解的时变系统瞬时模态参数识别方法。该方法首先应用线调频小波对时变系统加速度响应信号进行时频分析,通过小波脊方法提取瞬时频率,再应用卡尔曼滤波进行自适应分解获得各阶响应的幅值信息。为了更精确地识别时变结构的阻尼比本文推导了基于幅值能量法的时变阻尼比识别方法。线调频小波相比传统小波对具有更高的能量聚集性,因此该方法具有更高的瞬时频率提取精度,此外传统的基于幅值的阻尼识别方法易受噪声干扰,能量法在短区间内基于幅值进行整体积分具有较强的抗噪性和更高的识别精度。仿真算例中,构造了一个三自由度时变结构来验证识别方法,通过与传统小波和时域峰值法作对比,验证了方法识别瞬时模态参数的精确性和抗噪性。

Abstract

An instantaneous modal parameter identification method of time-varying systems based on the time-frequency decomposition of chirplet transform (CT) was proposed.First, the adaptive linear chirplet transform was applied in the time-frequency analysis of time-varying system acceleration responses, and the instantaneous frequency was extracted by using the wavelet ridge method.Then each order response was adaptively decomposed to obtain the amplitude information with a Kalman filter.In order to more accurately identify the damping ratio of the structure, a time-varying damping ratio identification method based on the amplitude energy was also proposed.It is found that CT has higher energy concentration than the traditional wavelet.Therefore, the method has higher instantaneous frequency extraction accuracy.In addition, the traditional amplitude-based damping identification method is susceptible to noise interference, while the energy method has strong noise immunity and higher identification accuracy based on the integral of the amplitude in a short interval.As a simulation example, a three-degree-of-freedom time-varying structure was constructed to verify the correctness and anti-noise capability of the method.The method can more precisely identify the instantaneous frequency and damping ratio, comparing with the traditional wavelet and time domain peak method.

关键词

线调频小波变换 / 时频脊提取 / 自适应滤波 / 时变系统 / 瞬时模态参数识别 / 能量法

Key words

chirplet transform (CT) / time-frequency ridge extraction / adaptive filtering / time-varying system / instantaneous modal parameters identification / energy method

引用本文

导出引用
张杰,史治宇,赵宗爽. 基于线调频自适应分解的时变系统瞬时模态参数识别 [J]. 振动与冲击, 2020, 39(22): 103-109
ZHANG Jie,SHI Zhiyu,ZHAO Zongshuang. Instantaneous modal parameter identification of time-varying systems based on adaptive chirplet decomposition[J]. Journal of Vibration and Shock, 2020, 39(22): 103-109

参考文献

[1]于开平, 庞世伟, 赵婕. 时变线性/非线性结构参数识别及系统辨识方法研究进展[J]. 科学通报, 2009, 54(20): 3147-3156.
Yu Kaiping, Pang Shiwei, Zhao Jie. Advances in method of time-varying linear/nonlinear structural system identification and parameter estimate[J]. Chinese Science Bulletin, 2009, 54(20): 3147-3156.
[2] Feldman M. Non-linear system vibration analysis using Hilbert transform II: forced vibration analysis method[J]. Mechanical Systems and Signal Processing, 1994, 8(3): 309-318.
[3] Bao C X, Hao H, et al. Time-varying system identification using a newly improved HHT algorithm[J]. Computers and Structures, 2009, 87:1611-1623.
[4] 程军圣, 张亢, 杨宇, 于德介. 局部均值分解与经验模式分解的对比研究[J]. 振动与冲击, 2009, 28(5):13-16.
Cheng Junsheng, Zhang Kang, Yang Yu, Yu Dejie. Comparison between the methods of local mean decomposition and empirical mode decomposition[J]. Journal of Vibration and Shock, 2009, 28(5):13-16.
[5] Shi Z Y, Law S S, Xu X. Identification of linear time-varying MDOF dynamic systems from forced excitation using Hilbert transform and EMD method[J]. Journal of Sound and Vibration, 2009, 321(3-5): 572-589.
[6] 续秀忠, 华宏星, 张志谊, 陈兆能. 应用时频表示进行结构时变模态参数辨识[J]. 振动与冲击, 2002, 21(2): 36-40.
Xu Xiuzhong, Hua Hongxing, Zhang Zhiyi, Chen Zhaoneng. Time-varying modal frequency identification by using time-frequency representation[J]. Journal of Vibration and Shock, 2002, 21(2): 36-40.
[7] Dziedziech K, Staszewski W J, Basu B, et al. Wavelet-based detection of abrupt changes in natural frequencies of time-variant systems[J]. Mechanical Systems and Signal Processing, 2015, 64-65:347-359.
[8] Zhang G W, Tang B P, Chen Z. Operational modal parameter identification based on PCA-CWT[J]. Measurement, 2019, 139: 334-345.
[9] Xu X, Shi Z Y, You Q. Identification of linear time-varying systems using a wavelet-based state-space method[J]. Mechanical Systems and Signal Processing, 2012, 26: 91-103.
[10] Hera A, Shinde A, Hou Z K. Issues in tracking instantaneous modal parameters for structural health monitoring using wavelet approach[C]. In: Proc 23rdinternational modal analysis conference (IMAC XXIII), Orlando, Florida, USA, 2005: 338-347.
[11] Liu K. Identification of linear time-varying systems[J]. Journal of Sound and Vibration, 1997, 204: 487-500.
[12] 庞世伟, 于开平, 邹经湘. 识别时变结构模态参数的改进子空间方法[J]. 应用力学学报, 2005, 22: 184-188.
PANG Shiwei, Yu Kaiping, Zou Jingxiang. Improved subspace method with application in linear time-varying structural modal parameter identification[J]. Chinese Journal of Applied Mechanics, 2005, 22: 184-188.
[13] Tasker F, Bosse A, Fisher S. Real-time modal parameter estimation using subspace methods: Theory[J]. Mechanical Systems and Signal Processing, 1998, 12: 797-808.
[14] Shi Z Y, Law S S, Li H N. Subspace-Based identification of time-varying system[J]. Journal of AIAA, 2007, 45(8): 2042-2050.
[15] Deng Y, Cheng C M, Yang Y, et al. Parametric identification of nonlinear vibration systems via polynomial chirplet transform[J]. Journal of Vibration and Acoustics, 2016, 138(5):051014-1-18.
[16] Yu G, Zhou Y. General linear chirplet transform[J]. Mechanical Systems and Signal Processing, 2015, (s70-71):958-973.
[17] Vold H,Herlufsen H,Mains M.Multi axle order tracking with the Vold-Kalman tracking filter[J].Journal of Sound and Vibration, 1997, 13(5):30-34.
[18] Seybert A F. Estimation of damping from response spectra[J]. Journal of Sound and Vibration.1981, 75(2):199-206.
[19] 李中付, 华宏星, 宋汉文, 陈之炎. 用时域峰值法计算频率和阻尼[J]. 振动与冲击, 2001, 20(3):5-6.
Li Zhongfu, Hua Hongxing, Song Hanwen, Chen Zhiyan. identification of frequencies and damping ratios with time domain peak values[J]. Journal of Vibration and Shock, 2001, 20(3):5-6.
[20] 曾储惠, 黄方林, 柳成荫, et al. 基于信号能量分析的结构阻尼比识别方法[J]. 振动与冲击, 2003, 22(2):66-68.
 Zeng Chuhui, Huang Fanglin, Liu Chengyin, et al. Damping ratio identification method based on signal energy analysis[J]. Journal of Vibration and Shock, 2003, 22(2):66-68.
[21] Tuma J. Setting the pass bandwidth in the Vold-Kalman order tracking filter[C]. Twelfth International Congress on Sound and Vibration, Lisbon, 2005, Paper719.
[22] Xin Y, Hao H, Li J. Time-varying system identification by enhanced Empirical Wavelet Transform based on Synchroextracting Transform[J]. Engineering Structures, 2019, 196(1), Article ID 109313.
[23] Musafere F, Sadhu A, Liu K. Time-Varying System Identification Using a Hybrid Blind Source Separation Method[M]. Structural Health Monitoring, Damage Detection & Mechatronics, Springer International Publishing, 2016, 7: 99-104.

PDF(1489 KB)

Accesses

Citation

Detail

段落导航
相关文章

/