Modal parameter identification is significant in the field of vibration and control for tracking structural vibrations and evaluating structural performances. A novel parameter identification method based on the intrinsic chirp component decomposition (ICCD) and the Hilbert transform is proposed for forced time-varying systems. The developed ICCD decomposes the measured response signal into a limited number of intrinsic chirp components, and instantaneous modal parameters of the system are identified by the Hilbert transform of each intrinsic chirp component. The effectiveness and the accuracy of the proposed approach are investigated in two case studies, that is, a discrete mass-spring-damper oscillator and a cantilever beam. Smooth and periodical parameter variations are discussed in each case. Comparison with the empirical mode decomposition based method validates that the proposed method has better identification ability on estimating the instantaneous natural frequency of the system with the presence of noise. Results demonstrate that the ICCD-based approach could be a reasonable alternative for identification of forced time-varying systems in a noise environment.
BachnasAATóthRLudlageJHA, et al. (2014) A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study. Journal of Process Control24(4): 272–285.
2.
ChenSPengZYangY, et al. (2017) Intrinsic chirp component decomposition by using Fourier Series representation. Signal Processing137: 319–327.
3.
CooperJE (1990) Identification of time varying modal parameters. Aeronautical Journal94(938): 271–278.
4.
DjurovićIStankovićL (2004) An algorithm for the Wigner distribution based instantaneous frequency estimation in a high noise environment. Signal Processing84(3): 631–643.
5.
DragomiretskiyKZossoD (2014) Variational mode decomposition. IEEE Transactions on Signal Processing62(3): 531–544.
6.
DziedziechKStaszewskiWJUhlT (2015) Wavelet-based modal analysis for time-variant systems. Mechanical Systems and Signal Processing50-51: 323–337.
7.
FeldmanM (1994a) Non-linear system vibration analysis using Hilbert transform-I. Free vibration analysis method 'FREEVIB. Mechanical Systems and Signal Processing8(2): 119–127.
8.
FeldmanM (1994b) Non-linear system vibration analysis using Hilbert transform-II. Forced vibration analysis method 'FORCEVIB. Mechanical Systems and Signal Processing8(3): 309–318.
9.
FeldmanM (2006) Time-varying vibration decomposition and analysis based on the Hilbert transform. Journal of Sound and Vibration295: 518–530.
10.
GhanemRRomeoF (2000) A Wavelet-based approach for the identification of linear time-varying dynamical systems. Journal of Sound and Vibration234(4): 555–576.
11.
GuoYKareemA (2016) Non-stationary frequency domain system identification using time–frequency representations. Mechanical Systems and Signal Processing72-73: 712–726.
12.
HuangNEShenZLongSR, et al. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences454: 903–995.
13.
KangWZhangYDongX (2018) Micro-Doppler feature extraction for interferometric radar based on Viterbi algorithm and intrinsic chirp component decomposition. In: 22nd International Microwave and Radar Conference. Poland: Poznan, pp. 508–511.
14.
KijewskiTKareemA (2003) Wavelet transforms for system identification in civil engineering. Computer-Aided Civil and Infrastructure Engineering18(5): 339–355.
15.
LeiYLuJHuangJ (2020) Integration of identification and vibration control of time-varying structures subject to unknown seismic ground excitation. Journal of Vibration and Control26(15–16): 1330–1344.
16.
LiJHaoH (2016) A review of recent research advances on structural health monitoring in Western Australia. Structural Monitoring and Maintenance3(1): 33–49.
17.
LiuKDengL (2005) Experimental verification of an algorithm for identification of linear time-varying systems. Journal of Sound and Vibration279(3–5): 1170–1180.
18.
LiuTYanSZZhangW (2016) Time–frequency analysis of nonstationary vibration signals for deployable structures by using the constant-Q nonstationary Gabor transform. Mechanical Systems and Signal Processing75: 228–244.
19.
MaZ-SDingQZhouS-D (2019) Novel adaptive methods for output-only recursive identification of time-varying systems subject to gross errors. Journal of Vibration and Control26(5–6): 306–317.
20.
MaZ-SLiLDingQ (2020) Multivariate recursive Bayesian linear regression and its applications to output-only identification of time-varying mechanical systems. Journal of Vibration and Control27(11–12): 1395–1406.
21.
NiPLiJHaoH, et al. (2018) Time-varying system identification using variational mode decomposition. Structural Control and Health Monitoring25(6): e2175.
22.
NiuJNingGShenY, et al. (2019) Detection and identification of cutting chatter based on improved variational nonlinear chirp mode decomposition. International Journal of Advanced Manufacturing Technology104(5–8): 2567–2578.
23.
ShiZYLawSSLiHN (2007) Subspace-Based Identification of Linear Time-Varying System. AIAA Journal45(8): 2042–2050.
24.
ShiZYLawSSXuX (2009) Identification of linear time-varying mdof dynamic systems from forced excitation using Hilbert transform and EMD method. Journal of Sound and Vibration321(3–5): 572–589.
25.
StaszewskiWJWallaceDM (2014) Wavelet-based frequency response function for time-variant systems—an exploratory study. Mechanical Systems and Signal Processing47(1–2): 35–49.
26.
SuWCLiuCYHuangCS (2014) Identification of instantaneous modal parameter of time-varying systems via a Wavelet-based approach and its application. Computer-Aided Civil and Infrastructure Engineering29(4): 279–298.
27.
TahaMMRNoureldinALuceroJL, et al. (2016) Wavelet transform for structural health monitoring: a compendium of uses and features. Structural Health Monitoring5(3): 267–295.
28.
WangCRenWXWangZC, et al. (2013) Instantaneous frequency identification of time-varying structures by continuous wavelet transform. Engineering Structures52: 17–25.
29.
WangCZhangJZhuHP (2020) A combined method for time-varying parameter identification based on variational mode decomposition and generalized Morse wavelet. International Journal of Structural Stability and Dynamics20(7): 2050077.
30.
WangZChenG (2014) Analytical mode decomposition with Hilbert transform for modal parameter identification of buildings under ambient vibration. Engineering Structures59: 173–184.
31.
WangZCChenGD (2013) Analytical mode decomposition of time series with decaying amplitudes and overlapping instantaneous frequencies. Smart Materials and Structures22(9): 095003.
32.
WangZCRenWXChenG (2018) Time–frequency analysis and applications in time-varying/nonlinear structural systems: A state-of-the-art review. Advances in Structural Engineering21(10): 1562–1584.
33.
WeiSChenSDongX, et al. (2019) Parametric identification of time-varying systems from free vibration using intrinsic chirp component decomposition. Acta Mechanica Sinica36(1): 188–205.
34.
YangYPengZKDongXJ, et al. (2014) General parameterized time-frequency transform. IEEE Transactions on Signal Processing62(11): 2751–2764.
35.
YangYBChangKC (2009) Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique. Journal of Sound and Vibration322(4–5): 718–739.
