Modal Parameters Identification Based on Noise Cancellation from Measured Vibration Response Signals

Abstract:

Measured vibration response signals are inevitably contaminated with noise when a data acquisition system is used for an experimental measurement. This situation often leads to serious difficulties in identifying the modal parameters with proper accuracy. This paper presents a noise cancellation method for measured impulsive response functions (IRFs) based on structured low rank approximation (SLRA) so as to improve the accuracy of the modal parameters identification. Numerical study of a cantilever beam model is carried out to demonstrate the performance of the proposed method. The results show that this method can remove noise from measured IRFs efficiently, and the modal parameter identifications based on the filtered IRFs are very good.

• Periodical: Applied Mechanics and Materials (Volumes 50-51)
• Pages: 828-832
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.50-51.828
• Online since: February 2011
• Authors: Xing Xian Bao, Cui Lin Li
• Keywords: Impulsive Response Function, Modal Paramenter Identification, Noise Cancellation, Singular Value Decomposition (SVD), Vibration Measuring

References

[1] D.J. Ewins: Modal Testing: Theory, Practice and Applications, (Research Studies Press, Baldock, Hertfordshire, England, 2000).

[2] N. Maia, J. Silva, J. He, N. Lieven, R. -M. Lin, G. Skingle, et al.: Theoretical and Experimental Modal Analysis, (Research Studies Press, Taunton, Somerset, England, 1997).

[3] R.J. Allemang and D.L. Brown: A unified matrix polynomial approach to modal identification, Journal of Sound and Vibration, vol. 211 (1998), p.301–322.

DOI: 10.1006/jsvi.1997.1321

[4] B. De Moor: Structured total least squares and L2 approximation problems, Linear Algebra and its Applications (1993), pp.188-189: 163–207.

DOI: 10.1016/0024-3795(93)90468-4

[5] I. Markovsky and S.V. Huffel: Overview of total least squares methods, Signal Processing, vol. 87 (2007), p.2283–2302.

DOI: 10.1016/j.sigpro.2007.04.004

[6] I. Markovsky: Structured low-rank approximation and its applications, Automatica, vol. 44 (2008), p.891–909.

DOI: 10.1016/j.automatica.2007.09.011

[7] M. Schuermans, P. Lemmerling and S. V. Huffel: Block-row hankel weighted low rank approximation, Numerical Linear Algebra with Applications, vol. 13 (2006), p.293–302.

DOI: 10.1002/nla.459

[8] M. T. Chu, R. E. Funderlic and R. J. Plemmons: Structured low rank approximation, Linear Algebra and its Applications, vol. 366 (2003), p.157–172.

DOI: 10.1016/s0024-3795(02)00505-0

[9] J.A. Cadzow: Signal enhancement—A composite property mapping algorithm, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36 (1988), p.49–62.

DOI: 10.1109/29.1488

[10] D. Tufts and A. Shah: Estimation of a signal waveform from noisy data using low-rank approximation to a data matrix, IEEE Transactions on Signal Processing, vol. 41 (1993), p.1716–1721.

DOI: 10.1109/78.212753