Modelling and Wavelet-Based Identification of 3-DOF Vehicle Suspension System
- 1
- 2
- 3
Abstract
In this paper, a three Degrees Of Freedom (DOF) model of a quarter vehicle suspension system is proposed including the seat driver mass. The modal parameters of this system, which indicate the comfort and the safety of the suspension, are identified using Wavelet analysis. Two applications of wavelet analysis are presented: signal denoising based on the Discrete Wavelet Transform (DWT) and modal identification based on the Continuous Wavelet Transform (CWT). It is shown that the CWT analysis of the system response, initially denoised using DWT, allows the estimation of the natural pulsations and the damping ratios. The usefulness of the DWT in denoising and the accuracy of the CWT in modal identification are tested and confirmed by applying them to the proposed model. The complete modeling and identification of a 3-DOF vehicle suspension system is developed and the simulation results verify these statements and are satisfactory.
- L. Zhu, Y. Zhu, M. Mao and M. Gu, “A New Method for Sparse Signal Denoising Based on Compressed Sensing, Second International Symposium on Knowledge Acquisition and Modeling,” Second International Symposium on Knowledge Acquisition and Modeling, Wuhan, 30 November-1 December 2009, pp. 35-38.
- D. Giaouris, J. W. Finch, O. C. Ferreira, R. M. Kennel and G. M. El-Murr, “Wavelet Denoising for Electric Drives,” IEEE Transactions on Industrial Electronics, Vol. 5, No. 2, 2008, pp. 543-550. doi:10.1109/TIE.2007.911943
- H. S. Hu, J. Wang, S. X. Qian and X. Z. Jiang, “Test Modeling and Parameter Identification of a Gun Magnetorheological Recoil Damper,” International Conference on Mechatronics and Automation, Changchun, 9-12 August 2009, pp. 3431-3436.
- N. Amann, J. Bocker and F. Prenner, “Active Damping of Drive Train Oscillations for an Electrically Driven Vehicle,” IEEE/ASME Transactions on Mechatronics, Vol. 9, No. 4, 2004, pp. 697-700. doi:10.1109/TMECH.2004.839036
- S.-L. Chen, J.-J. Liua and H.-C. Laia, “Wavelet Analysis for Identification of Damping Ratios and Natural Frequencies,” Journal of Sound and Vibration, Vol. 323, No. 1-2, 2009, pp. 130-147. doi:10.1016/j.jsv.2009.01.029
- J. Slavic, I. Simonovski and M. Boltezar, “Damping Identification Using a Continuous Wavelet Transform: Application to Real Data,” Journal of Sound and Vibration, Vol. 262, No. 2, 2003, pp. 291-307. doi:10.1016/S0022-460X(02)01032-5
- D. S. Laila, A. R. Messina and B. C. Pal, “A Refined Hilbert-Huang Transform with Applications to Interarea Oscillation Monitoring,” IEEE Transactions on Power Systems, Vol. 24, 2009, pp. 610-620. doi:10.1109/TPWRS.2009.2016478
- M. Zheng, F. Shen, Y. Dou and X. Yan, “Modal Identification Based on Hilbert-Huang Transform of Structural Response with SVD Preprocessing,” Acta Mechanica Sinica, Vol. 25, No. 6, 2009, pp. 883-888.
- I. Daubenchies, “The Wavelet Transform, Time-Frequency Localisation and Signal Analysis,” IEEE Transactions on Information Theory, Vol. 36, No. 5, 1990, pp. 961-1005. doi:10.1109/18.57199
- O. Rioul and M. Vetterli, “Wavelets and Signal Processing,” IEEE Signal Processing Magazine, Vol. 8, No. 4, 1991, pp. 14-38. doi:10.1109/79.91217
- M. Vetterli and C. Herley, “Wavelets and Filter Banks: Theory and Design,” IEEE Transaction on Signal Processing, Vol. 40, No. 9, 1992, pp. 2207-2232. doi:10.1109/78.157221
- W. J. Staszewski, “Wavelet Based Compression and Feature Selection for Vibration Analysis,” Journal of Sound and Vibration, Vol. 211, No. 5, 1998, pp. 735-760. doi:10.1006/jsvi.1997.1380