Utilization of Blind Source Separation Techniques for Modal Analysis
In past few years, there have been attempts at utilizing Blind Source Separation (BSS) and Independent Component Analysis (ICA) techniques for modal analysis purposes. Most of the early work in this regard has been promising, though restricted to application of these techniques to analytical and laboratory based experimental structures. It is felt that in order to make these techniques applicable to more challenging scenarios, they need to be modified keeping in view the demands of modal parameter estimation procedure. This includes making them more robust and applicable to handle complex scenarios (for e.g. closely coupled modes, heavily damped modes, low signal-to-noise ratio, etc.). This forms the motivation for this paper which aims at tuning BSS / ICA methods for modal analysis purposes in an effective and efficient manner. Amongst other methods, it is shown how to incorporate signal processing techniques, modify BSS techniques to handle data in specified frequency ranges, extract modal parameters from limited output channels, etc. to derive most benefits out of these algorithms.
Unable to display preview. Download preview PDF.
- 1.Antoni, J.; “Blind separation of vibration components: principles and demonstrations”, Mechanical Systems and Signal Processing, Vol. 19 (6), pp. 1166–1180, November 2005.Google Scholar
- 3.Poncelet, F., Kerschen, G., Golinval, J.C. (2006), “Experimental Modal Analysis Using Blind Source Separation Techniques”; Proceedings of ISMA International Conference on Noise and Vibration Engineering, Katholieke Universiteit Leuven, Belgium.Google Scholar
- 4.Chauhan, S., Martell, R., Allemang, R. J. and Brown, D. L. (2007), “Application of Independent Component Analysis and Blind Source Separation Techniques to Operational Modal Analysis”, Proceedings of the 25th IMAC, Orlando (FL), USA.Google Scholar
- 5.Antoni, J., Chauhan, S. (2010); “Second Order Blind Source Separation (SO-BSS) and its relation to Stochastic Subspace Identification (SSI) algorithm”, To be presented at 28th IMAC, Jacksonville (FL), USA.Google Scholar
- 8.Belouchrani, A., Abed-Meraim, K.K., Cardoso, J.F., Moulines, E.; “Second order blind separation of correlated sources”, Proceedings of International Conference on Digital Signal Processing, pp. 346–351, 1993.Google Scholar
- 9.Cardoso, J.F., Souloumiac, A.; “Jacobi angles for simultaneous diagonalization”, SIAM Journal of Matrix Analysis and Applications, Vol. 17, Number 1, pp. 161–164, January, 1996.Google Scholar
- 10.Hori, G.; “A new approach to joint diagonalization”, Proceedings of 2nd International Workshop on ICA and BSS, ICA’ 2000, pp. 151–155, Helsinki, Finland, June 2000.Google Scholar
- 11.Stoica, P., Moses, R.L.; “Introduction to Spectral Analysis”, Prentice-Hall, 1997.Google Scholar
- 12.Allemang, R.J.; “Vibrations: Experimental Modal Analysis”, Structural Dynamics Research Laboratory, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, CN-20-263-663/664, Revision - June 1999.Google Scholar
- 13.Vold, H., Kundrat, J., Rocklin, T., Russell, R.; “A multi-input modal estimation algorithm for mini-computers”, SAE Transactions, Volume 91, Number 1, pp. 815–821, January, 1982.Google Scholar
- 14.Shelly, S.J.; “Investigation of discrete modal filters for structural dynamic applications”, PhD Dissertation, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, 1991.Google Scholar
- 15.Meirovitch, L.; “Analytical Methods in Vibrations”, Macmillan, 1967.Google Scholar