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Identification of time varying modal parameters

Published online by Cambridge University Press:  04 July 2016

J. E. Cooper
J. E. Cooper*
Affiliation:
Department of Aeronautical Engineering, University of Manchester
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Abstract

The ability to track time varying frequency and damping parameters using on-line versions of seven time domain system identification algorithms; Least Squares, Double Least Squares, Correlation Fit, Instrumental Variables, Instrumental Matrix with Delayed Observations, Extended Least Squares and Maximum Likelihood, is examined. Comparisons are made on results obtained from data generated from various simulated time varying systems, corrupted with both input and measurement noise, as a preliminary step before advancing onto real data. Only the Maximum Likelihood and Double Least Squares methods gave acceptable results on the most difficult data sets that were considered. The choice of an exponential weighting factor is crucial to the performance of the on-line methods when applied to data from time varying systems. The effect of assigning various values of the weighting factor is demonstrated. Further work is required to determine the optimum schemes for defining this parameter.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 94 , Issue 938 , October 1990, pp. 271 - 278
Copyright
Copyright © Royal Aeronautical Society 1990 

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Footnotes

*

Formerly of the Materials & Structures Department, Royal Aerospace Establishment, Farnborough.

References

1. Russo, M., Healy, M. and Brock, M. X-29A flight flutter data analysis by advanced methods, 3rd Flight Testing Conf 1986, AIAA 86-9737.Google Scholar
2. Eykhoff, P. System Identification, John Wiley. 1974.Google Scholar
3. Ibrahim, S. R. Modal confidence factor in vibration testing; J Spacecraft Rockets, 1978, 15, (5), pp 313–316.Google Scholar
4. Vold, H and Crowley, J. A modal confidence factor for the polyreference method; 3rd International Modal Analysis Conference, 1985, pp 305-310.Google Scholar
5. Ljung, L. and Soderstrom, T. Theory and Practice of Recursive Identification, MIT Press, 1983.Google Scholar
6. Davies, P. and Hammond, J. K. A Comparison of Fourier and Parametric Methods for Structural System Identification. J. Vibration, Acoustics, Stress + Reliability in Design. 1984, 106, pp 40–48.Google Scholar
7. Capecchi, D. Difference models for identification of mechanical linear systems in dynamics; Mech Systems Signal Process, 1989, 3, (2), pp 157–172.Google Scholar
8. Cooper, J. E. Comparison of Some Time Domain System Identification Techniques using Approximate Data Correlations. Proc IMAC 6. 1988. pp 1589–1595. (To appear in Int J Anal Exp Modal Anal).Google Scholar
9. Wright, J. R. Flutter test analysis using a recursive system representation, J. Aircraft, 1974, 11, (12), pp 774–777.Google Scholar
10. Freidlander, B. The overdetermined recursive instrumental variable method, IEEE Trans AC, 1984, 29, (4), pp 353–355.Google Scholar
11. Cooper, J. E. and Wright, J. R. ‘Comparison of Some Time Domain Methods for Structural System Identification’, 2nd Symposium on Aeroelasticity and Structural Dynamics, 1985, pp 595–604.Google Scholar
12. Astrom, K. and Eykhoff, P. System Identification, A Survey. Automatica, 1971, 7, pp 123–162.Google Scholar