Abstract
Online monitoring of modal and physical parameters which change due to damage progression and aging of mechanical and structural systems is important for the condition and health monitoring of these systems. Usually, only the limited number of imperfect, noisy system state measurements is available, thus identification of time-varying systems with nonlinearities can be a very challenging task. In order to avoid conventional least squares and gradient identification methods which require uni-modal and double differentiable objective functions, this work proposes a modified differential evolution (DE) algorithm for the identification of time-varying systems. DE is an evolutionary optimisation method developed to perform direct search in a continuous space without requiring any derivative estimation. DE is modified so that the objective function changes with time to account for the continuing inclusion of new data within an error metric. This paper presents results of identification of a time-varying SDOF system with Coulomb friction using simulated noise-free and noisy data for the case of time-varying friction coefficient, stiffness and damping. The obtained results are promising and the focus of the further work will be on the convergence study with respect to parameters of DE and on applying the method to experimental data.
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References
Ljung L (1987) System identification: theory for the user. Prentice-Hall, Englewood Cliffs, p 519, ISBN 0-13-881640
Price KV, Storn RM, Lampinen JA (2005) Differential evolution a practical approach to global optimization. Springer, Berlin/New York
Gross D, Harris CM (1985) Fundamentals of queuing theory. Wiley, New York
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
Rechenberg I (1973) Evolutionstrategie. Frommann-Holzboog, Stuttgart
Schwefel HP (1994) Evolution and optimum seeking. Wiley, New York
Holland JH (1962) Outline for a logical theory of adaptive systems. J assoc comput mach 8:212–229
Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, Reading
Storn R, Price KV (1995) Differential evolution – a simple and efficient adaptive scheme for global optimization over continous spaces. Technical report TR-95-012, ICSI
Kyprianou A, Worden K, Panet M (2001) Identification of hysteretic systems using the differential evolution algorithm. J Sound Vib 248: 289–314
Worden K, Manson G (2011) On the Identification of Hysteretic Systems, Part I: an Extended Evolutionary Scheme. In: Nonlinear modeling and applications, vol 2 conference proceedings of the society for experimental mechanics series, Proceedings of the 28th IMAC, A Conference on Structural Dynamics, February 1–4, 2010, Jacksonville, Florida, USA, vol 11, pp 67–75
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The financial support by the SYSWIND project, funded by the Marie Curie Actions under the Seventh Framework Programme for Research and Technology Development of the EU, is gratefully acknowledged.
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© 2014 The Society for Experimental Mechanics
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Perisic, N., Green, P.L., Worden, K., Kirkegaard, P.H. (2014). Identification of Time-Varying Nonlinear Systems Using Differential Evolution Algorithm. In: Allemang, R., De Clerck, J., Niezrecki, C., Wicks, A. (eds) Topics in Modal Analysis, Volume 7. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6585-0_56
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DOI: https://doi.org/10.1007/978-1-4614-6585-0_56
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