Abstract
Modal analysis is a well-established method for analysis of linear dynamic structures, but its extension to non-linear structures has proven to be much more problematic. A number of viewpoints on non-linear modal analysis as well as a range of different non-linear system identification techniques have emerged in the past, each of which tries to preserve a subset of properties of the original linear theory. The objective of this paper is to discuss how the Hilbert-Huang transform can be used for detection and characterization of non-linearity, and to present an optimization framework which combines the Hilbert-Huang transform and complex non-linear modal analysis for quantification of the selected model. It is argued that the complex non-linear modes relate to the intrinsic mode functions through the reduced order model of slow-flow dynamics. The method is demonstrated on simulated data from a system with cubic non-linearity.
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Acknowledgements
The authors are grateful to Rolls-Royce plc for providing the financial support for this project and for giving permission to publish this work. This work is part of a Collaborative R and T Project “SAGE 3 WP4 Nonlinear Systems” supported by the CleanSky Joint Undertaking and carried out by Rolls-Royce plc and Imperial College.
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Ondra, V., Sever, I.A., Schwingshackl, C.W. (2017). Non-linear System Identification Using the Hilbert-Huang Transform and Complex Non-linear Modal Analysis. In: Kerschen, G. (eds) Nonlinear Dynamics, Volume 1. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-54404-5_8
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