The slow-flow method of identification in nonlinear structural dynamics

Kerschen, G; Vakakis, AF; Lee, YS; McFarland, DM; Bergman, LA

dc.contributor.author	Kerschen, G	en
dc.contributor.author	Vakakis, AF	en
dc.contributor.author	Lee, YS	en
dc.contributor.author	McFarland, DM	en
dc.contributor.author	Bergman, LA	en
dc.date.accessioned	2014-03-01T02:51:17Z
dc.date.available	2014-03-01T02:51:17Z
dc.date.issued	2007	en
dc.identifier.issn	0277786X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35441
dc.subject	Complexification-averaging	en
dc.subject	Hilbert-Huang transform	en
dc.subject	Nonlinear system identification	en
dc.subject	Slow flow	en
dc.subject.other	Complexification averaging	en
dc.subject.other	Empirical mode decomposition	en
dc.subject.other	Hilbert-Huang transforms (HHT)	en
dc.subject.other	Slow flow	en
dc.subject.other	Identification (control systems)	en
dc.subject.other	Mathematical models	en
dc.subject.other	Structural dynamics	en
dc.subject.other	Nonlinear systems	en
dc.title	The slow-flow method of identification in nonlinear structural dynamics	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1117/12.716823	en
heal.identifier.secondary	http://dx.doi.org/10.1117/12.716823	en
heal.identifier.secondary	65291M	en
heal.publicationDate	2007	en
heal.abstract	The Hilbert-Huang transform (HHT) has been shown to be effective for characterizing a wide range of nonstationary signals in terms of elemental components through what has been called the empirical mode decomposition. The HHT has been utilized extensively despite the absence of a serious analytical foundation, as it provides a concise basis for the analysis of strongly nonlinear systems. In this paper, we attempt to provide the missing link, showing the relationship between the EMD and the slow-flow equations of the system. The slow-flow model is established by performing a partition between slow and fast dynamics using the complexification-averaging technique, and a dynamical system described by slowly-varying amplitudes and phases is obtained. These variables can also be extracted directly from the experimental measurements using the Hilbert transform coupled with the EMD. The comparison between the experimental and analytical results forms the basis of a nonlinear system identification method, termed the slow-flow model identification method, which is demonstrated using numerical examples.	en
heal.journalName	Proceedings of SPIE - The International Society for Optical Engineering	en
dc.identifier.doi	10.1117/12.716823	en
dc.identifier.volume	6529 PART 1	en