Toward a fundamental understanding of the Hilbert-Huang transform 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	2014-03-01T02:50:55Z
dc.date.available	2014-03-01T02:50:55Z
dc.date.issued	2006	en
dc.identifier.issn	21915644	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35208
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-84861536498&partnerID=40&md5=04eee6dddbcc4fdb992e78b405ec6595	en
dc.subject.other	Analytical results	en
dc.subject.other	Elemental components	en
dc.subject.other	Empirical Mode Decomposition	en
dc.subject.other	Experimental measurements	en
dc.subject.other	Fast dynamics	en
dc.subject.other	Hilbert Huang transforms	en
dc.subject.other	Hilbert transform	en
dc.subject.other	Model identification	en
dc.subject.other	Non-linear system identification	en
dc.subject.other	Nonlinear structural dynamics	en
dc.subject.other	Nonstationary signals	en
dc.subject.other	Numerical example	en
dc.subject.other	Strongly nonlinear system	en
dc.subject.other	Dynamical systems	en
dc.subject.other	Exhibitions	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Structural dynamics	en
dc.subject.other	Signal processing	en
dc.title	Toward a fundamental understanding of the Hilbert-Huang transform in nonlinear structural dynamics	en
heal.type	conferenceItem	en
heal.publicationDate	2006	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	Conference Proceedings of the Society for Experimental Mechanics Series	en