A time-varying identification method for mixed response measurements

TC Allison, AK Miller, DJ Inman

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)

Abstract

The proper orthogonal decomposition is a method that may be applied to linear and nonlinear structures for extracting important information from a measured structural response. This method is often applied for model reduction of linear and nonlinear systems and has been applied recently for time-varying system identification. Although methods have previously been developed to identify time-varying models for simple linear and nonlinear structures using the proper orthogonal decomposition of a measured structural response, the application of these methods has been limited to cases where the excitation is either an initial condition or an applied load but not a combination of the two. This paper presents a method for combining previously published proper orthogonal decomposition-based identification techniques for strictly free or strictly forced systems to identify predictive models for a system when only mixed response data are available, i.e. response data resulting from initial conditions and loads that are applied together. This method extends the applicability of the previous proper orthogonal decomposition-based identification techniques to operational data acquired outside of a controlled laboratory setting. The method is applied to response data generated by finite element models of simple linear time-invariant, time-varying, and nonlinear beams and the strengths and weaknesses of the method are discussed.
Translated title of the contributionA time-varying identification method for mixed response measurements
Original languageEnglish
Pages (from-to)850 - 868
Number of pages16
JournalJournal of Sound and Vibration
Volume319 (3-5)
DOIs
Publication statusPublished - Jul 2009

Fingerprint

Dive into the research topics of 'A time-varying identification method for mixed response measurements'. Together they form a unique fingerprint.

Cite this