Empirical Slow-Flow Identification for Structural Health Monitoring and Damage Detection

Young S. Lee, Michael McFarland, Lawrence Bergman, Alexander F Vakakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We utilize the nonlinear system identification (NSI) methodology, which was recently developed based on the correspondence between analytical and empirical slow-flow dynamics. Performing empirical mode decomposition on the simulated or measured time series to extract intrinsic mode oscillations, we establish nonlinear interaction models, which invoke slowly-varying forcing amplitudes that can be computed from empirical slow-flows. By comparing the spatio-temporal variations of the nonlinear modal interactions for structures with defects and those for the underlying healthy structure, we will demonstrate that the proposed NSI method can not only explore the smooth/nonsmooth nonlinear dynamics caused by structural damage, but also the extracted vibration characteristics can directly be implemented for structural health monitoring and detecting damage locations. Starting with traditional tools such as the modal assurance criterion (MAC) and the coordinate MAC are utilized.

Original languageEnglish (US)
Title of host publicationTopics in Dynamics of Civil Structures- Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013
Pages617-624
Number of pages8
Edition7
DOIs
StatePublished - Jan 1 2014
Event31st International Modal Analysis Conference on Structural Dynamics, IMAC 2013 - Garden Grove, CA, United States
Duration: Feb 11 2013Feb 14 2013

Publication series

NameConference Proceedings of the Society for Experimental Mechanics Series
Number7
Volume45
ISSN (Print)2191-5644
ISSN (Electronic)2191-5652

Other

Other31st International Modal Analysis Conference on Structural Dynamics, IMAC 2013
CountryUnited States
CityGarden Grove, CA
Period2/11/132/14/13

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Keywords

  • Empirical mode decomposition
  • Intrinsic modal oscillator
  • Nonlinear interaction model
  • Nonlinear system identification
  • Structural health monitoring

ASJC Scopus subject areas

  • Engineering(all)
  • Computational Mechanics
  • Mechanical Engineering

Cite this

Lee, Y. S., McFarland, M., Bergman, L., & Vakakis, A. F. (2014). Empirical Slow-Flow Identification for Structural Health Monitoring and Damage Detection. In Topics in Dynamics of Civil Structures- Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013 (7 ed., pp. 617-624). (Conference Proceedings of the Society for Experimental Mechanics Series; Vol. 45, No. 7). https://doi.org/10.1007/978-1-4614-6585-0_59