Damage classification for structural health monitoring using time-frequency feature extraction and continuous hidden Markov models

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

15 Scopus citations

Abstract

We propose an algorithm for the classification of structural damage based on the use of the continuous hidden Markov modeling (HMM) technique. Our approach employs HMMs to model time-frequency damage features extracted from structural data using the matching pursuit decomposition algorithm. We investigate modeling with continuous observation-density HMMs and discuss the trade-offs involved as compared to the discrete HMM case. A variational Bayesian method is employed to automatically estimate the HMM state number and adapt the classifier for real-time use. We present results that classify structural and material (fatigue) damage in a boltedjoint structure.

Original languageEnglish (US)
Title of host publicationConference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Pages848-852
Number of pages5
DOIs
StatePublished - Dec 1 2007
Event41st Asilomar Conference on Signals, Systems and Computers, ACSSC - Pacific Grove, CA, United States
Duration: Nov 4 2007Nov 7 2007

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other41st Asilomar Conference on Signals, Systems and Computers, ACSSC
CountryUnited States
CityPacific Grove, CA
Period11/4/0711/7/07

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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