Abstract

A novel approach based on hidden Markov models (HMMs) is proposed for damage classification in composite structures. Time-frequency damage features are first extracted from the measured signals using the matching pursuit decomposition algorithm. The features are then incorporated as observation sequences to be modeled statistically by the HMMs. Once built, the HMMs are integrated very efficiently into a Bayesian framework for the classification of structural damage. Both discrete and continuous observation density HMMs are considered; continuous HMMs are shown to yield better accuracy, but at the cost of added computational complexity. A decision fusion procedure is employed to combine the local classification results at each sensor, significantly enhancing the overall classification performance. The utility of the proposed technique is demonstrated by its application to the classification of delamination damage, impact damage, and progressive tensile damage in laminated composites.

Original languageEnglish (US)
Pages (from-to)1271-1288
Number of pages18
JournalJournal of Intelligent Material Systems and Structures
Volume20
Issue number11
DOIs
StatePublished - Jul 2009

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Hidden Markov models
Composite structures
Laminated composites
Delamination
Computational complexity
Fusion reactions
Decomposition
Sensors

Keywords

  • composite structures
  • damage classification
  • damage detection
  • hidden Markov model
  • Integrated vehicle health management
  • matching pursuit decomposition
  • sensor fusion.

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanical Engineering

Cite this

On the use of hidden markov modeling and time-frequency features for damage classification in composite structures. / Wenfan Zhou, Zhou; Kovvali, Narayan; Reynolds, Whitney; Papandreou-Suppappola, Antonia; Chattopadhyay, Aditi; Cochran, Douglas.

In: Journal of Intelligent Material Systems and Structures, Vol. 20, No. 11, 07.2009, p. 1271-1288.

Research output: Contribution to journalArticle

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