2 Scopus citations

Abstract

We describe a statistical method for the classification of damage in complex structures. Our approach is based on a Bayesian framework using hidden Markov models (HMMs) to model time-frequency features extracted from structural data. We also propose two different methods for sensor fusion to combine information from multiple distributed sensors such that the overall classification performance is increased. The proposed approaches are applied to the classification and localization of delamination in a laminated composite plate. Results using both discrete and continuous observation density HMMs, together with the sensor fusion, are presented and discussed.

Original languageEnglish (US)
Title of host publicationModeling, Signal Processing, and Control for Smart Structures 2008
DOIs
StatePublished - Jun 3 2008
EventModeling, Signal Processing, and Control for Smart Structures 2008 - San Diego, CA, United States
Duration: Mar 10 2008Mar 12 2008

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6926
ISSN (Print)0277-786X

Other

OtherModeling, Signal Processing, and Control for Smart Structures 2008
CountryUnited States
CitySan Diego, CA
Period3/10/083/12/08

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Keywords

  • Damage detection and classification
  • Hidden Markov models
  • Sensor fusion
  • Structural health monitoring

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Zhou, W., Reynolds, W. D., Moncada, A., Kovvali, N., Chattopadhyay, A., Papandreou-Suppappola, A., & Cochran, D. (2008). Sensor fusion and damage classification in composite materials. In Modeling, Signal Processing, and Control for Smart Structures 2008 [69260N] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6926). https://doi.org/10.1117/12.776608