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Proceedings Paper

Sensor fusion and damage classification in composite materials
Author(s): Wenfan Zhou; Whitney D. Reynolds; Albert Moncada; Narayan Kovvali; Aditi Chattopadhyay; Antonia Papandreou-Suppappola; Douglas Cochran
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Paper 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.

Paper Details

Date Published: 3 April 2008
PDF: 12 pages
Proc. SPIE 6926, Modeling, Signal Processing, and Control for Smart Structures 2008, 69260N (3 April 2008); doi: 10.1117/12.776608
Show Author Affiliations
Wenfan Zhou, Arizona State Univ. (United States)
Whitney D. Reynolds, Arizona State Univ. (United States)
Albert Moncada, Arizona State Univ. (United States)
Narayan Kovvali, Arizona State Univ. (United States)
Aditi Chattopadhyay, Arizona State Univ. (United States)
Antonia Papandreou-Suppappola, Arizona State Univ. (United States)
Douglas Cochran, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 6926:
Modeling, Signal Processing, and Control for Smart Structures 2008
Douglas K. Lindner, Editor(s)

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