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Proceedings Paper

Hidden Markov model based classification of structural damage
Author(s): Wenfan Zhou; Narayan Kovvali; Antonia Papandreou-Suppappola; Douglas Cochran; Aditi Chattopadhyay
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Paper Abstract

The ability to detect and classify damages in complex materials and structures is an important problem from both safety and economical perspectives. This paper develops a novel approach based on Hidden Markov Models (HMMs) for the classification of structural damage. Our approach here is based on using HMMs for modeling the time-frequency features extracted from time-varying structural data. Unlike conventional deterministic methods, the HMM is a stochastic approach which better accounts for the uncertainties encountered in the structural problem and leads to a more robust health monitoring system. The utility of the proposed approach is demonstrated via example results for the classification of fastener damage in an aluminum plate.

Paper Details

Date Published: 18 April 2007
PDF: 9 pages
Proc. SPIE 6523, Modeling, Signal Processing, and Control for Smart Structures 2007, 652311 (18 April 2007);
Show Author Affiliations
Wenfan Zhou, Arizona State Univ. (United States)
Narayan Kovvali, Arizona State Univ. (United States)
Antonia Papandreou-Suppappola, Arizona State Univ. (United States)
Douglas Cochran, Arizona State Univ. (United States)
Aditi Chattopadhyay, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 6523:
Modeling, Signal Processing, and Control for Smart Structures 2007
Douglas K. Lindner, Editor(s)

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