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On the Use of Hidden Markov Modeling and Time-frequency Features for Damage Classification in Composite Structures

¹ Department of Electrical Engineering, Arizona State University, Tempe, Arizona, USA
² Department of Mechanical and Aerospace Engineering, Arizona State University, Tempe, Arizona, USA

^* To whom correspondence should be addressed.

	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.

Key Words: Integrated vehicle health management, composite structures, damage detection, damage classification, matching pursuit decomposition, hidden Markov model, sensor fusion.

First published on January 27, 2009, doi:10.1177/1045389X08099968

Journal of Intelligent Material Systems and Structures 2009;20:1271.

A more recent version of this article appeared on July 1, 2009


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