TY - JOUR
T1 - On the use of hidden markov modeling and time-frequency features for damage classification in composite structures
AU - Wenfan Zhou, Zhou
AU - Kovvali, Narayan
AU - Reynolds, Whitney
AU - Papandreou-Suppappola, Antonia
AU - Chattopadhyay, Aditi
AU - Cochran, Douglas
PY - 2009/7
Y1 - 2009/7
N2 - 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.
AB - 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.
KW - Integrated vehicle health management
KW - composite structures
KW - damage classification
KW - damage detection
KW - hidden Markov model
KW - matching pursuit decomposition
KW - sensor fusion.
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U2 - 10.1177/1045389X08099968
DO - 10.1177/1045389X08099968
M3 - Article
AN - SCOPUS:67650224963
SN - 1045-389X
VL - 20
SP - 1271
EP - 1288
JO - Journal of Intelligent Material Systems and Structures
JF - Journal of Intelligent Material Systems and Structures
IS - 11
ER -