TY - JOUR
T1 - Damage classification structural health monitoring in bolted structures using time-frequency techniques
AU - Chakraborty, Debejyo
AU - Kovvali, Narayan
AU - Wei, Jun
AU - Papandreou-Suppappola, Antonia
AU - Cochran, Douglas
AU - Chattopadhyay, Aditi
PY - 2009/7
Y1 - 2009/7
N2 - The analysis, detection, and classification of damage in complex bolted structures is an important component of structural health monitoring. In this article, an advanced signal processing and classification method is introduced based on time-frequency techniques. The time-varying signals collected from sensors are decomposed into linear combinations of highly localized Gaussian functions using the matching pursuit decomposition algorithm. These functions are chosen from a dictionary of time-frequency shifted and scaled versions of an elementary Gaussian basis function. The dictionary is also modified to use real measured data as the basis elements in order to obtain a more parsimonious signal representation. Classification is then achieved by matching the extracted damage features in the time-frequency plane. To further improve classification performance, the information collected from multiple sensors is integrated using a Bayesian sensor fusion approach. Results are presented demonstrating the algorithm performance for classifying signals obtained from various types of fastener failure damage in an aluminum plate.
AB - The analysis, detection, and classification of damage in complex bolted structures is an important component of structural health monitoring. In this article, an advanced signal processing and classification method is introduced based on time-frequency techniques. The time-varying signals collected from sensors are decomposed into linear combinations of highly localized Gaussian functions using the matching pursuit decomposition algorithm. These functions are chosen from a dictionary of time-frequency shifted and scaled versions of an elementary Gaussian basis function. The dictionary is also modified to use real measured data as the basis elements in order to obtain a more parsimonious signal representation. Classification is then achieved by matching the extracted damage features in the time-frequency plane. To further improve classification performance, the information collected from multiple sensors is integrated using a Bayesian sensor fusion approach. Results are presented demonstrating the algorithm performance for classifying signals obtained from various types of fastener failure damage in an aluminum plate.
KW - Structural health monitoring
KW - damage classification
KW - fastener failure.
KW - matching pursuit decomposition
KW - sensor fusion
KW - time-frequency analysis
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U2 - 10.1177/1045389X08100044
DO - 10.1177/1045389X08100044
M3 - Article
AN - SCOPUS:67650254739
SN - 1045-389X
VL - 20
SP - 1289
EP - 1305
JO - Journal of Intelligent Material Systems and Structures
JF - Journal of Intelligent Material Systems and Structures
IS - 11
ER -