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Damage classification structural health monitoring in bolted structures using time-frequency techniques
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.
Damage classification structural health monitoring in bolted structures using time-frequency techniques
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.
Damage classification structural health monitoring in bolted structures using time-frequency techniques
Chakraborty, Debejyo (Autor:in) / Kovvali, Narayan (Autor:in) / Wei, Jun (Autor:in) / Papandreou-Suppappola, Antonia (Autor:in) / Cochran, Douglas (Autor:in) / Chattopadhyay, Aditi (Autor:in)
Journal of Intelligent Material Systems and Structures ; 20 ; 1289-1305
2009
17 Seiten, 36 Quellen
Aufsatz (Zeitschrift)
Englisch
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