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Electro-mechanical admittance-based damage detection using extreme value statistics

Providakis Konstantinos

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/0CF85495-8DC2-4C5B-A9FA-1B7B6D46E6B4-
Αναγνωριστικόhttps://doi.org/ 10.4028/www.scientific.net/KEM.385-387.561-
Γλώσσαen-
Μέγεθος4 pagesen
ΤίτλοςElectro-mechanical admittance-based damage detection using extreme value statisticsen
ΔημιουργόςProvidakis Konstantinosen
ΔημιουργόςΠροβιδακης Κωνσταντινοςel
ΠερίληψηThis paper presents the use of statistically rigorous algorithms combined with electromechanical (E/M) impedance approach for health monitoring of engineering structures. In particular, a statistical pattern recognition procedure is developed, based on frequency domain data of electromechanical impedance, to establish a decision boundary for damage identification. In order to diagnose damage with statistical confidence, health monitoring is cast in the context of outlier detection framework. Inappropriate modeling of tail distribution of outliers imposes potentially misleading behavior associated with damage. The present paper attempts to address the problem of establishing decision boundaries based on extreme value statistics so that the extreme values of outliers associated with tail distribution can be properly modeled. The validity of the proposed method is demonstrated using finite element method (FEM) simulated data while a comparison is performed for the extreme value analysis results contrasted with the standard approach where it is assumed that the damage-sensitive features are normally distributed.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-18-
Ημερομηνία Δημοσίευσης2008-
Θεματική ΚατηγορίαEngineering, Structuralen
Θεματική ΚατηγορίαStructures, Engineering ofen
Θεματική Κατηγορίαstructural engineeringen
Θεματική Κατηγορίαengineering structuralen
Θεματική Κατηγορίαstructures engineering ofen
Βιβλιογραφική ΑναφοράC. Providakis, “Electro-mechanical admittance-based damage detection using extreme value statistics,"in 2008 7th Int. Conf. on Fracture and Damage Mechanics ,pp.561-564.doi:10.4028/www.scientific.net/KEM.385-387.561en

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