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Detection of structural changes through principal component analysis and multivariate statistical inference

Autor
Pozo, F.; Arruga, I.; Mujica, L.E.; Ruiz, M.; Podivilova, E.
Tipus d'activitat
Article en revista
Revista
Structural health monitoring: an international journal
Data de publicació
2016-01-27
DOI
https://doi.org/10.1177/1475921715624504 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/87419 Obrir en finestra nova
http://shm.sagepub.com/content/early/2016/01/26/1475921715624504.abstract Obrir en finestra nova
URL
http://shm.sagepub.com/ Obrir en finestra nova
Resum
This article introduces a new methodology for the detection of structural changes using a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. The three main features that characterize the proposed methodology are (a) the nature of the data used in the test since vectors of principal component analysis projections are used instead of the entire measured response of the structure or the coefficients of an AutoRegres...
Citació
Pozo, F., Arruga, I., Mujica, L.E., Ruiz, M., Podivilova, E. Detection of structural changes through principal component analysis and multivariate statistical inference. "Structural health monitoring: an international journal", 27 Gener 2016.
Paraules clau
Damage detection, multivariate hypothesis testing, principal component analysis, structural health monitoring.
Grup de recerca
CoDAlab - Control, Modelització, Identificació i Aplicacions

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