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A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring

Autor
Tibaduiza, D.A.; Torres-Arredondo, M.A.; Mujica, L.E.; Rodellar, J.; Fritzen, C.P
Tipus d'activitat
Article en revista
Revista
Mechanical systems and signal processing
Data de publicació
2013-12-06
Volum
41
Número
1-2
Pàgina inicial
467
Pàgina final
484
DOI
https://doi.org/10.1016/j.ymssp.2013.05.020 Obrir en finestra nova
Projecte finançador
Estructuras inteligentes: sistemas de monitorización e identificación de daños con aplicación en aeronáutica y en plantas eólicas
URL
http://www.sciencedirect.com/science/article/pii/S0888327013002793 Obrir en finestra nova
Resum
This article is concerned with the practical use of Multiway Principal Component Analysis (MPCA), Discrete Wavelet Transform (DWT), Squared Prediction Error (SPE) measures and Self-Organizing Maps (SOM) to detect and classify damages in mechanical structures. The formalism is based on a distributed piezoelectric active sensor network for the excitation and detection of structural dynamic responses. Statistical models are built using PCA when the structure is known to be healthy either directly f...
Paraules clau
Active Ultrasonic Guided-wave Testing, Discrete Wavelet Transform, Principal Component Analysis, Self-organizing Maps, Structural Health Monitoring
Grup de recerca
CoDAlab - Control, Modelització, Identificació i Aplicacions

Participants