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Data-driven methodology to detect and classify structural changes under temperature variations

Autor
Anaya, M.; Tibuadiza, D.; Torres-Arredondo, M.A.; Pozo, F.; Ruiz, M.; Mujica, L.E.; Rodellar, J.; Fritzen, C.P
Tipus d'activitat
Article en revista
Revista
Smart materials and structures
Data de publicació
2014-04-01
Volum
23
Número
4
Pàgina inicial
045006-1
Pàgina final
045006-21
DOI
https://doi.org/10.1088/0964-1726/23/4/045006 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://iopscience.iop.org/article/10.1088/0964-1726/23/4/045006/meta;jsessionid=D58A555F66325B81CCA2B094C59BE09D.c6.iopscience.cld.iop.org Obrir en finestra nova
Resum
This paper presents a methodology for the detection and classification of structural changes under different temperature scenarios using a statistical data-driven modelling approach by means of a distributed piezoelectric active sensor network at different actuation phases. An initial baseline pattern for each actuation phase for the healthy structure is built by applying multiway principal component analysis (MPCA) to wavelet approximation coefficients calculated using the discrete wavelet tran...
Paraules clau
Damage Classification, Damage Index, Discrete Wavelet Transform (dwt), Principal Component Analysis (pca), Self-organizing Maps (som), Structural Health Monitoring (shm), Temperature Effects, Principal Component Analysis, Self-organizing Map, Health, Performance, Validation, Waves
Grup de recerca
CoDAlab - Control, Modelització, Identificació i Aplicacions

Participants