Damage detection using robust fuzzy principal component analysis

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Title:	Damage detection using robust fuzzy principal component analysis
Author:	Gharibnezhad, Fahit; Mujica Delgado, Luis Eduardo; Rodellar Benedé, José; Fritzen, Claus-Peter
Other authors:	Universitat Politècnica de Catalunya. Departament de Matemàtica Aplicada III; Universitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
Abstract:	In this work Robust Fuzzy Principal Component Analysis (RFPCA) is used and compared with comparing with classical Principal Component Analysis (PCA) to detect and classify damages. It has been proved that the RFPCA method achieves better result mainly because it is more compressible than classical PCA and also carries more information, hence not only it can distinguish the healthy structure from the damaged structure much sharper than the traditional counterparts but also in some cases traditional PCA is incapable of discerning the pristine from damaged structure. This work involves experimental results using pipe-like structure powered by a piezoelectric actuators and sensors.
Abstract:	Peer Reviewed
Subject(s):	-Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències -Structural health monitoring -Robust fuzzy principal component analysis
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Document type:	Article - Published version Conference Object
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