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Damage detection using robust fuzzy principal component analysis

Author
Gharibnezhad, F.; Mujica, L.E.; Rodellar, J.; Fritzen, C.P
Type of activity
Presentation of work at congresses
Name of edition
6th European Workshop on Structural Health Monitoring
Date of publication
2012
Presentation's date
2012-07
Book of congress proceedings
Proceedings 6th European Workshop on Structural Health Monitoring & 1st European Conference On Prognostics and Health Management, July 3-6, 2012, Dresden, Germany
First page
1
Last page
6
Repository
http://hdl.handle.net/2117/17821 Open in new window
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 tradition...
Citation
Gharibnezhad, F. [et al.]. Damage detection using robust fuzzy principal component analysis. A: European Workshop on Structural Health Monitoring. "Proceedings 6th European Workshop on Structural Health Monitoring". Dresden: 2013, p. 1-6.
Group of research
CoDAlab - Control, Dynamics and Applications

Participants

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