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AuthorChaabane, Marwa
AuthorBen Hamida, Ahmed
AuthorMansouri, Majdi
AuthorNounou, Hazem N.
AuthorAvci, Onur
Available date2020-08-20T11:44:18Z
Publication Date2017
Publication Name2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings
ResourceScopus
URIhttp://dx.doi.org/10.1109/STA.2016.7952052
URIhttp://hdl.handle.net/10576/15739
AbstractThis paper addresses the problem of damage detection technique of structural health monitoring (SHM). Kernel principal components analysis (KPCA)-based generalized likelihood ratio (GLR) technique is developed to enhance the damage detection of SHM processes. The data are collected from the complex three degree of freedom spring-mass-dashpot system in order to calculate the KPCA model. The developed KPCA-based GLR is the method that attempts to combine the advantages of GLR statistic in the cases where process models are not available and a multivariate statistical process control
AbstractKPCA. The simulations show the improved performance of the KPCA-based GLR damage detection method.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDamage detection
GLR
Kernel PCA
SHM
TitleDamage detection using enhanced multivariate statistical process control technique
TypeConference
Pagination234-238
dc.accessType Abstract Only


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