Damage detection using enhanced multivariate statistical process control technique
| Author | Chaabane, Marwa |
| Author | Ben Hamida, Ahmed |
| Author | Mansouri, Majdi |
| Author | Nounou, Hazem N. |
| Author | Avci, Onur |
| Available date | 2020-08-20T11:44:18Z |
| Publication Date | 2017 |
| Publication Name | 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings |
| Resource | Scopus |
| Abstract | This 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 |
| Abstract | KPCA. The simulations show the improved performance of the KPCA-based GLR damage detection method. |
| Language | en |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Subject | Damage detection GLR Kernel PCA SHM |
| Type | Conference Paper |
| Pagination | 234-238 |
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Electrical Engineering [2424 items ]