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Please use this identifier to cite or link to this item: http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/14339

Titre: Kernel Principal Component Analysis Improvement based on Data-Reduction via Class Interval
Auteur(s): Habib Kaib, Mohammed Tahar
Kouadri, Abdelmalek
Harkat, Mohamed Faouzi
Bensmail, Abderazak
Mansouri, Majdi
Nounou, Mohamed
Mots-clés: Data-driven techniques
Fault Detection (FD)
Histogram
Kernel Principal Component Analysis (KPCA)
Principal Component Analysis (PCA)
Tennessee Eastman Process
Issue Date: 2024
Editeur: Elsevier B.V.
Collection/Numéro: IFAC-PapersOnLine /Vol. 58, N° 4(2024). 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2024;pp. 390 - 395
Résumé: Kernel Principal Component Analysis (KPCA) is an effective nonlinear extension of the Principal Component Analysis for fault detection. For large-sized data, KPCA may drop its detection performance, occupy more storage space for the monitoring model, and take more execution time in the online part. Reduced KPCA pre-processes the training data before applying the KPCA method, the proposed approach selects samples based on class interval to reduce the number of observations in the training data set while maintaining decent detection performance. This approach is applied to the Tennessee Eastman Process and then compared to some of the existing approaches.
URI: https://www.sciencedirect.com/science/article/pii/S2405896324003331?via%3Dihub
https://doi.org/10.1016/j.ifacol.2024.07.249
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/14339
ISSN: 2405-8963
Appears in Collections:Communications Internationales

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