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Veuillez utiliser cette adresse pour citer ce document : http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/14844

Titre: Dynamic Interval-Valued PCA for Enhanced Fault Detection
Auteur(s): Rouani, Lahcene Rouani
Harkat, Mohamed Faouzi Harkat
Kouadri, Abdelmalek Kouadri
Bensmail, Abderazak
Mansouri, Majdi
Nounou, Mohamed
Mots-clés: Fault detection
Process monitoring
Principal component analysis (PCA)
Interval-valued data
Dynamic process
Date de publication: 2024
Editeur: Institute of Electrical and Electronics Engineers Inc.
Collection/Numéro: " 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), Vallette, Malta, 2024;pp. 2911-2916
Résumé: This study introduces three novel dynamic interval-valued principal component analysis (DIPCA) methods: dynamic centers PCA (D-CPCA), dynamic vertices PCA (D-VPCA), and dynamic complete information PCA (D-CIPCA). These methods advance traditional interval-valued PCA (IPCA) by integrating dynamic aspects of industrial processes, thus addressing both data uncertainties and temporal correlations. The DIPCA methods were validated using real-world data from the Ain El Kebira cement plant. Results indicate significant improvements in fault detection accuracy, achieving lower false alarm rates and higher reliability compared to classical IPCA methods. Furthermore, an enhanced combined index for interval-valued data was developed, providing a single, comprehensive statistical measure for streamlined process monitoring.
URI/URL: 10.1109/CoDIT62066.2024.10708428
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/14844
https://ieeexplore.ieee.org/document/10708428
ISSN: 2576-3555
Collection(s) :Communications Internationales

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