@inproceedings{2dd888241c1f4471a6b9ea95e4952897,
title = "Dynamic Interval-Valued PCA for Enhanced Fault Detection",
abstract = "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.",
keywords = "Dynamic process, Fault detection, Interval-valued data, Process monitoring, principal component analysis (PCA)",
author = "Lahcene Rouani and Harkat, \{Mohamed Faouzi\} and Abdelmalek Kouadri and Abderazak Bensmail and Majdi Mansouri and Mohamed Nounou",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 10th International Conference on Control, Decision and Information Technologies, CoDIT 2024 ; Conference date: 01-07-2024 Through 04-07-2024",
year = "2024",
doi = "10.1109/CoDIT62066.2024.10708428",
language = "English",
isbn = "979-8-3503-7398-1",
series = "International Conference On Control Decision And Information Technologies",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2911--2916",
booktitle = "2024 10th International Conference On Control, Decision And Information Technologies, Codit 2024",
address = "United States",
}