Publications Scientifiques

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Author: search.filters.author.Mansouri, MajdiSubject: search.filters.subject.Principal component analysis (PCA)

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    Dynamic Interval-Valued PCA for Enhanced Fault Detection
    (Institute of Electrical and Electronics Engineers Inc., 2024) Rouani, Lahcene Rouani; Harkat, Mohamed Faouzi Harkat; Kouadri, Abdelmalek Kouadri; Bensmail, Abderazak; Mansouri, Majdi; Nounou, Mohamed
    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.
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    Improving kernel PCA-based algorithm for fault detection in nonlinear industrial process through fractal dimension
    (Institution of Chemical Engineers, 2023) Kaib, Mohammed Tahar Habib; Kouadri, Abdelmalek; Harkat, Mohamed Faouzi; Bensmail, Abderazak; Mansouri, Majdi
    Principal Component Analysis (PCA) is a widely used technique for fault detection and diagnosis. PCA works well when the data set has linear characteristics. However, most industrial processes have nonlinear characteristics in their data. Kernel PCA (KPCA) is an alternative solution for such types of data sets. This solution doesn’t come without a cost since one of KPCA’s disadvantages is a large number of observations which results in more occupied storage space and more execution time than the PCA technique. Furthermore, if the data is too large it may minimize the monitoring performance of the KPCA model. Reduced KPCA (RKPCA) is a solution for the conventional KPCA limitations. Firstly, RKPCA can deal with nonlinear characteristics without crucial problems because it is based on the KPCA algorithm with a data reduction part where it keeps most of the data’s infor- mation. Thus, by reducing the number of observations RKPCA reduces the occupied storage space and execution time while preserving tolerable monitoring performance. The proposed RKPCA algorithm consists of two parts. First, the large-sized training data set is reduced using the fractal dimension technique (correlation dimension). Afterward, the KPCA model is developed through the obtained reduced training data set. The proposed scheme is applied to the Tennessee Eastman Process and the Cement Plant Rotary Kiln data sets to evaluate its performance in comparison with other algorithms.
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    Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems
    (Elsevier, 2020) Hajji, Mansour; Harkat, Mohamed-Faouzi; Kouadri, Abdelmalek; Abodayeh, Kamaleldin; Mansouri, Majdi; Nounou, Hazem; Nounou, Mohamed