Search Results - "Bensmail, Abderazak"

Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval by Kaib, Mohammed Tahar Habib, Kouadri, Abdelmalek, Harkat, Mohamed-Faouzi, Bensmail, Abderazak, Mansouri, Majdi

“…Fault detection and diagnosis (FDD) systems play a crucial role in maintaining the adequate execution of the monitored process. One of the widely used…”
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Fault detection and diagnosis in a cement rotary kiln using PCA with EWMA-based adaptive threshold monitoring scheme by Bakdi, Azzeddine, Kouadri, Abdelmalek, Bensmail, Abderazak

“…This paper presents main results of fault detection and diagnosis in a cement manufacturing plant using a new monitoring scheme. The scheme is based on…”
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A Modified Moving Window dynamic PCA with Fuzzy Logic Filter and application to fault detection by Ammiche, Mustapha, Kouadri, Abdelmalek, Bensmail, Abderazak

“…Principal Component Analysis (PCA) model is constructed from measured data and used to monitor new testing samples. In fact, the statistical independency…”
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Multivariate nuisance alarm management in chemical processes by Kaced, Radhia, Kouadri, Abdelmalek, Baiche, Karim, Bensmail, Abderazak

“…Alarm systems are of vital importance in the safe and effective functioning of industrial plants, yet they frequently suffer from too many nuisance alarms…”
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Improving kernel PCA-based algorithm for fault detection in nonlinear industrial process through fractal dimension by 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…”
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RKPCA-based approach for fault detection in large scale systems using variogram method by Kaib, Mohammed Tahar Habib, Kouadri, Abdelmalek, Harkat, Mohamed Faouzi, Bensmail, Abderazak

“…Principal Component Analysis (PCA)-based approach for fault detection is a simple and accurate data-driven technique for feature extraction and selection…”
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Kernelized relative entropy for direct fault detection in industrial rotary kilns by Hamadouche, Anis, Kouadri, Abdelmalek, Bensmail, Abderazak

“…Summary The objective of this work is to use a 1‐dimensional signal that reflects the dissimilarity between multidimensional probability densities for…”
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An adaptive threshold estimation scheme for abrupt changes detection algorithm in a cement rotary kiln by Kouadri, Abdelmalek, Bensmail, Abderazak, Kheldoun, Aissa, Refoufi, Larbi

“…This work deals with a major problem that arises when searching for a reliable, accurate and easily exploitable adaptive threshold based fault detection…”
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Dynamic Interval-Valued PCA for Enhanced Fault Detection by Rouani, Lahcene, Harkat, Mohamed Faouzi, Kouadri, Abdelmalek, 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…”
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Uncertainty Quantification Kernel PCA: Enhancing Fault Detection in Interval-Valued Data by Louifi, Abdelhalim, Kouadri, Abdelmalek, Harkat, Mohamed Faouzi, Bensmail, Abderazak, Mansouri, Majdi, Nounou, Hazem

“…The interval-valued kernel PCA (UQ-KPCA) is a variation of the kernel PCA (KPCA) designed for interval-valued data, designed to handle data uncertainty by…”
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Interval Valued PCA-Based Approach For Fault Detection In Complex Systems by Louifi, Abdelhalim, Louhab, Salah Eddine, Kouadri, Abdelmalek, Rouani, Lahcene, Bensmail, Abderazak, Harkat, Mohamed Faouzi

“…The purpose of this article is to emphasize the importance of detecting process sensor faults using Principal Component Analysis (PCA). In practice,…”
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