Doctorat
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Item Interval-valued statistical approaches for process monitoring(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Louifi, Abdelhalim; Harkat, Mohamed Faouzi(Directeur de thèse)Various data-driven approaches, such as Principal Component Analysis (PCA), are widely employed for process monitoring in industrial applications, particularly for detecting abnormal events. PCA-based Fault Detection and Isolation is a well-established strategy, praised for its robust performance. However, its reliability diminishes in uncertain systems where model uncertainties signi?cantly impact e ectiveness. To address this challenge, process modeling is conducted using PCA for interval-valued data, incorporating uncertainties directly into the modeling phase. Four of the most prominent methods for interval-valued PCA are detailed, alongside an extension of conventional PCAbased statistical process monitoring to handle interval-valued data. Over the past decade, this approach has garnered substantial research attention, leading to the development of multiple interval-valued PCA models. This thesis proposes a novel approach called Interval-Valued Principal Component Analysis (IV-PCA), designed to handle uncertainties by de?ning a safe interval for data ?uctuations. The developed technique is applied to the cement rotary kiln process and the Tennessee Eastman Process, where its performance is compared against conventional PCA and four leading Interval-Valued Data PCA (IVD-PCA) methods. Through tests involving actual involuntary system faults and various sensor faults, the IV-PCA demonstrates superior performance in accurately and quickly detecting distinct faults, even in stochastic environments with unknown and uncontrolled uncertainties. The results show signi?cant reductions in false alarms and missed detections compared to the best outcomes of the studied methodsItem Interval-valued statistical approaches for process monitoring(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Louifi, Abdelhalim; Harkat, Mohamed Faouzi(Directeur de thèse)Various data-driven approaches, such as Principal Component Analysis (PCA), are widely employed for process monitoring in industrial applications, particularly for detecting abnormal events. PCA-based Fault Detection and Isolation is a well-established strategy, praised for its robust performance. However, its reliability diminishes in uncertain systems where model uncertainties signi?cantly impact e ectiveness. To address this challenge, process modeling is conducted using PCA for interval-valued data, incorporating uncertainties directly into the modeling phase. Four of the most prominent methods for interval-valued PCA are detailed, alongside an extension of conventional PCAbased statistical process monitoring to handle interval-valued data. Over the past decade, this approach has garnered substantial research attention, leading to the development of multiple interval-valued PCA models. This thesis proposes a novel approach called Interval-Valued Principal Component Analysis (IV-PCA), designed to handle uncertainties by de?ning a safe interval for data ?uctuations. The developed technique is applied to the cement rotary kiln process and the Tennessee Eastman Process, where its performance is compared against conventional PCA and four leading Interval-Valued Data PCA (IVD-PCA) methods. Through tests involving actual involuntary system faults and various sensor faults, the IV-PCA demonstrates superior performance in accurately and quickly detecting distinct faults, even in stochastic environments with unknown and uncontrolled uncertainties. The results show signi?cant reductions in false alarms and missed detections compared to the best outcomes of the studied methodsItem Process fault detection and isolation based on symbolic data or interval-valued principal component analysis(Université M'Hamed Bougara : Institut de génie électrique et électronique, 2021) Rouani, Lahcene; Harkat, Mohamed Faouzi(Directeur de thèse)Principal component analysis (PCA) is a well-known data-driven method that has extensively been used as a fault detection technique for the last three decades. Aside from the non-linearity property, processes today are associated with measurement uncertainties and dynamic properties. The standard PCA method cannot acknowledge these uncertainty and/or dynamic features, let alone incorporate them into the fault detection model. The dynamic PCA has been proposed in the literature to take care of the dynamic properties of real processes in an effort to build a robust fault detection model. On the other end of the spectrum, multiple variants of PCA methods have been developed for interval-valued data. Since interval data, which are a type of symbolic data, are capable of modeling measurement errors and uncertainties, these proposed interval PCA methods prove helpful for modeling systems with sensor imprecision and uncertainties. Still, they cannot handle dynamic properties as the dynamic PCA method did. In this thesis, different interval PCA methods have been investigated to detect faults in real processes. Being capable of acknowledging measurement uncertainties, these interval PCA methods produce better performance than their classical counterpart. Three of these interval PCA methods have been extended to include dynamic properties—a treat that existing methods in the literature did not accomplish. Included in this manuscript is an extension of the combined index to the intervalvalued case where it has been implemented and tested with common interval-valued PCA methods. Moreover, the contribution plot isolation method has also been extended to the interval-valued case for the purpose of isolating faulty variables when using interval PCA methods. Real data from a cement plant and a grid-connected photovoltaic system have been used to apply and test the proposed techniques