Doctorat
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Item Multivariate statistical process monitoring using kernel statistical techniques(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Kaib, Mohammed Tahar Habib; Harkat, Mohamed Faouzi(Directeur de thèse)Fault Detection and Diagnosis (FDD) is an important part of industrial plants because monitoring systems are responsible for capturing faults as soon as they occur to avoid major casualties in equipment, operators, and the environment......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 Multivariate statistical process monitoring using kernel principal component analysis(Université M'Hamed Bougara : Institut de génie électrique et électronique, 2022) Bencheikh, Fares; Harkat, Mohamed Faouzi(Directeur de thèse)Fault detection and diagnosis field (FDD) plays an important role in industrial processes. It assures the safe operation of the process and reduces its maintenance costs. The implementation of mechanisms for early detection and diagnosis of faults is called process monitoring. Due to the size and complexity of industrial processes, multivariate statistical methods are finding wide application in process monitoring. Some popular methods are principal component analysis (PCA) for linear processes, and kernel principal component analysis (KPCA) for nonlinear processes. The main challenge in the KPCA based fault detection and diagnosis method is the high computation time and memory storage space whenever the size of the training data increases. The developed kernel matrix size depends on the number of training observations. So, it requires O(n 2 ) storage space for its build and for which O(n ) computation time for its eigendecomposition procedures. In this dissertation, three new methods have been proposed to address the computation drawbacks of KPCA. The first method aims to eliminate the redundant observations among the training dataset based on the Euclidean distances between observations such that any two observations with zero Euclidean distance are considered similar and one of them can be removed. The second method removes the correlated observations and keeps only the representative non-correlated observations to build a reduced training dataset. The third method reduces the training dataset by eliminating the dependent observation guarding only the independent observations. The reduced training datasets are used to build KPCA algorithm to compute the fault indices thresholds in order to fire the alarms when the index violated its threshold. The proposed methods are applied to two case study industrial processes: Ain El Kebira rotary kiln process and Tennessee Eastman process. The obtained results are compared to the ordinary KPCA and different Reduced KPCA (RKPCA) methods; in terms of false alarm rate (FAR), missed detection rate (MDR), and detection time delay (DTD); to evaluate the efficiency of these proposed methods. The proposed RKPCA techniques are able to enhance the time and space computation of KPCA and contribute better monitoring performance