Browsing by Author "Kaib, Mohammed Tahar Habib"

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Improvement of kernel principal component analysis-based approach for nonlinear process monitoring by data set size reduction using class interval
(Institute of Electrical and Electronics Engineers Inc, 2024) 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 data-driven FDD methods is the Principal Component Analysis (PCA). Unfortunately, PCA's reliability drops when data has nonlinear characteristics as industrial processes. Kernel Principal Component Analysis (KPCA) is an alternative PCA technique that is used to deal with a similar data set. For a large-sized data set, KPCA's execution time and occupied storage space will increase drastically and the monitoring performance can also be affected in this case. So, the Reduced KPCA (RKPCA) was introduced with the aim of reducing the size of a given training data set to lower the execution time and occupied storage space while maintaining KPCA's monitoring performance for nonlinear systems. Generally, RKPCA reduces the number of samples in the training data set and then builds the KPCA model based on this data set. In this paper, the proposed algorithm selects relevant observations from the original data set by utilizing a class interval technique (i.e. histogram) to maintain a bunch of representative samples from each bin. The proposed algorithm has been tested on three tank system pilot plant and Ain El Kebira Cement rotary kiln process. The proposed algorithm has successfully maintained homogeneity to the original data set, reduced the execution time and occupied storage space, and led to decent monitoring performance.
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.
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......
RKPCA-based approach for fault detection in large scale systems using variogram method
(Elsevier, 2022) 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. However, PCA performs poorly if the data used has nonlinear characteristics where this type of data is widely present in most industrial processes. To overcome this drawback, Kernel PCA (KPCA) is an alternative technique used to work on this type of data but it requires more computation time and memory storage space for large-sized data sets. Many size reduction techniques have been developed to select the most relevant observations that will be employed by KPCA. This, known as Reduced KPCA (RKPCA), consequently requires less computation time and memory storage space than KPCA. Besides, it possesses the advantages of both KPCA and standard PCA. In this paper, a reduction in the size of a data set based on a multivariate variogram is proposed. According to its conventional formalism, the uncorrelated observations are selected and kept to form a reduced training data set. Afterward, the KPCA model is built through this data set for faults detection purposes. The proposed RKPCA scheme is tested using an actual involuntary process fault and various simulated sensor faults in a cement plant. Compared to other RKPCA techniques, the developed one yields better results
Speed self-tuning adaptive control of DC motor
(2019) Messilem, Med Abdelmouamin; Kaib, Mohammed Tahar Habib; Ouadi, Abderrahmane (supervisor)
This thesis presents a self tuning pole placement adaptive controller which updates its parameters automatically in order to conserve the desired characteristics of the controlled system. PMDC Motor is used as plant (process) response of this adaptive controller. Two methods are used for offline system identification (Pattern Search Genetic Algorithm, Non-Linear Least Squares Estimation), data acquisition (NI-PCI 6221-37pin) is used to acquire data that will be used to identify the parameter of the motor (armature resistance, inductance, and inertia), armature current, input voltage and rotational speed of the PMDC Motor are the data acquired and used in system identification. DAQ tool box in MTLAB/SIMULINK is used to perform the analysis on the data acquired based on Non-Linear Least Squares Estimation and Pattern Search Genetic Algorithm. PID controller is designed based on the model estimated, and is implemented and tested using the motor in order to validate the obtained model. The Recursive Least Squares Estimator block in SIMULINK is used to perform the online estimation. The controller is implemented and simulated in SIMULINK (SIMULINK Desktop Real-Time), the behavior of the PMDC motor is close to the desired response. Output response of the PMDC Motor can be changed due to a perturbation, environment changes or parameters changes. STC is able to track the system (plant or process) for maintaining the desired behavior.