Publications Scientifiques

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Subject: search.filters.subject.Robustness

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    Real-Time Fault Detection and Diagnosis Method for Industrial Chemical Tennessee Eastman Process
    (Institute of Electrical and Electronics Engineers Inc., 2024) Attouri, Khadija; Mansouri, Majdi; Hajji, Mansour; Kouadri, Abdelmalek; Bouzrara, Kais; Nounou, Hazem
    The accurate detection and diagnosis of faults are critical for maintaining optimal operation and ensuring the reliability of industrial processes. Notably, the topic of online fault detection and diagnosis has recently presented a significant challenge. This work mainly deploys a neural network technique for the comprehensive detection and diagnosis of faults within the Tennessee Eastman Process (TEP) on a low-computational power system, the Raspberry Pi board. The devolved methodology showcases a remarkable level of accuracy (94.50%) in diagnosing the various TEP faults, affirming its robustness and effectiveness. To elevate the practical applicability of the proposed approach, a meticulous investigation into the implementation of the suggested approach on a Raspberry Pi 4 card was undertaken. The successful realization of this implementation not only highlights the adaptability of the approach but also paves the way for its seamless integration into practical industrial applications.
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    Real-Time Fault Detection Scheme for Industrial Chemical Tennessee Eastman Process
    (Institute of Electrical and Electronics Engineers Inc., 2024) Attouri, Khadija; Mansouri, Majdi; Hajji, Mansour; Kouadri, Abdelmalek; Bouzrara, Kais; Nounou, Hazem
    The key idea behind this study is to integrate a moving window dynamic PCA (MW-DPCA) methodology for fault detection within the Tennessee Eastman process (TEP) into a low-computational power system, the Raspberry Pi 4 card, for real-time application. Indeed, the paramount importance of real-time fault detection (FD) in intricate industrial processes presents a critical challenge. Various data-driven techniques have been developed to ensure safety, maintain operational stability, and optimize productivity in such processes. Principal Component Analysis (PCA) is a fundamental data-driven technique that utilizes dimensionality reduction to extract the most informative features from high-dimensional data, simplifying analysis and potentially revealing underlying fault patterns. However, PCA primarily focuses on static relationships and may miss crucial temporal dynamics for fault identification. This is where dynamic PCA (DPCA) excels. By incorporating lagged values of variables, DPCA captures the temporal evolution of features, enabling a more comprehensive understanding of process behavior and improving the detection of faults involving dynamic changes. In order to address the stochastic measurements, a moving average filter tool is also employed. The results obtained and the successful realization of this implementation demonstrate the adaptability of the approach and pave the way for its seamless integration into practical industrial applications.
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    Robustness Enhancement of Fractionalized Order Proportional Integral Controller for Speed Control of Indirect Field-Oriented Control Induction Motor
    (Wydawnictwo SIGMA-NOT, 2024) Ousaadi, Zahira; Akroum, Hamza; Idir, Abdelhakim
    This article presents a novel approach for controlling an induction motor (IM) drive using a fractionalized order proportional integral (FrOPI) controller within an indirect field-oriented control (IFOC) scheme. In contrast to the conventional Integer Order PI controllers (IOPI), the FrOPI controllers demonstrate enhanced performance owing to their nonlinear characteristics and the inherent iso-damping property of fractional-order operators. The performance of the induction motor is thoroughly assessed under various conditions, including starting, running, speed reversal, and sudden changes in load torque. Simulation results are then presented to confirm the effectiveness of the induction motor drive when utilizing the FrOPI controller
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    A comparative study between fractionalized and fractional order PID controllers for control of a stable system based on particle swarm optimization algorithm
    (Wydawnictwo SIGMA-NOT, 2023) Idir, Abdelhakim; Berrabah, Fouad; Laurent, Canale
    Most industrial applications use integer-order proportional integral derivative (IOPID) controllers due to well-known characteristics such as simplicity and ease of implementation. However, because of their nonlinear nature and the underlying iso-damping feature of fractional-order operators, fractional-order PID (FOPID) and fractionalized-order PID (FrOPID) controllers outperform the IOPID controllers. In this study, three different controllers based on particle swarm optimization are used to regulate a stable system. While a FrOPID controller only has to optimize four parameters and a normal PID controller only needs to optimize three parameters, a FOPID controller requires the optimization of five parameters. Set-point tracking, and better disturbance rejection are obtained with the fractional PID controller, whereas fractionalized PID outperforms the other controllers in terms of noise attenuation
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    Robust block roots relocation via MIMO compensator design
    (IEEE, 2017) Bekhiti, Belkacem; Dahimene, Abdelhakim; Nail, Bachir; Hariche, Kamel