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Author: search.filters.author.Attouri, KhadijaHas files: No

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    Adaptive Polynomial Kolmogorov–Arnold Networks with Multi-Modal Sensor Data Fusion for High-Precision Fault Diagnosis in Wind Energy Systems
    (Institute of Electrical and Electronics Engineers, 2025) Attouri, Khadija; Mansouri, Majdi; Kouadri, Abdelmalek
    This paper presents a robust multi-modal sensor-based fault diagnosis system for a variable-speed Wind Energy Conversion System (WECS) equipped with a Squirrel Cage Induction Generator (SCIG). The system’s intricate dynamics and the non-linear nature of faults pose significant challenges to accurate and timely detection. To address these challenges, we introduce a novel classifier based on the Adaptive Polynomial Kolmogorov–Arnold Network (AdaptpolyKAN), which employs trainable polynomial basis functions to capture complex signal patterns collected from heterogeneous sensors. A comprehensive dataset was generated, covering three fault types open-circuit, short-circuit, and wear-out artificially injected into the system’s dual-converter topology. The proposed AdaptpolyKAN model is evaluated using two sensor data fusion strategies: Early Fusion, which concatenates features from three distinct sensor modalities (Generator, DC Bus, and Grid), and Late Fusion, which aggregates predictions from separate classifiers. The results demonstrate that the AdaptpolyKAN model, particularly with the Early Fusion strategy, achieves a near-perfect accuracy of 99.97%, outperforming all other benchmark classifiers. In particular, it clearly outperforms the baseline PolyKAN. With Early Fusion, AdaptpolyKAN achieved a near-perfect accuracy of 99.97%, compared to PolyKAN’s 95.03%. Even under Late Fusion, AdaptpolyKAN (96.24%) maintained a substantial margin over PolyKAN (91.99%). This performance gap highlights the superiority of AdaptpolyKAN’s adaptive polynomial basis functions in capturing nonlinear, multimodal interactions, whereas the fixed structure of PolyKAN remains limited. Furthermore, the study confirms that Early Fusion consistently delivers superior performance compared to Late Fusion for this application. These findings underscore the effectiveness of AdaptpolyKAN’s adaptive architecture and the advantages of a holistic multi-sensor fusion approach for high-precision fault diagnosis in WECS
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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.