Publications Scientifiques

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Author: search.filters.author.Attouri, Khadija

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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.
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    Enhanced Neural Network Method-Based Multiscale PCA for Fault Diagnosis: Application to Grid-Connected PV Systems
    (MDPI, 2023) Attouri, Khadija; Mansouri, Majdi; Hajji, Mansour; Kouadri, Abdelmalek; Bouzrara, Kais; Nounou, Hazem
    In this work, an effective Fault Detection and Diagnosis (FDD) strategy designed to increase the performance and accuracy of fault diagnosis in grid-connected photovoltaic (GCPV) systems is developed. The evolved approach is threefold: first, a pre-processing of the training dataset is applied using a multiscale scheme that decomposes the data at multiple scales using high-pass/low-pass filters to separate the noise from the informative attributes and prevent the stochastic samples. Second, a principal component analysis (PCA) technique is applied to the newly obtained data to select, extract, and preserve only the more relevant, informative, and uncorrelated attributes; and finally, to distinguish between the diverse conditions, the extracted attributes are utilized to train the NNs classifiers. In this study, an effort is made to take into consideration all potential and frequent faults that might occur in PV systems. Thus, twenty-one faulty scenarios (line-to-line, line-to-ground, connectivity faults, and faults that can affect the normal operation of the bay-pass diodes) have been introduced and treated at different levels and locations; each scenario comprises various and diverse conditions, including the occurrence of simple faults in the 𝑃𝑉1 array, simple faults in the 𝑃𝑉2 array, multiple faults in 𝑃𝑉1, multiple faults in 𝑃𝑉2, and mixed faults in both PV arrays, in order to ensure a complete and global analysis, thereby reducing the loss of generated energy and maintaining the reliability and efficiency of such systems. The obtained outcomes demonstrate that the proposed approach not only achieves good accuracies but also reduces runtimes during the diagnosis process by avoiding noisy and stochastic data, thereby removing irrelevant and correlated samples from the original dataset.
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    Improved fault detection based on kernel PCA for monitoring industrial applications
    (Elsevier, 2024) Attouri, Khadija; Mansouri, Majdi; Hajji, Mansour; Kouadri, Abdelmalek; Bensmail, Abderrazak; Bouzrara, Kais; Nounou, Hazem
    The conventional Kernel Principal Component Analysis (KPCA) -based fault detection technique requires more computation time and memory storage space to analyze large-sized datasets. In this context, two techniques, Spectral Clustering (SpC) and Random Sampling (RnS), are developed to reduce the dataset size by retaining the more relevant observations while preserving the main statistical characteristics of the original dataset. These two techniques and others use the training dataset from two different industrial processes, Tennessee Eastman (TEP) and Cement Plant (CP) to be reduced and provided to build the Reduced KPCA (RKPCA) model-based fault detection scheme. The obtained results show the effectiveness of the proposed techniques in terms of some fault detection performance indices and computation costs.
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    Wind power converter fault diagnosis using reduced kernel PCA-Based BiLSTM
    (MDPI, 2023) Attouri, Khadija; Mansouri, Majdi; Hajji, Mansour; Kouadri, Abdelmalek; Bouzrara, Kais; Nounou, Hazem
    In this paper, we present a novel and effective fault detection and diagnosis (FDD) method for a wind energy converter (WEC) system with a nominal power of 15 KW, which is designed to significantly reduce the complexity and computation time and possibly increase the accuracy of fault diagnosis. This strategy involves three significant steps: first, a size reduction procedure is applied to the training dataset, which uses hierarchical K-means clustering and Euclidean distance schemes; second, both significantly reduced training datasets are utilized by the KPCA technique to extract and select the most sensitive and significant features; and finally, in order to distinguish between the diverse WEC system operating modes, the selected features are used to train a bidirectional long-short-term memory classifier (BiLSTM). In this study, various fault scenarios (short-circuit (SC) faults and open-circuit (OC) faults) were injected, and each scenario comprised different cases (simple, multiple, and mixed faults) on different sides and locations (generator-side converter and grid-side converter) to ensure a comprehensive and global evaluation. The obtained results show that the proposed strategy for FDD via both applied dataset size reduction methods not only improves the accuracy but also provides an efficient reduction in computation time and storage space
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    Faults classification in Grid-Connected photovoltaic systems
    (IEEE, 2021) Attouri, Khadija; Hajji, Mansour; Mansouri, Majdi; Nounou, Hazem; Kouadri, Abdelmalek; Bouzrara, Kais
    Fault detection and diagnosis (FDD) for Grid-Connected Photovoltaic (GCPV) systems have been received an important measure for improving the operation of these systems. Therefore, in this paper, an enhanced FDD approach, so-called principal component analysis (PCA)-based on a Kullback-Leibler Divergence (KLD), aims to provide the reliability and safety of the overall GCPV system is proposed. The developed approach merges the benefits of PCA model and KLD metric. Firstly, the GCPV features are extracted using PCA model. Secondly, the extracted features are fed to KLD metric for classification purposes. The obtained results confirm the high accuracy of the developed technique. The proposed approach showed superior effectiveness and robustness in process fault diagnosis