Publications Scientifiques
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Item Enhancing Fault Detection in Stochastic Environments Using Interval-Valued KPCA: A Cement Rotary Kiln Case Study(Institute of Electrical and Electronics, 2025) Louifi, Abdelhalim; Kouadri, Abdelmalek; Harkat, Mohamed-Faouzi; Bensmail, Abderazak; Mansouri, MajdiFault detection in industrial processes is challenging due to significant data uncertainty, which complicates the accurate modeling of interval-valued data and the quantification of errors necessary for reliable detection. Existing approaches, such as kernel principal component analysis (KPCA), struggle with these challenges because they rely on single-valued data representations and are unable to effectively handle interval-based variability. To address these limitations, this paper introduces the interval-valued model KPCA (IV-KPCA), which extends KPCA by redefining similarity measures and kernel functions to accommodate interval-valued uncertainty. IV-KPCA preserves the interval structure throughout the modeling process, enhancing robustness to dynamic uncertainties and improving fault detection in complex nonlinear systems. Within this framework, fault detection statistics (T 2 , Q, and 8) are developed to enable precise error quantification. The proposed method is validated on a cement rotary kiln process, a highly stochastic industrial system characterized by significant uncertainties. Experimental results demonstrate that IV-KPCA reduces false alarms, missed detections, and detection delays by over 100%, 90%, and 95%, respectively, compared to traditional methods. These findings underscore the potential of IV-KPCA in enhancing fault detection performance in complex, uncertain environmentsItem 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, AbdelmalekThis 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 WECSItem Deep Learning-Based Fish Health Monitoring and Diagnosis: A Review(IEEE, 2025) Kheriji, Lazhar; Kouadri, Abdelmalek; Mansouri,MajdiFish in aquaculture systems face health challenges influenced by aging, water quality, and environmental conditions. These issues affect critical components like feeding and filtration, potentially reducing efficiency and causing system failure. Effective Health Monitoring and Diagnosis (HMD) relies on high-quality features such as behavior, physical condition, feeding habits, and water parameters. However, traditional hand-crafted approaches often fail to capture the complex and nonlinear interactions between biological and environmental factors, limiting their adaptability to sudden changes in water conditions or disease outbreaks. This gap motivates the use of intelligent, multimodal learning strategies that integrate diverse data sources for more robust and reliable analysis. Advances in computing power, large datasets, and sophisticated algorithms have made deep learning (DL) a transformative tool in this field. By combining DL with multimodal data integration, it becomes possible to learn high-level representations directly from heterogeneous inputs such as water quality measures, behavioral signals, and visual observations, thereby overcoming the limitations of conventional feature-based methods. This paper reviews DL-based multimodal approaches in aquaculture HMD, comparing recent techniques, their strengths, and limitations. We also discuss future directions, emphasizing multimodal data fusion to enhance DL-driven health monitoring. This review provides a concise resource for researchers and practitioners aiming to advance aquaculture health monitoring.Item Deep Learning for Sustainable Aquaculture: Opportunities and Challenges(Institute of Electrical and Electronics Engineers Inc., 2025) Kheriji, Lazhar; Kouadri, Abdelmalek; Mansouri, MajdiWith the rising global demand for aquatic products, aquaculture has become a cornerstone of food security and sustainability. This review comprehensively analyzes the application of deep learning in sustainable aquaculture, covering key areas such as fish detection and counting, growth prediction and health monitoring, intelligent feeding systems, water quality forecasting, and behavioral and stress analysis. The study discusses the suitability of deep learning architectures, including CNNs, RNNs, GANs, Transformers, and MobileNet, under complex aquatic environments characterized by poor image quality and severe occlusion. It highlights ongoing challenges related to data scarcity, real-time performance, model generalization, and cross-domain adaptability. Looking forward, the paper outlines future research directions including multimodal data fusion, edge computing, lightweight model design, synthetic data generation, and digital twin-based virtual farming platforms. Deep learning is poised to drive aquaculture toward greater intelligence, efficiency, and sustainabilityItem Sensor Fault Detection in Uncertain Large-Scale Systems Using Interval-Valued PCA Technique(IEEE, 2025) Louifi, Abdelhalim; Kouadri, Abdelmalek; Harkat, Mohamed-FaouziPrincipal component analysis (PCA)-based fault detection and diagnosis (FDD) is a well-established, data- driven method that has shown remarkable performance. Despite the excellent reputation of the PCA, it is not an opti- mal solution, mainly due to the effect of system parameters’ uncertainties and imprecise measurements. These drasti- cally affect the decision-making concerning the operating state of the process. In this article, the data collected by different sensors are transformed from a single value to an interval value form by which errors and uncertainties in the measurements are quantified satisfactorily. Then, the process modeling based on the PCA technique has been duly performed for interval-valued. Afterward, the well-known fault detection statistics T 2 , Q, and 8 are obtained under an interval-valued representation. The developed technique is tested in the cement rotary kiln process. Its performance in terms of false and missed alarms and detection delay is compared with that of other techniques through an actual involuntary system fault and other different types of sensor faults. The obtained results show high superiority in detecting accurately and quickly distinct faults in a stochastic environment, including unknown and uncontrolled uncertainties. Consequently, the results have been reduced by more than 33%, 85%, and 45% for T 2 , Q, and 8, respectively, compared with the best results of the studied methods.Item 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, HazemThe 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.Item Uncertainty Quantification Kernel PCA: Enhancing Fault Detection in Interval-Valued Data(Institute of Electrical and Electronics Engineers Inc., 2024) Louifi, Abdelhalim; Kouadri, Abdelmalek; Harkat, Mohamed Faouzi; Bensmail, Abderazak; Mansouri, Majdi; Nounou, HazemThe interval-valued kernel PCA (UQ-KPCA) is a variation of the kernel PCA (KPCA) designed for interval-valued data, designed to handle data uncertainty by defining specific similarity measures and kernel functions for interval data. This paper introduces Uncertainty Quantification KPCA (UQ-KPCA) as a novel method to address uncertainties in data. UQ-KPCA converts the traditional KPCA model from single-valued to interval-valued representations, allowing for accurate error and uncertainty quantification. The process modeling using KPCA is then performed on data based on the interval model, followed by the computation of fault detection statistics such as T 2 , Q, and Φ. The method’s effectiveness is evaluated in the context of the cement rotary kiln process, and compared with the KPCA demonstrating superior performance in accurately identifying faults within a stochastic setting with unknown uncertainties.Item 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, HazemThe 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.Item Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic Systems using Deep GRU-based Bayesian optimization(Elsevier B.V., 2024) Yahyaoui, Zahra; Hajji, Mansour; Mansouri, Majdi; Kouadri, Abdelmalek; Bouzrara, Kais; Nounou, HazemThe efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.Item Kernel Principal Component Analysis Improvement based on Data-Reduction via Class Interval(Elsevier B.V., 2024) Habib Kaib, Mohammed Tahar; Kouadri, Abdelmalek; Harkat, Mohamed Faouzi; Bensmail, Abderazak; Mansouri, Majdi; Nounou, MohamedKernel Principal Component Analysis (KPCA) is an effective nonlinear extension of the Principal Component Analysis for fault detection. For large-sized data, KPCA may drop its detection performance, occupy more storage space for the monitoring model, and take more execution time in the online part. Reduced KPCA pre-processes the training data before applying the KPCA method, the proposed approach selects samples based on class interval to reduce the number of observations in the training data set while maintaining decent detection performance. This approach is applied to the Tennessee Eastman Process and then compared to some of the existing approaches.