Publications Internationales
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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 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, HazemIn 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.Item 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, MajdiPrincipal 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.Item 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, MajdiFault 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.Item Medium-term wind power forecasting using reduced principal component analysis based random forest model(SAGE Publications Inc, 2024) Jamii, Jannet; Trabelsi, Mohamed; Mansouri, Majdi; Kouadri, Abdelmalek; Mimouni, Mohamed Faouzi; Nounou, MohamedDue to its dependence on weather conditions, wind power (WP) forecasting has become a challenge for grid operators. Indeed, the dispatcher needs to predict the WP generation to apply the appropriate energy management strategies. To achieve an accurate WP forecasting, it is important to choose the appropriate input data (weather data). To this end, a medium-term wind power forecasting using reduced principal component analysis (RKPCA) based Random Forest Model is proposed in this paper. Two-stage WP forecasting model is developed. In the first stage, a Kernel Principal Component Analysis (KPCA) and reduced KPCA (RKPCA)-based data pre-processing techniques are applied to select and extract the important input data features (wind speed, wind direction, temperature, pressure, and relative humidity). The main idea behind the RKPCA technique is to use Euclidean distance for reducing the number of observations in the training data set to overcome the problem of computation time and storage costs of the conventional KPCA in the feature extraction phase. In the second stage, a Random Forest (RF) algorithm is proposed to predict the WP for medium-term. To evaluate the performance of the proposed RKPCA-RF technique it has been applied to data extracted from NOAA’S Surface Radiation (SURFRAD) network at Bondville station, located in USA. The presented results show that the proposed RKPCA-RF technique achieved more accurate results than the state-of-the-art methodologies in terms of RMSE (0.09), MAE (0.23), and R2 (0.85). In addition, the proposed technique achieved the lowest overall computation time (CPU).Item Robust fault estimation for wind turbine pitch and drive train systems(Elsevier, 2024) Azizi, Abdesamia; Youssef, Tewfik; Kouadri, Abdelmalek; Mansouri, Majdi; Mimouni, Mohamed FouziThe reliability and accuracy of the wind conversion system largely depend on the early detection and diagnosis of faults. In this paper, a novel fault estimator for wind turbine pitch and drive train systems is developed. The main objective is to estimate actuator and sensor faults along with the system states while mitigating the impact of process disturbances and noises. To accomplish this, an augmented state is created by combining the states of the system and different faults. Subsequently, an Unknown Input Observer (UIO) is developed to estimate them simultaneously. The UIO matrices are obtained by optimizing a multi-objective function formed by transforming states and faults estimation errors into the frequency domain using a genetic algorithm. Compared with other approaches, particularly H∞, the proposed technique shows great superiority in accurately estimating various actuators and sensors faults.