Publications Scientifiques
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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 New reduced kernel PCA for fault detection and diagnosis in cement rotary kiln(Elsevier, 2020) Bencheikh, Fares; Harkat, M. F.; Kouadri, A.; Bensmail, A.Fault detection and diagnosis (FDD) based on data-driven techniques play a crucial role in industrial process monitoring. It intends to promptly detect and identify abnormalities and enhance the reliability and safety of the processes. Kernel Principal Component Analysis (KPCA) is a powerful FDD based data-driven method. It has gained much interest due to its ability in monitoring nonlinear systems. However, KPCA suffers from high computing time and large storage space when a large-sized training dataset is used. So, extracting and selecting the more relevant observations could provide a good solution to high computation time and memory re- quirements costs. In this paper, a new Reduced KPCA (RKPCA) approach is developed to address that issue. It aims to preserve one representative observation for each similar and selected Euclidean distance between training samples. Afterwards, the obtained reduced training dataset is used to build a KPCA model for FDD purposes. The developed RKPCA scheme is tested and evaluated across a numerical example and an actual involuntary process fault and various simulated sensor faults in a cement plant. The obtained results show high monitoring perfor- mance with highest robustness to false alarms and maximum fault detection sensitivity compared to conventional PCA, KPCA and other well-established RKPCA techniques. Furthermore, the unified contribution plot method demonstrates superior potentials in identifying faulty variables.Item Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV(Elsevier, 2019) Bakdi, Azzeddine; Bounoua, Wahiba; Mekhilef, Saad; Halabi, Laith M.In parallel to sustainable growth in solar fraction, continuous reductions in Photovoltaic (PV) module and installation costs fuelled a profound adoption of residential Rooftop Mounted PV (RMPV) installations already reaching grid parity. RMPVs are promoted for economic, social, and environmental factors, energy performance, reduced greenhouse effects and bill savings. RMPV modules and energy conversion units are subject to anomalies which compromise power quality and promote fire risk and safety hazards for which reliable protection is crucial. This article analyses historical data and presents a novel design that easily integrates with data storage units of RMPV systems to automatically process real-time data streams for reliable supervision. Dominant Transformed Components (TCs) are online extracted through multiblock Principal Component Analysis (PCA), most sensitive components are selected and their time-varying characteristics are recursively estimated in a moving window using smooth Kernel Density Estimation (KDE). Novel monitoring indices are developed as preventive alarms using Kullback-Leibler Divergence (KLD). This work exploits data records during 2015–2017 from thin-film, monocrystalline, and polycrystalline RMPV energy conversion systems. Fourteen test scenarios include array faults (line-to-line, line-to-ground, transient arc faults); DC-side mismatches (shadings, open circuits); grid-side anomalies (voltage sags, frequency variations); in addition to inverter anomalies and sensor faultsItem An improved plant-wide fault detection scheme based on PCA and adaptive threshold for reliable process monitoring : application on the new revised model of Tennessee Eastman process(Wiley, 2017) Bakdi, Azzeddine; Kouadri, AbdelmalekItem Fault detection and diagnosis in a cement rotary kiln using PCA with EWMA-based adaptive threshold monitoring scheme(Elsevier, 2017) Bakdi, Azzeddine; Kouadri, Abdelmalek; Bensmail, AbderazakItem A new adaptive PCA based thresholding scheme for fault detection in complex systems(Elsevier, 2017) Bakdi, Azzeddine; Kouadri, AbdelmalekFor large scale and complex processes, data-driven analysis methods are receiving increasing attention for fault detection and diagnosis to improve process operation by detecting when abnormal process operations exist and diagnosing the sources of the abnormalities. Common methods based on multivariate statistical analysis are widely used and particularly principal component analysis (PCA), fault detection indices used along with PCA including the Hotelling T² statistic and the sum of squared prediction error (SPE) known as the Q statistic can be used to identify faults. This paper develops a new adaptive thresholding scheme based on a modified exponentially weighted moving average (EWMA) control chart statistic, which is effective in detecting small changes and abrupt shifts in the process operation. The aim is to enhance the performance of PCA methods for process monitoring, while maintaining a low false alarm rate with good sensitivity of anomalies. The performance of the developed scheme is compared to a conventional fixed thresholding technique by evaluating the detection performance across various types of faults that occurred in the Tennessee Eastman Process, The results demonstrate the promising capabilities of our proposed schemeItem Prediction of boiler output variables through the PLS linear regression technique(2011) Kouadri, A.; Zelmat, M.; Albarbar, A.