Attouri, KhadijaMansouri, MajdiHajji, MansourKouadri, AbdelmalekBouzrara, KaisNounou, Hazem2024-12-022024-12-0220242576-3555https://ieeexplore.ieee.org/document/1070831710.1109/CoDIT62066.2024.10708317https://dspace.univ-boumerdes.dz/handle/123456789/14845The 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.enFault detectionPower system dynamicsPower system stabilityFeature extractionElectrical fault detectionReal-time systemsStability analysisRobustnessSafetyPrincipal component analysisArticle