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Please use this identifier to cite or link to this item: https://repository.esi-sba.dz/jspui/handle/123456789/526
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dc.contributor.authorFIDMA, MOhamed ABdelillah-
dc.date.accessioned2023-10-17T07:44:58Z-
dc.date.available2023-10-17T07:44:58Z-
dc.date.issued2023-
dc.identifier.urihttps://repository.esi-sba.dz/jspui/handle/123456789/526-
dc.descriptionEncadré par :M. Benslimane Sidi Mohamed / Co-encadrant : Mme. Hamour Nora /M. Ouchani Samiren_US
dc.description.abstractAbstract : The advent of Industry 4.0 has brought about significant advancements in manufacturing processes, leveraging advanced sensing, and data analytics technologies to optimize efficiency. Within this paradigm, predictive maintenance plays a crucial role in ensuring the reliability and availability of production systems. However, the heterogeneous nature of industrial data poses challenges in achieving effective maintenance decision-making and interoperability across different manufacturing systems. This graudation project aims to address these challenges by proposing a hybrid approach that combines the strengths of ontologies and machine learning techniques. The research focuses on developing an intelligent system that leverages standardized knowledge representation and predictive capabilities to enhance maintenance decision-making in real-time. This research objective is to explore the use of ontologies as a standardized knowledge representation approach within the context of smart factories and Cyber- Physical Systems (CPS). By bridging the semantic gap and improving interoperability, ontologies facilitate the integration of diverse manufacturing systems, enabling a holistic view of the maintenance processes. Furthermore, we aim is to leverage machine learning techniques, such as data mining and predictive analytics, to detect and predict potential anomalies in manufacturing processes. By analyzing historical data, machine learning models can identify patterns and trends that assist in making accurate predictions for maintenance needs. This approach enables proactive maintenance actions, reducing downtime and enhancing production efficiency. *** Résumé : L’av`enement de l’Industrie 4.0 a entraˆın´e d’importants progr`es dans les processus de fabrication, en exploitant les technologies avanc´ees de d´etection et d’analyse de donn´ees pour optimiser l’efficacit´e. Dans ce paradigme, la maintenance pr´edictive joue un rˆole crucial dans la garantie de la fiabilit´e et de la disponibilit´e des syst`emes de production. Cependant, la nature h´et´erog`ene des donn´ees industrielles pose des d´efis pour prendre des d´ecisions de maintenance efficaces et assurer l’interop´erabilit´e entre diff´erents syst`emes de fabrication. Ce projet de fin d’´etudes vise `a relever ces d´efis en proposant une approche hybride qui combine les atouts des ontologies et des techniques d’apprentissage automatique. La recherche se concentre sur le d´eveloppement d’un syst`eme intelligent qui exploite la repr´esentation normalis´ee des connaissances et les capacit´es pr´edictives pour am´eliorer la prise de d´ecision en mati`ere de maintenance en temps r´eel. De plus, notre objectif est d’exploiter les techniques d’apprentissage automatique, telles que l’exploration de donn´ees et l’analyse pr´edictive, pour d´etecter et pr´edire les anomalies potentielles dans les processus de fabrication. En analysant les donn´ees historiques, les mod`eles d’apprentissage automatique peuvent identifier des sch´emas et des tendances qui aident `a faire des pr´edictions pr´ecises pour les besoins de maintenance. En combinant les ontologies et les techniques d’apprentissage automatique, ce projet de recherche vise `a d´evelopper un syst`eme hybride de maintenance pr´edictive. La validation de l’efficacit´e du syst`eme sera r´ealis´ee par des ´etudes exp´erimentales, comparant ses performances `a celles des approches existantes. L’´evaluation portera sur des aspects tels que la pr´ecision de la d´etection des besoins de maintenance, la r´eduction des temps d’arrˆet et l’am´elioration globale de l’efficacit´e de production.en_US
dc.language.isoenen_US
dc.subjectIndustry 4.0en_US
dc.subjectIndustrial Cyber-Physical Systemen_US
dc.subjectPredictive Maintenanceen_US
dc.subjectOntologyen_US
dc.subjectChronicle Miningen_US
dc.subjectSWRL Rulesen_US
dc.titleHybrid predictive maintenace system based on ontologies and machine learning in industry 4.0en_US
dc.typeThesisen_US
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