Skip navigation
Please use this identifier to cite or link to this item: https://repository.esi-sba.dz/jspui/handle/123456789/525
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFIDMA, MOhamed ABdelillah-
dc.date.accessioned2023-10-17T07:40:18Z-
dc.date.available2023-10-17T07:40:18Z-
dc.date.issued2023-
dc.identifier.urihttps://repository.esi-sba.dz/jspui/handle/123456789/525-
dc.descriptionEncadré par : M. Benslimane Sidi Mohamed / Co-encadrant :M. Ouchani Samir / Mme. Hamour Noraen_US
dc.description.abstractAbstract : In few recent years, there has been a significant global focus on the fourth industrial revolution, traditional manufacturing factories are transforming into so-called “smart factories”, which apply high-tech sensing and computation technologies on different manufacturing processes and production systems. As today’s manufacturing market is becoming more competitive, how to improve the availability, and quality of manufacturing services in smart factories is a crucial concern for manufactures. The current scenario has created a growing need for the implementation of predictive maintenance in production lines. Predictive maintenance involves proactive maintenance activities aimed at preventing failures and enhancing the availability and safety of the maintained system. This demand arises from the recognition of the importance of minimizing downtime, maximizing operational efficiency, and ensuring the reliability of industrial processes. There are several existing approaches for PdM in IR4.0, each with its own advantages and disadvantages. This master thesis explores the predictive maintenance in Industry 4.0, with a focus on studying and comparing the existing approaches. it’s complementary by surveying the existing contributions in this field, and applying the best selected strategy on a real industrial system. *** Résumé : Ces derni`eres ann´ees, la quatri`eme r´evolution industrielle a attir´e l’attention du monde entier. Les usines de fabrication traditionnelle se transforment en ce que l’on appelle des ≪ usines intelligentes ≫, qui appliquent des technologies de d´etection de haute technologie et de calcul sur diff´erents proc´ed´es de fabrication et syst`emes de production. Comme le march´e de la fabrication devient de plus en plus comp´etitif, am´eliorer la disponibilit´e et la qualit´e des services de fabrication dans les usines intelligentes est devenu une pr´eoccupation cruciale pour les fabricants. Cette situation a entraˆın´e une demande croissante pour la mise en place de la maintenance pr´edictive sur les lignes de production, qui consiste `a r´ealiser des activit´es de maintenance pour ´eviter les d´efaillances et am´eliorer la disponibilit´e et la s´ecurit´e du syst`eme maintenu. Il existe plusieurs approches existantes pour la maintenance pr´edictive dans IR4.0, chacune avec ses propres avantages et inconv´enients. Ce m´emoire de master explore la maintenance pr´edictive dans l’industrie 4.0, en mettant l’accent sur l’´etude et la comparaison des approches existantes. Il compl`ete les contributions existantes dans ce domaine, et applique la meilleure strat´egie choisie sur un syst`eme industriel r´eel.en_US
dc.description.sponsorshipMme. Hamour Noraen_US
dc.language.isoenen_US
dc.subjectIndustry 4.0en_US
dc.subjectIndustrial Cyber-Physical Systemen_US
dc.subjectPredictive Maintenanceen_US
dc.titleStudying and Comparing the existing approaches for predictive maintenance in industry 4.0en_US
dc.typeThesisen_US
Appears in Collections:Master

Files in This Item:
File Description SizeFormat 
Master_Thesis-Fidma-1-1.pdf74,44 kBAdobe PDFView/Open
Show simple item record


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.