DC Field | Value | Language |
dc.contributor.author | FIDMA, MOhamed ABdelillah | - |
dc.date.accessioned | 2023-10-17T07:40:18Z | - |
dc.date.available | 2023-10-17T07:40:18Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/525 | - |
dc.description | Encadré par : M. Benslimane Sidi Mohamed / Co-encadrant :M. Ouchani Samir / Mme. Hamour Nora | en_US |
dc.description.abstract | Abstract :
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.sponsorship | Mme. Hamour Nora | en_US |
dc.language.iso | en | en_US |
dc.subject | Industry 4.0 | en_US |
dc.subject | Industrial Cyber-Physical System | en_US |
dc.subject | Predictive Maintenance | en_US |
dc.title | Studying and Comparing the existing approaches for predictive maintenance in industry 4.0 | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Master
|