IIOT and Deep Learning for Decision-Making: Heading Towards Intelligent Maintenance

Traditional maintenance (TM) practices in industrial companies reveal several drawbacks. It is predominantly conducted on fixed schedules rather than based on actual equipment conditions, resulting in issues such as downtime and additional maintenance costs. These issues stem from a lack of real-tim...

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Bibliographic Details
Main Authors: Chaibi, Adham, Stitou, Ahmed, Lagrat, Ismail, Serrou, Driss, El Mhamedi, Abderrahman, Tchoffa, David, Oussama, Bouazaoui, Labiba, Bousmaki
Format: Conference Proceeding
Language:English
Subjects:
Costs
Deep learning
Fourth Industrial Revolution
Industrial Internet of Things
Industrial systems
Machine learning
Maintenance
Optimization
Predictive maintenance
Real-time systems
Schedules
Solids
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Summary:Traditional maintenance (TM) practices in industrial companies reveal several drawbacks. It is predominantly conducted on fixed schedules rather than based on actual equipment conditions, resulting in issues such as downtime and additional maintenance costs. These issues stem from a lack of real-time data and precise diagnostics, making early detection of failures challenging. This underscores the increasing importance of Industry 4.0 (I4), particularly Predictive maintenance (PM) or what is known as Maintenance 4.0. The latter leverages real-time operational data and sophisticated techniques like data analytics, Machine learning (ML), and peep learning (DL) to anticipate failures in advance. This approach helps reduce maintenance costs and unplanned downtime.
ISSN:2768-6388
DOI:10.1109/ICOA62581.2024.10754342