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The adoption of Digital Twins in automated maintenance represents a pivotal shift in industrial operations, involving the creation of virtual replicas of physical assets or processes to monitor, analyze, and optimize performance. In the context of Smart Factories and Industry 4.0, Digital Twins play a crucial role as models representing the factory, facilitating data analysis and strategic planning to enhance production efficiency. While predictive maintenance predominates in maintenance management studies, preventive maintenance also holds significance, contributing to overall maintenance strategies based on factors like equipment complexity and criticality. Digital Twins further enable remote monitoring of production processes, offering operational flexibility to maintenance teams. The paper is structured to delve into the significance of Digital Twins for both predictive and preventive maintenance in subsequent sections, presenting a proposed approach and an illustrative example of a motor digital twin on the AWS platform, concluding with perspectives on the topic.

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Preventive and Predictive Maintenance Approach with the Use of Digital Twin Concept

  • 会议论文

  • 发表日期：

    2025年

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  • Ihsane Abouzid,
  • Rajaa Saidi

摘要

The adoption of Digital Twins in automated maintenance represents a pivotal shift in industrial operations, involving the creation of virtual replicas of physical assets or processes to monitor, analyze, and optimize performance. In the context of Smart Factories and Industry 4.0, Digital Twins play a crucial role as models representing the factory, facilitating data analysis and strategic planning to enhance production efficiency. While predictive maintenance predominates in maintenance management studies, preventive maintenance also holds significance, contributing to overall maintenance strategies based on factors like equipment complexity and criticality. Digital Twins further enable remote monitoring of production processes, offering operational flexibility to maintenance teams. The paper is structured to delve into the significance of Digital Twins for both predictive and preventive maintenance in subsequent sections, presenting a proposed approach and an illustrative example of a motor digital twin on the AWS platform, concluding with perspectives on the topic.