研究成果 / 2024 / 会议论文
Enhancing Digital Twins with Deep Reinforcement Learning: A Use Case in Maintenance Prioritization
利用深度强化学习增强数字孪生：维护优先级排序用例
Proceedings - Winter Simulation Conference · 2024
摘要
原始英文摘要
This paper introduces an innovative framework that enhances digital twins with deep reinforcement learning (DRL) to support maintenance in manufacturing systems. Utilizing a sophisticated artificial intelligence (AI) layer, this framework integrates real-time and historical production data from a physical manufacturing system to a digital twin, enabling dynamic simulation and analysis. Maintenance decisions are informed by DRL algorithms that analyze this data, facilitating smart maintenance strategies that adaptively prioritize tasks based on predictive analytics. The effectiveness of this approach is demonstrated through a case study in a lab-scale drone factory, where maintenance tasks are prioritized using a proximal policy optimization. This integration not only refines maintenance decisions but also aligns with the broader goals of operational efficiency and sustainability in Industry 4.0. Our results highlight the potential of combining DRL with digital twins to significantly enhance decision-making in industrial maintenance, offering a novel approach to predictive and prescriptive maintenance practices.
原始来源: Chalmers Research ↗
这项研究在做什么
维护优先级会随生产系统状态而变化。本文将数字孪生与近端策略优化算法结合，在实验室无人机工厂中研究动态维护优先级排序。
研究要点
- 连接实时与历史生产数据，构建 AI 增强数字孪生。
- 使用近端策略优化算法动态安排维护任务优先级。
- 在实验室规模的无人机工厂中验证方法。
引用这项成果
Siyuan Chen, Paulo Victor Lopes, Silvan Marti, Mohan Rajashekarappa, Sunith Bandaru, Christina Windmark, Jon Bokrantz, Anders Skoogh (2024). Enhancing Digital Twins with Deep Reinforcement Learning: A Use Case in Maintenance Prioritization. Proceedings - Winter Simulation Conference, pp. 1611–1622. https://doi.org/10.1109/WSC63780.2024.10838867
@inproceedings{Chen2024DigitalTwinsDeep,
title = {{Enhancing Digital Twins with Deep Reinforcement Learning: A Use Case in Maintenance Prioritization}},
author = {Chen, Siyuan and Lopes, Paulo Victor and Marti, Silvan and Rajashekarappa, Mohan and Bandaru, Sunith and Windmark, Christina and Bokrantz, Jon and Skoogh, Anders},
year = {2024},
booktitle = {2024 Winter Simulation Conference (WSC)},
pages = {1611--1622},
doi = {10.1109/WSC63780.2024.10838867},
url = {https://research.chalmers.se/en/publication/545197}
}
英文摘要来自所链接的原始记录；研究解读和要点用于简要说明论文内容。