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基于数字孪生的集成监测系统:韩国应用案例。

Digital Twin-Based Integrated Monitoring System: Korean Application Cases.

机构信息

R&D Research Center, IGI Korea, Seoul 08376, Korea.

IT Converged Process R&D Group, Korea Institute of Industrial Technology, Ansan-si 15588, Korea.

出版信息

Sensors (Basel). 2022 Jul 21;22(14):5450. doi: 10.3390/s22145450.

DOI:10.3390/s22145450
PMID:35891132
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9319650/
Abstract

A digital twin is a virtual model of a process, product, or service, which is one of the key technologies in the fourth industry. The pairing of the virtual and physical world allows analysis of data and monitoring of systems to head off problems before they occur. This paper presents a digital twin architecture and a system based on an interoperable data model. It explains how to build a digital twin for the integrated control monitoring using edge devices, data analytics, and realistic 3D visualization. The system allows continuous collaboration between field engineers for data gathering, designers for modeling 3D models, and layout engineers for layout changing by generating 3D digital twin models automatically. The system helps stakeholders focus on their respective roles to build digital twins. Examples applied to the Korean automotive parts makers are also introduced in this paper. The system can be easily used by small and medium-sized enterprises (SMEs) as well as large companies. Beyond simply watching the production site with CCTV, the production site can be intuitively managed based on the digital twin.

摘要

数字孪生是过程、产品或服务的虚拟模型,是第四次工业的关键技术之一。虚拟世界和物理世界的配对允许对数据进行分析和对系统进行监控,以在问题发生之前解决问题。本文提出了一种基于可互操作数据模型的数字孪生架构和系统。它解释了如何使用边缘设备、数据分析和逼真的 3D 可视化来构建用于集成控制监控的数字孪生。该系统允许现场工程师、3D 模型设计师和布局工程师通过自动生成 3D 数字孪生模型进行数据收集、3D 模型设计和布局更改方面的持续协作。该系统有助于利益相关者专注于各自的角色来构建数字孪生。本文还介绍了应用于韩国汽车零部件制造商的示例。该系统可以很容易地被中小企业(SMEs)和大公司使用。不再仅仅通过 CCTV 监控生产现场,而是可以基于数字孪生直观地管理生产现场。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/37e7c62ccd37/sensors-22-05450-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/18c715f63aad/sensors-22-05450-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/6190eb8a37b3/sensors-22-05450-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/0eb695fa3f0e/sensors-22-05450-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/687fed5ddb3b/sensors-22-05450-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/9e514d45dd2c/sensors-22-05450-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/1774d6dea1d8/sensors-22-05450-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/37e7c62ccd37/sensors-22-05450-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/18c715f63aad/sensors-22-05450-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/6190eb8a37b3/sensors-22-05450-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/0eb695fa3f0e/sensors-22-05450-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/687fed5ddb3b/sensors-22-05450-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/9e514d45dd2c/sensors-22-05450-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/1774d6dea1d8/sensors-22-05450-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b2/9319650/37e7c62ccd37/sensors-22-05450-g007.jpg

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Integration of Digital Twin, Machine-Learning and Industry 4.0 Tools for Anomaly Detection: An Application to a Food Plant.数字孪生、机器学习和工业 4.0 工具的集成用于异常检测:在食品工厂的应用。
Sensors (Basel). 2022 May 30;22(11):4143. doi: 10.3390/s22114143.
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Digital Twin for Civil Engineering Systems: An Exploratory Review for Distributed Sensing Updating.
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