By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry~4.0 promotes integrating cyber-physical worlds through cyber-physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT is an emerging but conceptually different construct than CPS. Like CPS, DT relies on communication to create a highly-consistent, synchronized digital mirror image of the objects or physical processes. DT, in addition, uses built-in models on this precise image to simulate, analyze, predict, and optimize their real-time operation using feedback. DT is rapidly diffusing in the industries with recent advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research in next-generation (NextG) wireless technologies (e.g., 5G and beyond networks), various tools (e.g., age of information, federated learning, data analytics), and other promising trends in networked computing (e.g., edge and cloud computing). Moreover, we discuss the DT deployment strategies at different industrial communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT's adoption.
翻译:通过合并最近的通信和控制技术、计算和数据分析技术以及模块制造,工业~4.4通过监测、优化和预测工业流程的网络物理系统(CPS)和数字双对(DT),促进将网络物理世界一体化,以监测、优化和预测工业流程。DT是一个新兴的但概念上与CPS不同的结构。与CPS一样,DT依赖通信来创造高度一致的、同步的物体或物理流程的数字镜图像。此外,DT还利用这一精确图像的内在边际模型,利用反馈模拟、分析、预测和优化其实时运行。DT在工业应用中迅速扩散,工业互联网(IIOT)、边缘和云计算、机器学习、人工智能智能智能和高级数据分析的最新进步。然而,现有的文献缺乏从通信和计算机化角度确定和讨论这些技术在DT驱动的产业中的作用和要求。在本篇文章中,我们首先介绍了智能产业应用的功能、吸引力和创新性应用。然后,我们在工业应用中迅速传播工业应用,通过系统审查和反映最新G数据工具。