Recent Advances and Prospects in Industrial AI and Applications

被引:0
|
作者
Lee J. [1 ,2 ]
Li X. [1 ]
Xu Y.-M. [1 ]
Yang S. [1 ]
Sun K.-Y. [2 ]
机构
[1] Department of Mechanical Engineering, University of Cincinnati, Cincinnati, 45221, OH
[2] Foxconn Technology Group, Milwaukee
来源
Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 10期
关键词
Automation; Industrial; 4.0; Industrial AI; Intelligent manufacturing;
D O I
10.16383/j.aas.c200501
中图分类号
学科分类号
摘要
Industry 4.0 is an advanced architecture which aims to improve the manufacturing process and product quality by using large-scale machine to machine communication and Internet of things deployments to offer increased automation, enhanced communication and self-monitoring, without the need for human intervention. The artificial intelligence (AI) technology plays an important role in the revolution of industry. However, the traditional AI technology focuses more on the daily life, society aspects and finance areas rather than the practical problems in the real industries. To address this issue, the architecture of Industrial AI is developed to comprise efficiency, robustness and system optimization of intelligent system within the industrial areas, which is more specifically designed to solve the actual problems in practice and create larger values. This paper firstly presents the concepts of industrial AI, which is followed by several case studies from different applications to validate the effectiveness and success of architecture of industrial AI. Copyright ©2019 Acta Automatica Sinica. All rights reserved.
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页码:2031 / 2044
页数:13
相关论文
共 54 条
  • [1] CIFAR Pan-Canadian Artificial Intelligence Strategy, (2018)
  • [2] State Council Issued Notice of the New Generation Artificial Intelligence Development Plan, (2017)
  • [3] Strategie Kunstliche Intelligenz der Bundesregierung
  • [4] Accelerating America's Leadership in Artificial Intelligence, (2019)
  • [5] Heidary R, Gabriel S, Modarres M, Et al., A Review of Data-Driven Oil and Gas Pipeline Pitting Corrosion Growth Models Applicable for Prognostic and Health Management, International Journal of Prognostics and Health Management, 9, 1, (2018)
  • [6] Davis J, Edgar T, Porter J, Et al., Smart manufacturing, manufacturing intelligence and demand-dynamic performance, Computers & Chemical Engineering, 47, pp. 145-156, (2012)
  • [7] Lee J, Ni J, Wang AZ., From Big Data to Intelligent Manufacturing, (2016)
  • [8] Lee J., Industrial Big Data, (2015)
  • [9] Tao F, Qi Q, Liu A, Et al., Data-driven Smart Manufacturing, Journal of Manufacturing Systems, 48, pp. 157-169, (2018)
  • [10] Liu Y, Zhao J, Wang W., Improved echo state network based on data-driven and its application in prediction of blast furnace gas output, Acta Automatica Sinica, 35, pp. 731-738, (2009)