Big data analytics for smart factories of the future

被引：89
|
作者：
Gao, Robert X. [1 ]
Wang, Lihui [2 ]
Helu, Moneer [3 ]
Teti, Roberto [4 ]
机构：
[1] Case Western Reserve Univ, Dept Mech & Aerosp Engn, 10900 Euclid Ave,Glennan Engr Bldg, Cleveland, OH 44106 USA
[2] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden
[3] NIST, Engn Lab, Gaithersburg, MD 20899 USA
[4] Univ Naples Federico II, Dept Chem Mat & Ind Prod Engn, Naples, Italy
来源：
CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2020年 / 69卷 / 02期
基金：
美国国家科学基金会;
关键词：
Digital manufacturing system; Information; Learning; DEFINITIVE-SCREENING DESIGNS; STATISTICAL PROCESS-CONTROL; FAULT-DIAGNOSIS; DIGITAL TWIN; MANUFACTURING PROCESS; ADVERSARIAL NETWORKS; FEATURE-EXTRACTION; NEURAL-NETWORKS; DISCOVERY RATE; DEEP;
D O I：
10.1016/j.cirp.2020.05.002
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Continued advancement of sensors has led to an ever-increasing amount of data of various physical nature to be acquired from production lines. As rich information relevant to the machines and processes are embedded within these "big data", how to effectively and efficiently discover patterns in the big data to enhance productivity and economy has become both a challenge and an opportunity. This paper discusses essential elements of and promising solutions enabled by data science that are critical to processing data of high volume, velocity, variety, and low veracity, towards the creation of added-value in smart factories of the future. (C) 2020 CIRP. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：668 / 692
页数：25
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