Visualization of RFID-enabled shopfloor logistics Big Data in Cloud Manufacturing

被引:153
作者
Zhong, Ray Y. [1 ,2 ]
Lan, Shulin [1 ]
Xu, Chen [3 ]
Dai, Qingyun [4 ]
Huang, George Q. [1 ]
机构
[1] Univ Hong Kong, Dept Ind & Mfg Syst Engn, HKU ZIRI Lab Phys Internet, Hong Kong, Hong Kong, Peoples R China
[2] Shenzhen Univ, Coll Informat Engn, Shenzhen, Peoples R China
[3] Shenzhen Univ, Inst Intelligent Comp Sci, Shenzhen, Peoples R China
[4] Guangdong Polytech Normal Univ, Guangzhou, Guangdong, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Big Data visualization; RFID; Cloud Manufacturing; Shopfloor; Logistics; EXECUTION SYSTEM; MANAGEMENT; INTERNET; QUALITY; THINGS; MODEL;
D O I
10.1007/s00170-015-7702-1
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cloud Manufacturing twining with Internet of Things (IoT) has been waked up to achieve final intelligent manufacturing. With the IoT technologies such as radio frequency identification (RFID) implemented in manufacturing sites, enormous data will be generated. Such data are so complex, abstract, and variable so that it is difficult to make full use of the data which carry great myriad of useful information and knowledge. This paper presents a visualization approach for the RFID-enabled shopfloor logistics Big Data from Cloud Manufacturing. An innovative RFID-Cuboid model is used for reconstructing the RFID raw data given the production logic and time series. Several contributions are highlighted. Firstly, a possible approach to integrate IoT and Cloud Manufacturing is introduced to upgrade and transform the traditional industry for an intelligent future. Secondly, an RFID-Cuboid model is proposed by using the production logic and time stamps to chain the RFID data so that the data could be interpreted. Thirdly, a real-life case is reported to show the feasibility and practicality of the proposed visualization approach to help different end-users to ease their daily operations. Lessons and insights from this case are meaningful for the implementation of IoT-enabled Cloud Manufacturing and Big Data analytics in industry field.
引用
收藏
页码:5 / 16
页数:12
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