A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 1
Jan.  2018

IEEE/CAA Journal of Automatica Sinica

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    CiteScore: 11.2, Top 5% (Q1)
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Article Contents
Kai Ding and Pingyu Jiang, "RFID-based Production Data Analysis in an IoT-enabled Smart Job-shop," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 128-138, Jan. 2018. doi: 10.1109/JAS.2017.7510418
Citation: Kai Ding and Pingyu Jiang, "RFID-based Production Data Analysis in an IoT-enabled Smart Job-shop," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 128-138, Jan. 2018. doi: 10.1109/JAS.2017.7510418

RFID-based Production Data Analysis in an IoT-enabled Smart Job-shop

doi: 10.1109/JAS.2017.7510418
Funds:

the National Natural Science Foundation of China 71571142

the National Natural Science Foundation of China 51275396

More Information
  • Under industry 4.0, internet of things (IoT), especially radio frequency identification (RFID) technology, has been widely applied in manufacturing environment. This technology can bring convenience to production control and production transparency. Meanwhile, it generates increasing production data that are sometimes discrete, uncorrelated, and hard-to-use. Thus, an efficient analysis method is needed to utilize the invaluable data. This work provides an RFID-based production data analysis method for production control in IoT-enabled smart job-shops. The physical configuration and operation logic of IoT-enabled smart job-shop production are firstly described. Based on that, an RFID-based production data model is built to formalize and correlate the heterogeneous production data. Then, an eventdriven RFID-based production data analysis method is proposed to construct the RFID events and judge the process command execution. Furthermore, a near big data approach is used to excavate hidden information and knowledge from the historical production data. A demonstrative case is studied to verify the feasibility of the proposed model and methods. It is expected that our work will provide a different insight into the RFIDbased production data analysis.

     

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