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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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

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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  • [1]
    ITU. Internet of Things Global Standards Initiative, 2015. [Online]. Available: http://www.itu.int/en/ITU-T/gsi/iot/Pages/default.aspx
    K. Ding, P. Y. Jiang, P. L. Sun, and C. Wang, "RFID-enabled physical object tracking in process flow based on an enhanced graphical deduction modeling method, " IEEE Trans. Syst. Man Cybern. Syst., vol. 47, no. 11, pp. 3006-3018, 2017. doi: 10.1109/TSMC.2016.2558104
    P. Y. Jiang and W. Cao, "An RFID-driven graphical formalized deduction for describing the time-sensitive state and position changes of work-inprogress material flows in a job-shop floor, " J. Manuf. Sci. Eng., 135, 3, pp. 031009, May 2013. doi: 10.1115/1.4024037
    R. Y. Zhong, Q. Y. Dai, T. Qu, G. J. Hu, and G. R. Huang, "RFID-enabled real-time manufacturing execution system for masscustomization production, " Robot. Comput. -Integr. Manuf., vol. 29, no. 2, pp. 283-292, Apr. 2013. http://www.sciencedirect.com/science/article/pii/S0736584512000956
    T. Qu, H. D. Yang, G. Q. Huang, Y. F. Zhang, H. Luo, and W. Qin, "A case of implementing RFID-based real-time shop-floor material management for household electrical appliance manufacturers, " J. Intell. Manuf., vol. 23, no. 6, pp. 2343-2356, Dec. 2012. http://dl.acm.org/citation.cfm?id=2423788
    F. Q. Zhang, P. Y. Jiang, M. Zheng, and W. Cao, "A performance evaluation method for radio frequency identification-based tracking network of job-shop-type work-in-process material flows, " Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf., vol. 227, no. 10, pp. 1541-1557, Sep. 2013. https://www.mendeley.com/research-papers/performance-evaluation-method-radio-frequency-identificationbased-tracking-network-jobshoptype-worki/
    C. Saygin, "Adaptive inventory management using RFID data, " Int. J. Adv. Manuf. Technol., vol. 32, no. 9, pp. 1045-1051, Apr. 2007. doi: 10.1007/s00170-006-0405-x
    E. E. Ozguven and K. Ozbay, "An RFID-based inventory management framework for emergency relief operations, " Transp. Res. Part C: Emerg. Technol., vol. 57, pp. 166-187, Aug. 2015. http://ieeexplore.ieee.org/document/6338812/
    R. Y. Zhong, Z. Li, L. Y. Pang, Y. Pan, T. Qu, and G. Q. Huang, "RFID-enabled real-time advanced planning and scheduling shell for production decision making, " Int. J. Comput. Integr. Manuf., vol. 26, no. 7, pp. 649-662, Jan. 2013. http://www.ingentaconnect.com/content/tandf/tcim/2013/00000026/00000007/art00004
    W. K. Wong, Z. X. Guo, and S. Y. S. Leung, "Intelligent multi-objective decision-making model with RFID technology for production planning, " Int. J. Product. Econom., vol. 147, pp. 647-658, Jan. 2014. http://www.sciencedirect.com/science/article/pii/S0925527313002375
    Z. X. Guo, E. W. T. Ngai, C. Yang, and X. D. Liang, "An RFIDbased intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment, " Int. J. Product. Econom., vol. 159, pp. 16-28, Jan. 2015. http://www.sciencedirect.com/science/article/pii/S0925527314002825
    J. Lyu, Jr., S. Y. Chang, and T. L. Chen, "Integrating RFID with quality assurance system-framework and applications, " Exp. Syst. Appl., vol. 36, no. 8, pp. 10877-10882, Oct. 2009. https://dl.acm.org/citation.cfm?id=1542543.1542651
    Y. B. Fu and P. Y. Jiang, "RFID based e-quality tracking in serviceoriented manufacturing execution system, " Chin. J. Mech. Eng., vol. 25, no. 5, pp. 974-981, Sep. 2012. doi: 10.3901/CJME.2012.05.974
    H. B. Cai, A. R. Andoh, X. Su, and S. Li, "A boundary condition based algorithm for locating construction site objects using RFID and GPS, " Adv. Eng. Inform., vol. 28 no. 4, pp. 455-468, Oct. 2014. http://www.sciencedirect.com/science/article/pii/S1474034614000470
    C. Wang, P. Y. Jiang, and K. Ding, "A hybrid-data-on-tag-enabled decentralized control system for flexible smart workpiece manufacturing shop floors, " Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci., to be published. doi: 10.1177/0954406215620452
    C. C. Aggarwal and J. W. Han. A survey of RFID data processing, " in Managing and Mining Sensor Data, C. C. Aggarwal, Ed. New York: Springer, 2013, pp. 349-382. doi: 10.1007/978-1-4614-6309-2_11
    R. Y. Zhong, G. Q. Huang, S. L. Lan, Q. Y. Dai, X. Chen, and T. Zhang, "A big data approach for logistics trajectory discovery from RFID-enabled production data, " Int. J. Product. Econom., vol. 165, pp. 260-272, Jul. 2015. http://www.sciencedirect.com/science/article/pii/S0925527315000481
    R. Y. Zhong, S. L. Lan, C. Xu, Q. Y. Dai, and G. Q. Huang, "Visualization of RFID-enabled shopfloor logistics Big Data in Cloud Manufacturing, " Int. J. Adv. Manuf. Technol., vol. 84, no. 1, pp. 5-16, Apr. 2016. doi: 10.1007/s00170-015-7702-1
    K. Katchasuwanmanee, R. Bateman, and K. Cheng, "Development of the Energy-smart Production Management system (e-ProMan): A big data driven approach, analysis and optimisation, " Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf., vol. 230, no. 5, pp. 972-978, May 2016. https://curve.coventry.ac.uk/open/items/4010c1d1-89d5-4e7b-afef-856f487039ef/1/
    P. Y. Jiang, Y. B. Fu, Q. Q. Zhu and M. Zheng, "Event-driven graphical representative schema for job-shop-type material flows and data computing using automatic identification of radio frequency identification tags, " Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf., vol. 226, no. 2, pp. 339-352, Feb. 2012. https://www.mendeley.com/research-papers/eventdriven-graphical-representative-schema-jobshoptype-material-flows-data-computing-usingautomatic/
    I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. U. Khan, "The rise of ig data on cloud computing: Review and open research issues, "Inform. Syst., vol. 47, pp. 98-115, Jan. 2015. http://www.sciencedirect.com/science/article/pii/S0306437914001288
    M. A. Hernandez, "A generalization of band joins and the merge/purge problem, " Department of Computer Science, Columbia University, Technical Report CUCS-005-1995, 1995.
    C. Wang and P. Y. Jiang, "Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops, " J. Intell. Manuf., to be published. doi: 10.1007/s10845-016-1194-1
    J. R. Quinlan, C4.5:Programs for Machine Learning. San Mateo, CA:Morgan Kaufman Publishers Inc., 1993.
    Y. -R. Shiue, "Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach, " Int. J. Prod. Res., vol. 47, no. 13, pp. 3669-3690, Jul. 2009. doi: 10.1080/00207540701846236
    H. N. Chen, Y. L. Zhu, K. Y. Hu, and T. Ku, "RFID network planning using a multi-swarm optimizer, " J. Netw. Comput. Appl., vol. 34, no. 3, pp. 888-901, May 2011. http://dl.acm.org/citation.cfm?id=1953644.1953774
    D. Cireşan, U. Meier, J. Masci, and J. Schmidhuber, "Multi-column deep neural network for traffic sign classification, " Neural Netw., vol. 32, pp. 333-338, Aug. 2012. http://europepmc.org/abstract/MED/22386783
    P. Y. Jiang, K. Ding, and J. W. Leng, "Towards a cyber-physicalsocial-connected and service-oriented manufacturing paradigm: Social Manufacturing, " Manuf. Lett., vol. 7, pp. 15-21, Jan. 2016. http://www.sciencedirect.com/science/article/pii/S221384631500022X
    P. Y. Jiang, J. W. Leng, K. Ding, P. H. Gu, and Y. Koren, "Social manufacturing as a sustainable paradigm for mass individualization, " Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf., vol. 230, no. 10, pp. 1961-1968, Sep. 2016. doi: 10.1177/0954405416666903


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