A Heterogeneous Data Analytics Framework for RFID-Enabled Factories

Ray Y. Zhong; Goran D. Putnik; Stephen T. Newman

doi:10.1109/TSMC.2019.2956201

A Heterogeneous Data Analytics Framework for RFID-Enabled Factories

Ray Y. Zhong, Goran D. Putnik, Stephen T. Newman

Research output: Contribution to journal › Article › peer-review

7 Citations (SciVal)

Abstract

As the wide use of various smart sensors in the manufacturing environment, traditional factories have been upgraded and transformed into an intelligent level. Smart manufacturing factory thus has been enabled by some advanced technologies, such as Internet of Things (IoT) which could facilitate production operations and decision-makings on the one hand. On the other hand, enormous data will be created by the IoT devices. Manufacturing companies are facing some challenges when attempting to make full use of the huge datasets which are heterogeneous in format, complex in logic, unstructured in storage, and abstract in interpretation. In order to address these challenges, this article proposes a data heterogeneous analytics framework for a radio-frequency identification (RFID) enabled factory. RFID captured data from a real-life company is used for validating the proposed framework. Specifically, the performance of machining processes, logistics operations, and inspection behavior are examined from the RFID captured data.

Original language	English
Article number	8931748
Pages (from-to)	5567-5576
Number of pages	10
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	51
Issue number	9
Early online date	12 Dec 2019
DOIs	https://doi.org/10.1109/TSMC.2019.2956201
Publication status	Published - 18 Aug 2021

Bibliographical note

Funding Information:
Manuscript received January 27, 2019; revised June 3, 2019 and October 1, 2019; accepted November 23, 2019. Date of publication December 12, 2019; date of current version August 18, 2021. This work was supported in part by the Seed Fund for Basic Research in HKU under Grant 201906159001, and in part by HKU KE Impact Project Scheme under Grant KE-IP-2019/20-31. This article was recommended by Associate Editor W. Shen. (Corresponding author: Ray Y. Zhong.) R. Y. Zhong is with the Industrial and Manufacturing Systems Engineering, University of Hong Kong, Hong Kong (e-mail: [email protected]).

Publisher Copyright:
© 2013 IEEE.

Keywords

Data analytics
framework
heterogeneity
radio-frequency identification (RFID)
smart manufacturing

ASJC Scopus subject areas

Software
Control and Systems Engineering
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TSMC.2019.2956201

Cite this

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abstract = "As the wide use of various smart sensors in the manufacturing environment, traditional factories have been upgraded and transformed into an intelligent level. Smart manufacturing factory thus has been enabled by some advanced technologies, such as Internet of Things (IoT) which could facilitate production operations and decision-makings on the one hand. On the other hand, enormous data will be created by the IoT devices. Manufacturing companies are facing some challenges when attempting to make full use of the huge datasets which are heterogeneous in format, complex in logic, unstructured in storage, and abstract in interpretation. In order to address these challenges, this article proposes a data heterogeneous analytics framework for a radio-frequency identification (RFID) enabled factory. RFID captured data from a real-life company is used for validating the proposed framework. Specifically, the performance of machining processes, logistics operations, and inspection behavior are examined from the RFID captured data. ",

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A Heterogeneous Data Analytics Framework for RFID-Enabled Factories

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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