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Sensors 2015, 15(12), 30165-30186; doi:10.3390/s151229789

Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops

1
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
2
Department of Information Engineering, Guangxi College of Water Resources and Electric Power, Nanning 530023, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yunchuan Sun, Antonio Jara and Shengling Wang
Received: 29 September 2015 / Revised: 20 November 2015 / Accepted: 23 November 2015 / Published: 3 December 2015
(This article belongs to the Special Issue Identification, Information & Knowledge in the Internet of Things)
View Full-Text   |   Download PDF [2959 KB, uploaded 3 December 2015]   |  

Abstract

Radio Frequency Identification (RFID) technology has been widely used in many fields. However, previous studies have mainly focused on product life cycle tracking, and there are few studies on real-time status monitoring of workpieces in manufacturing workshops. In this paper, a wisdom manufacturing model is introduced, a sensing-aware environment for a wisdom manufacturing workshop is constructed, and RFID event models are defined. A synthetic data cleaning method is applied to clean the raw RFID data. The Complex Event Processing (CEP) technology is adopted to monitor abnormal conditions of workpieces in real time. The RFID data cleaning method and data mining technology are examined by simulation and physical experiments. The results show that the synthetic data cleaning method preprocesses data well. The CEP based on the Rifidi® Edge Server technology completed abnormal condition monitoring of workpieces in real time. This paper reveals the importance of RFID spatial and temporal data analysis in real-time status monitoring of workpieces in wisdom manufacturing workshops. View Full-Text
Keywords: radio frequency identification (RFID); complex event processing (CEP); wisdom manufacturing; data cleaning; data mining radio frequency identification (RFID); complex event processing (CEP); wisdom manufacturing; data cleaning; data mining
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, C.; Yao, X.; Zhang, J. Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops. Sensors 2015, 15, 30165-30186.

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