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Towards industrial internet of things: crankshaft monitoring, traceability and tracking using RFID

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journal contribution
posted on 24.03.2016, 17:04 by Diana M. Segura-Velandia, Navjot Kaur, Paul ConwayPaul Conway, William WhittowWilliam Whittow, Andrew WestAndrew West
The large number of requirements and opportunities for automatic identification in manufacturing domains such as automotive and electronics has accelerated the demand for item-level tracking using radio-frequency identification technology. End-users are interested in implementing automatic identification systems, which are capable of ensuring full component process history, traceability and tracking preventing costly downtime to rectify processing defects and product recalls. The research outlined in this paper investigates the feasibility of implementing an RFID system for the manufacturing and assembly of crankshafts. The proposed solution involves the attachment of bolts with embedded RFID functionality by fitting a reader antenna reader to an overhead gantry that spans the production line and reads and writes production data to the tags. The manufacturing, assembly and service data captured through RFID tags and stored on a local server, could further be integrated with higher-level business applications facilitating seamless integration within the factory.


The authors wish to express their gratitude to the industrial and academic collaborators of the INTELLICO (Intelligent embedded components for enhanced supply chain observability and traceability) project. The TSB Grant award TP No. 14218-87248 supported this work financially. Also, we thank Xerafy and R-Tech who provided RFID tags and very insightful comments.



  • Mechanical, Electrical and Manufacturing Engineering

Published in

Robotics and Computer-Integrated Manufacturing




66 - 77


SEGURA-VELANDIA, D.M. .. et al., 2016. Towards industrial internet of things: crankshaft monitoring, traceability and tracking using RFID. Robotics and Computer-Integrated Manufacturing, 41, pp.66-77.


© Elsevier


AM (Accepted Manuscript)

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This paper was accepted for publication in the journal Robotics and Computer-Integrated Manufacturing and the definitive published version is available at





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