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Methodology for Optimizing Manufacturing Machines with IoT

 
: Cuk, Emir; Chaparro, Valentina

:

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2018. Proceedings : November 1-3, 2018, Bali, Indonesia
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-7358-4
ISBN: 978-1-5386-7357-7
ISBN: 978-1-5386-7359-1
S.90-96
International Conference on Internet of Things and Intelligence System (IOTAIS) <2018, Bali>
Englisch
Konferenzbeitrag
Fraunhofer IPA ()
Industrial Internet of Things; Signalverarbeitung; Datenanalyse; Produktionsmaschine; Internet der Dinge; Optimierungsmethode

Abstract
The goal of industry 4.0 is to use all the information that can be extracted from a supply chain to continuously optimize all aspects of its operation. The data acquisition is still a big challenge and the first step of the fourth industrial revolution. Getting data from software is much easier than getting data out of hardware like manufacturing machines. Especially if the Programmable Logic Controller (PLC) data is either poorly documented or not designed for these requirements. Therefore, we created a methodology to track the most common movement of a machine, which is the linear motion. The solution is an IoTdevice: a small, wireless, and low cost sensor, designed to provide data about linear motions within a machine in realtime. We designed a static generic model and a method for machine optimization with data acquisition results comparable to results in other approaches. By implementing the methodology to a real industrial scenario, the results enable us to prove our hypothesis. The IoT-device data was as good as the PLC data and even closer to real-time. Our methodology also shows a higher potential to automate the data analysis.

: http://publica.fraunhofer.de/dokumente/N-534957.html