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Classification of PROFINET I/O Configurations utilizing Neural Networks
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. ABB Industrial Automation, Process Control Platform, Västerås, Sweden.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. ABB Industrial Automation, Process Control Platform, Västerås, Sweden.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0001-5293-3804
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-1364-8127
Vise andre og tillknytning
2019 (engelsk)Inngår i: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Institute of Electrical and Electronics Engineers Inc. , 2019, s. 1321-1324Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In process automation installations, the I/O system connect the field devices to the process controller over a fieldbus, a reliable, real-time capable communication link with signal values cyclical being exchanged with a 10-100 millisecond rate. If a deviation from intended behaviour occurs, analyzing the potentially vast data recordings from the field can be a time consuming and cumbersome task for an engineer. For the engineer to be able to get a full understanding of the problem, knowledge of the used I/O configuration is required. In the problem report, the configuration description is sometimes missing. In such cases it is difficult to use the recorded data for analysis of the problem.In this paper we present our ongoing work towards using neural network models as assistance in the interpretation of an industrial fieldbus communication recording. To show the potential of such an approach we present an example using an industrial setup where fieldbus data is collected and classified. In this context we present an evaluation of the suitability of different neural net configurations and sizes for the problem at hand.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc. , 2019. s. 1321-1324
Emneord [en]
Field devices, Fieldbus, In-process, Neural network model, Process controllers, PROFInet, Real time, Signal value, Factory automation
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-47118DOI: 10.1109/ETFA.2019.8869024Scopus ID: 2-s2.0-85074197516ISBN: 9781728103037 (tryckt)OAI: oai:DiVA.org:mdh-47118DiVA, id: diva2:1394909
Konferanse
24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019, 10 September 2019 through 13 September 2019
Tilgjengelig fra: 2020-02-20 Laget: 2020-02-20 Sist oppdatert: 2020-02-20bibliografisk kontrollert

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