mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Classification of PROFINET I/O Configurations utilizing Neural Networks
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. ABB Industrial Automation, Process Control Platform, Västerås, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. ABB Industrial Automation, Process Control Platform, Västerås, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5293-3804
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1364-8127
Show others and affiliations
2019 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 1321-1324Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019. p. 1321-1324
Keywords [en]
Field devices, Fieldbus, In-process, Neural network model, Process controllers, PROFInet, Real time, Signal value, Factory automation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-47118DOI: 10.1109/ETFA.2019.8869024Scopus ID: 2-s2.0-85074197516ISBN: 9781728103037 (print)OAI: oai:DiVA.org:mdh-47118DiVA, id: diva2:1394909
Conference
24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019, 10 September 2019 through 13 September 2019
Available from: 2020-02-20 Created: 2020-02-20 Last updated: 2020-02-20Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Johansson, BjarneCausevic, AidaPapadopoulos, AlessandroNolte, Thomas

Search in DiVA

By author/editor
Johansson, BjarneLeander, BjörnCausevic, AidaPapadopoulos, AlessandroNolte, Thomas
By organisation
Embedded Systems
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf