https://www.mdu.se/

mdu.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
Enhanced Simulation Environment to Support Research in Modular Manufacturing Systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Abb AB, Process Control Platform, Västerås, Sweden.ORCID iD: 0000-0003-2488-5774
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-4920-2012
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
2023 (English)In: IECON Proc, IEEE Computer Society , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Modular automation provides a challenge for traditional physics simulators, especially if they are used as a simulator in the loop of a development or research project looking at behavior from a systems level. In this paper, we present extensions of a previously developed simulation environment that is tailored to provide these characteristics. The extensions include simulation engine level improvements, such as including better modeling of the material flow, and sensor anomaly injections to model sensor faults or tampering, as well as system-level enhancements and functionality including certificate handling and anomaly detection methods using machine learning. This simulation environment has proven useful for education as well as research and engineering work, and with the provided extensions several new directions of use can be envisioned. The system is demonstrated in the use case of a modular ice-cream factory, including all the new and enhanced functionalities.

Place, publisher, year, edition, pages
IEEE Computer Society , 2023.
Keywords [en]
Anomaly detection, Engineering research, Materials handling, Anomaly detection methods, Engineering works, Machine-learning, Material Flow, Model sensors, Modulars, Sensors faults, Simulation engine, Simulation environment, System levels, Industrial research
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-65151DOI: 10.1109/IECON51785.2023.10311913Scopus ID: 2-s2.0-85179184526ISBN: 9798350331820 (print)OAI: oai:DiVA.org:mdh-65151DiVA, id: diva2:1821864
Conference
IECON Proceedings (Industrial Electronics Conference)
Available from: 2023-12-21 Created: 2023-12-21 Last updated: 2023-12-21Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Leander, BjörnMarkovic, TijanaLeon, Miguel

Search in DiVA

By author/editor
Leander, BjörnMarkovic, TijanaLeon, Miguel
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
Total: 14 hits
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