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Application of Software Defined Networks for Collection of Process Data in Industrial Real-Time Systems

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Advances in Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1653))

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Abstract

Modern computer systems are expected to be more and more advanced and therefore their complexity is ever increasing. Some solutions make control systems more open and flexible. Whereas the main goal of others is to improve reliability. Such services may adding failure detection and even failure prediction features. Knowing the possibility of failures with a high level of accuracy may bring excellent management and economic benefits and refers to predictive maintenance. However, it is based on process, and diagnostic data gathered from the monitored system, which sometimes is not straightforward. In this paper, we present a way of obtaining knowledge about the system state without introducing significant modifications to the system itself. It is based on Software Defined Networks SDN, which allows for controlling of communication network traffic on the software level as well as obtaining information about the state of the network or even about the user data that are sent in it. Data gathered in such a way may be forwarded to machine learning services, e.g., to perform predictive maintenance. The conceptual solution presented in this paper was verified during some experimental research performed in a system based on Beckhoff industrial controllers and EtherCAT communication network. In the role of SDN switch and SDN controller Raspberry Pi development boards were used.

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Acknowledgement

The research leading to these results received funding from the Norway Grants 2014–2021, which the National Centre operates for Research and Development under the project “Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective” (Project Contract no.: NOR/POLNOR/CoBotAGV/0027/2019-00) and partially by the Polish Ministry of Science and Higher Education Funds for Statutory Research.

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Authors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Ireneusz Smołka & Jacek Stój

  2. Western Norway University of Applied Sciences, Førde, Norway

    Marcin Fojcik

Authors
  1. Ireneusz Smołka
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  2. Jacek Stój
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  3. Marcin Fojcik
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Corresponding author

Correspondence to Jacek Stój .

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Editors and Affiliations

  1. University of Craiova, Craiova, Romania

    Costin Bădică

  2. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. Claude Bernard University Lyon 1, Villeurbanne Cedex, France

    Djamal Benslimane

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Marek Krótkiewicz

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Smołka, I., Stój, J., Fojcik, M. (2022). Application of Software Defined Networks for Collection of Process Data in Industrial Real-Time Systems. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_37

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  • DOI: https://doi.org/10.1007/978-3-031-16210-7_37

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  • Print ISBN: 978-3-031-16209-1

  • Online ISBN: 978-3-031-16210-7

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