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Edge Architecture for the Integration of Soft Models Based Industrial AI Control into Industry 4.0 Cyber-Physical Systems

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 750))

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Abstract

Traditionally PLC and SCADA systems programmed by automation engineers have been responsible for the control of industrial machines and processes. Industry 4.0 paradigm has merged OT and IT domains, proposing new alternatives for this task. Industry 4.0 approaches start capturing OT industrial data and making it available to the IT domain. Then, this data is visualized, stored and/or analyzed to gain insights of the industrial processes. As a final step, AI models access real-time data to generate predictions and/or control industrial processes. However, this process requires OT and IT knowledge not present in many industrial companies, mainly SMEs. This paper proposes a micro-service edge architecture based on the MING (Mosquitto, InfluxDB, Node-RED and Grafana) stack to ease the integration of soft AI models to control a cyber-physical industrial system. The architecture has been successfully validated controlling the vacuum generation process of an industrial machine. Soft AI models applied to real-time data of the machine analyze the vacuum value to decide when the most suitable time is (i) to start the second pump of the machine, (ii) to finish the process, and (iii) to stop the process due to the detection of humidity.

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Acknowledgement

Research was partially supported by the Centre for the Development of Industrial Technology (CDTI) and the Spanish Minister of Science and Innovation (IDI-20210506) and by the Economic Development, Sustainability and Environment Department of the Basque Government (KK-2022/00119).

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Authors and Affiliations

  1. Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009, Donostia-San Sebastián, Spain

    Ander Garcia, Telmo Fernández de Barreana, Juan Luis Ferrando Chacón & Xabier Oregui

  2. Faculty of Engineering, University of Deusto, Mundaitz Kalea, 50, 20012, Donostia- San Sebastian, Spain

    Ander Garcia

  3. Gaindu, Inzu Group, Olaso Kalea 45, 20870, Elgoibar, Spain

    Zelmar Etxegoin

Authors
  1. Ander Garcia
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  2. Telmo Fernández de Barreana
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  3. Juan Luis Ferrando Chacón
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  4. Xabier Oregui
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  5. Zelmar Etxegoin
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Corresponding author

Correspondence to Ander Garcia .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Garcia, A., de Barreana, T.F., Chacón, J.L.F., Oregui, X., Etxegoin, Z. (2023). Edge Architecture for the Integration of Soft Models Based Industrial AI Control into Industry 4.0 Cyber-Physical Systems. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 750. Springer, Cham. https://doi.org/10.1007/978-3-031-42536-3_7

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