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Semantic Diagnostics of Smart Factories

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Semantic Technology (JIST 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11341))

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Abstract

Smart factories are one of the biggest trends in modern manufacturing, also known as Industry 4.0. They reach a new level of process automation and make heavy use of sensors in manufactoring equipment, which brings new challenges to monitoring and diagnostics at smart factories. We propose to address the challenges with a novel rule-based monitoring and diagnostics language that relies on ontologies and reasoning and allows one to write diagnostic tasks at a high level of abstraction. We show that our approach speeds up the diagnostic routine of engineers at Siemens: they can formulate and deploy diagnostic tasks in factories faster than with existing Siemens data-driven solutions. Moreover we show that our diagnostic language, despite the built-in reasoning, allows for efficient execution of diagnostic tasks over large volumes of industrial data. Finally, we implemented our ideas in a prototypical diagnostic system for smart factories.

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Notes

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Acknowledgments

This research is supported by the EPSRC projects MaSI\(^3\), DBOnto, ED\(^3\), and by the SIRIUS Centre, Norwegian Research Council project number 237898. Also it is partially supported by the Free University of Bozen-Bolzano projects QUEST, ROBAST and QUADRO.

Author information

Authors and Affiliations

  1. Free University of Bozen-Bolzano, Bolzano, Italy

    Ognjen Savković, Guohui Xiao, Elem Güzel Kalayc & Werner Nutt

  2. University of Oxford, Oxford, UK

    Evgeny Kharlamov & Ian Horrocks

  3. University of Oslo, Oslo, Norway

    Evgeny Kharlamov

  4. Siemens AG, Corporate Technology, Munich, Germany

    Martin Ringsquandl & Gulnar Mehdi

  5. Ludwig-Maximilians University, Munich, Germany

    Martin Ringsquandl

  6. Technical University of Munich, Munich, Germany

    Gulnar Mehdi

Authors
  1. Ognjen Savković
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  2. Evgeny Kharlamov
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  3. Martin Ringsquandl
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  4. Guohui Xiao
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  5. Gulnar Mehdi
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  6. Elem Güzel Kalayc
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  7. Werner Nutt
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  8. Ian Horrocks
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Corresponding author

Correspondence to Evgeny Kharlamov .

Editor information

Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Ryutaro Ichise

  2. Accenture Labs, Dublin, Ireland

    Freddy Lecue

  3. Japan Science and Technology Agency, Tokyo, Japan

    Takahiro Kawamura

  4. Peking University, Beijing, China

    Dongyan Zhao

  5. Imperial College London, London, UK

    Stephen Muggleton

  6. Osaka University, Osaka, Japan

    Kouji Kozaki

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Savković, O. et al. (2018). Semantic Diagnostics of Smart Factories. In: Ichise, R., Lecue, F., Kawamura, T., Zhao, D., Muggleton, S., Kozaki, K. (eds) Semantic Technology. JIST 2018. Lecture Notes in Computer Science(), vol 11341. Springer, Cham. https://doi.org/10.1007/978-3-030-04284-4_19

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  • DOI: https://doi.org/10.1007/978-3-030-04284-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04283-7

  • Online ISBN: 978-3-030-04284-4

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