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curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1753))

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Abstract

Recently and due to the advances in sensor technology and Internet-of-Things, the operation of machinery can be monitored, using a higher number of sources and modalities. In this study, we demonstrate that Multi-Modal Translation is capable of transferring knowledge from a modality with higher level of applicability (more usefulness to solve an specific task) but lower level of accessibility (how easy and affordable it is to collect information from this modality) to another one with higher level of accessibility but lower level of applicability. Unlike the fusion of multiple modalities which requires all of the modalities to be available during the deployment stage, our proposed method depends only on the more accessible one; which results in the reduction of the costs regarding instrumentation equipment. The presented case study demonstrates that by the employment of the proposed method we are capable of replacing five acceleration sensors with three current sensors, while the classification accuracy is also increased by more than 1%.

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Notes

  1. 1.

    https://cka-similarity.github.io/.

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Acknowledgments

This work was partially supported by Vinnova and by CHIST-ERA grant CHIST-ERA-19-XAI-012 funded by Swedish Research Council.

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Authors and Affiliations

  1. Center for Applied Intelligence Systems Research, Halmstad University, Halmstad, Sweden

    Amirhossein Berenji, Zahra Taghiyarrenani & Sławomir Nowaczyk

Authors
  1. Amirhossein Berenji
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  2. Zahra Taghiyarrenani
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  3. Sławomir Nowaczyk
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Corresponding author

Correspondence to Amirhossein Berenji .

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Editors and Affiliations

  1. University of Sydney, Sydney, Australia

    Irena Koprinska

  2. University of Bari Aldo Moro, Bari, Italy

    Paolo Mignone

  3. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  4. Warsaw University of Technology, Warsaw, Poland

    Szymon Jaroszewicz

  5. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  6. UniCredit, Rome, Italy

    Francesco Gullo

  7. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  8. Roche, Basel, Switzerland

    Damian Roqueiro

  9. Barcelona Supercomputing Center, Barcelona, Spain

    Gaia Ceddia

  10. Halmstad University, Halmstad, Sweden

    Slawomir Nowaczyk

  11. University of Porto, Porto, Portugal

    João Gama

  12. University of Porto, Porto, Portugal

    Rita Ribeiro

  13. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  14. University of Naples Federico II, Naples, Italy

    Elio Masciari

  15. University of North Carolina, Charlotte, USA

    Zbigniew Ras

  16. ICAR-CNR, Rende, Italy

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  17. University of Pisa, Pisa, Italy

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  18. Aalen University of Applied Sciences, Aalen, Germany

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  19. Warsaw University of Technology, Warszaw, Poland

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  20. KU Leuven, Leuven, Belgium

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  21. University of Duisburg-Essen, Essen, Germany

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  22. Graz University of Technology, Graz, Austria

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  23. AMD, Dublin, Ireland

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  24. UniCredit, Rome, Italy

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  25. UniCredit, Milan, Italy

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  26. National Agency for New Technologies, Rome, Italy

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  27. Unicredit, Rome, Italy

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  28. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  29. University of Bari Aldo Moro, Bari, Italy

    Giuseppina Andresini

  30. University of Lisbon, Lisbon, Portugal

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  31. University of Lisbon, Lisbon, Portugal

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  32. Northwestern University, Chicago, USA

    Lee Cooper

  33. Roche, Basel, Switzerland

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  34. University of Lausanne, Lausanne, Switzerland

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  40. University of Waikato, Hamilton, New Zealand

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  41. Halmstad University, Halmstad, Sweden

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Berenji, A., Taghiyarrenani, Z., Nowaczyk, S. (2023). curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1753. Springer, Cham. https://doi.org/10.1007/978-3-031-23633-4_28

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  • DOI: https://doi.org/10.1007/978-3-031-23633-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23632-7

  • Online ISBN: 978-3-031-23633-4

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