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Multi-source transfer learning of time series in cyclical manufacturing

Author

Listed:
  • Werner Zellinger

    (Johannes Kepler University Linz
    Software Competence Center Hagenberg GmbH)

  • Thomas Grubinger

    (Software Competence Center Hagenberg GmbH)

  • Michael Zwick

    (Software Competence Center Hagenberg GmbH)

  • Edwin Lughofer

    (Johannes Kepler University Linz)

  • Holger Schöner

    (Software Competence Center Hagenberg GmbH)

  • Thomas Natschläger

    (Software Competence Center Hagenberg GmbH)

  • Susanne Saminger-Platz

    (Johannes Kepler University Linz)

Abstract

This paper describes a new transfer learning method for modeling sensor time series following multiple different distributions, e.g. originating from multiple different tool settings. The method aims at removing distribution specific information before the modeling of the individual time series takes place. This is done by mapping the data to a new space such that the representations of different distributions are aligned. Domain knowledge is incorporated by means of corresponding parameters, e.g. physical dimensions of tool settings. Results on a real-world problem of industrial manufacturing show that our method is able to significantly improve the performance of regression models on time series following previously unseen distributions. Graphic abstract

Suggested Citation

  • Werner Zellinger & Thomas Grubinger & Michael Zwick & Edwin Lughofer & Holger Schöner & Thomas Natschläger & Susanne Saminger-Platz, 2020. "Multi-source transfer learning of time series in cyclical manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 777-787, March.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:3:d:10.1007_s10845-019-01499-4
    DOI: 10.1007/s10845-019-01499-4
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    Citations

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    Cited by:

    1. Hasan Tercan & Philipp Deibert & Tobias Meisen, 2022. "Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 283-292, January.
    2. Cinzia Giannetti & Aniekan Essien, 2022. "Towards scalable and reusable predictive models for cyber twins in manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 441-455, February.

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