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Sliding Window Symbolic Regression for Predictive Maintenance Using Model Ensembles

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Computer Aided Systems Theory – EUROCAST 2017 (EUROCAST 2017)

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Abstract

Predictive Maintenance (PdM) is among the trending topics in the current Industry 4.0 movement and hence, intensively investigated. It aims at sophisticated scheduling of maintenance, mostly in the area of industrial production plants. The idea behind PdM is that, instead of following fixed intervals, service actions could be planned based upon the monitored system condition in order to prevent outages, which leads to less redundant maintenance procedures and less necessary overhauls. In this work we will present a method to analyze a continuous stream of data, which describes a system’s condition progressively. Therefore, we motivate the employment of symbolic regression ensemble models and introduce a sliding-window based algorithm for their evaluation and the detection of stable and changing system states.

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Notes

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Acknowledgments

The work described in this paper was done within the project “Smart Factory Lab” which is funded by the European Fund for Regional Development (EFRE) and the country of Upper Austria as part of the program “Investing in Growth and Jobs 2014–2020”.

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Authors and Affiliations

  1. Institute for Smart Production, University of Applied Sciences Upper Austria, Campus Hagenberg, Steyr, Wels, Austria

    Jan Zenisek, Michael Affenzeller, Josef Wolfartsberger, Mathias Silmbroth, Christoph Sievi, Aziz Huskic & Herbert Jodlbauer

  2. Institute for Formal Models and Verification, Johannes Kepler University Linz, Altenberger Straße 69, 4040, Linz, Austria

    Jan Zenisek & Michael Affenzeller

Authors
  1. Jan Zenisek
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  2. Michael Affenzeller
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  3. Josef Wolfartsberger
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  4. Mathias Silmbroth
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  5. Christoph Sievi
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  6. Aziz Huskic
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  7. Herbert Jodlbauer
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Correspondence to Jan Zenisek .

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Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

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Zenisek, J. et al. (2018). Sliding Window Symbolic Regression for Predictive Maintenance Using Model Ensembles. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10671. Springer, Cham. https://doi.org/10.1007/978-3-319-74718-7_58

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  • DOI: https://doi.org/10.1007/978-3-319-74718-7_58

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  • Print ISBN: 978-3-319-74717-0

  • Online ISBN: 978-3-319-74718-7

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