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Sliding Window Symbolic Regression for Predictive Maintenance Using Model Ensembles

  • Jan Zenisek
  • Michael Affenzeller
  • Josef Wolfartsberger
  • Mathias Silmbroth
  • Christoph Sievi
  • Aziz Huskic
  • Herbert Jodlbauer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10671)

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.

Notes

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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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jan Zenisek
    • 1
    • 2
  • Michael Affenzeller
    • 1
    • 2
  • Josef Wolfartsberger
    • 1
  • Mathias Silmbroth
    • 1
  • Christoph Sievi
    • 1
  • Aziz Huskic
    • 1
  • Herbert Jodlbauer
    • 1
  1. 1.Institute for Smart ProductionUniversity of Applied Sciences Upper AustriaSteyr, WelsAustria
  2. 2.Institute for Formal Models and VerificationJohannes Kepler University LinzLinzAustria

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