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Modeling Sensor Networks for Predictive Maintenance

  • Jan ZenisekEmail author
  • Josef Wolfartsberger
  • Christoph Sievi
  • Michael Affenzeller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11231)

Abstract

Predictive Maintenance is one of the most intensively investigated topics in the current Industry 4.0 movement. It aims at scheduling maintenance actions based on industrial production plants’ past and current condition and therefore incorporates other trending technological developments such as the Internet of Things, Cyber-Physical Systems or Big Data Analytics. In this short paper we motivate the employment of machine learning algorithms to detect changing behavior as indication for the necessity of maintenance on a microscopic level and describe how cyber-physical environments benefit from this approach.

Keywords

Predictive maintenance Machine learning Digital twin 

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”.

References

  1. 1.
    Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. CRC Press, Boca Raton (2009)CrossRefGoogle Scholar
  2. 2.
    Ahmed, N.K., Atiya, A.F., Gayar, N.E., El-Shishiny, H.: An empirical comparison of machine learning models for time series forecasting. Econ. Rev. 29(5–6), 594–621 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Kronberger, G., Fink, S., Kommenda, M., Affenzeller, M.: Macro-economic time series modeling and interaction networks. In: Di Chio, C., et al. (eds.) EvoApplications 2011. LNCS, vol. 6625, pp. 101–110. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-20520-0_11CrossRefGoogle Scholar
  4. 4.
    Lee, J., Kao, H.A., Yang, S.: Service innovation and smart analytics for industry 4.0 and big data environment. Proc. Cirp 16, 3–8 (2014)CrossRefGoogle Scholar
  5. 5.
    Saxena, A., Goebel, K., Simon, D., Eklund, N.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: International Conference on Prognostics and Health Management, pp. 1–9. IEEE (2008)Google Scholar
  6. 6.
    Zenisek, J., et al.: Sliding window symbolic regression for predictive maintenance using model ensembles. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2017. LNCS, vol. 10671, pp. 481–488. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-74718-7_58CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jan Zenisek
    • 1
    • 2
    Email author
  • Josef Wolfartsberger
    • 1
  • Christoph Sievi
    • 1
  • Michael Affenzeller
    • 1
    • 2
  1. 1.Institute for Smart ProductionUniversity of Applied Sciences Upper Austria, Campus HagenbergHagenbergAustria
  2. 2.Institute for Formal Models and VerificationJohannes Kepler University LinzLinzAustria

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