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Machine Learning for Process-X: A Taxonomy

  • Felix Reinhart
  • Sebastian von Enzberg
  • Arno Kühn
  • Roman Dumitrescu
Conference paper
Part of the Technologien für die intelligente Automation book series (TIA, volume 11)

Abstract

Application of machine learning techniques for data-driven modeling of value-creating processes promises significant economic benefits. These applications include process monitoring, process configuration, process control and process optimization (process-X). However, similarities and distinguishing features between established approaches to process-X compared to machine learning are often unclear. This paper sheds light on this issue by deriving a taxonomy of process-X approaches that sharpens the role of machine learning in these applications. Moreover, the taxonomy and discussion identifies future research directions for applied machine learning in cyber-physical systems.

Keywords

Machine Learning Data Analytics Statistical Process Control Control Theory Virtual Sensors Expert systems 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Felix Reinhart
    • 1
  • Sebastian von Enzberg
    • 1
  • Arno Kühn
    • 1
  • Roman Dumitrescu
    • 1
  1. 1.Fraunhofer IEM Institute for Mechatronic Systems DesignPaderbornGermany

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