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IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

Intelligent Methods for the Factory of the Future

  • Book
  • Open Access
  • © 2018

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Overview

  • Provides engineering-lean, unsupervised methods that scale in realistic scenarios
  • Helps to improve reliability and efficiency of complex systems
  • Presents examples and results from real factories and real cyber-physical systems

Part of the book series: Technologien für die intelligente Automation (TIA, volume 8)

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About this book

This open access work presents selected results from the European research and innovation project IMPROVE which yielded novel data-based solutions to enhance machine reliability and efficiency in the fields of simulation and optimization, condition monitoring, alarm management, and quality prediction.

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Keywords

Table of contents (7 chapters)

Editors and Affiliations

  • inIT - Institut für industrielle Informationstechnik, Hochschule Ostwestfalen-Lippe, Lemgo, Germany

    Oliver Niggemann

  • Institut für Logic and Computation, Vienna University of Technology, Wien, Austria

    Peter Schüller

About the editors

Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.



Dr. Peter Schüller is postdoctoral researcher at Technische Universität Wien. His research interests are hybrid reasoning systems that combine Knowledge Representation and Machine Learning and applications in the fields of Cyber-Physical systems and Natural Language Processing.









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