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

Intelligent Methods for the Factory of the Future

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  • Open Access
  • © 2018

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Overview

Editors:
  1. Oliver Niggemann

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

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  2. Peter Schüller

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

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  • 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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Table of contents (7 chapters)

  1. Front Matter

    Pages I-VII
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  2. Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems

    • Emanuel Trunzer, Simon Lötzerich, Birgit Vogel-Heuser
    Pages 1-17Open Access
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  3. Social Science Contributions to Engineering Projects: Looking Beyond Explicit Knowledge Through the Lenses of Social Theory

    • Peter Müller, Jan-Hendrik Passoth
    Pages 19-36Open Access
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  4. Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps

    • Alexander von Birgelen, Oliver Niggemann
    Pages 37-54Open Access
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  5. Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps

    • Alexander von Birgelen, Oliver Niggemann
    Pages 55-71Open Access
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  6. A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes

    • Stefan Windmann, Oliver Niggemann
    Pages 73-91Open Access
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  7. Validation of similarity measures for industrial alarm flood analysis?

    • Marta Fullen, Peter Schüller, Oliver Niggemann
    Pages 93-109Open Access
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  8. Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause

    • Paul Wunderlich, Oliver Niggemann
    Pages 111-129Open Access
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Keywords

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.

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.









Bibliographic Information

  • Book Title: IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

  • Book Subtitle: Intelligent Methods for the Factory of the Future

  • Editors: Oliver Niggemann, Peter Schüller

  • Series Title: Technologien für die intelligente Automation

  • DOI: https://doi.org/10.1007/978-3-662-57805-6

  • Publisher: Springer Vieweg Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s) 2018

  • Softcover ISBN: 978-3-662-57804-9Published: 31 August 2018

  • eBook ISBN: 978-3-662-57805-6Published: 20 August 2018

  • Series ISSN: 2522-8579

  • Series E-ISSN: 2522-8587

  • Edition Number: 1

  • Number of Pages: VII, 129

  • Number of Illustrations: 23 b/w illustrations, 29 illustrations in colour

  • Topics: Quality Control, Reliability, Safety and Risk, Robotics and Automation, Input/Output and Data Communications

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