Table of contents

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.

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.


Factory Automation Machine reliability and efficiency Cyber-physical systems Machine Learning Simulation & optimization Condition monitoring Alarm management Quality prediction Lean Engineering Open Access

Editors and affiliations

  • Oliver Niggemann
    • 1
  • Peter Schüller
    • 2
  1. 1.inIT - Institut für industrielle InformationstechnikHochschule Ostwestfalen-LippeLemgoGermany
  2. 2.Institut für Logic and ComputationVienna University of TechnologyWienAustria

Bibliographic information

  • DOI
  • Copyright Information The Editor(s) (if applicable) and The Author(s) 2018
  • License CC BY
  • Publisher Name Springer Vieweg, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-662-57804-9
  • Online ISBN 978-3-662-57805-6
  • Series Print ISSN 2522-8579
  • Series Online ISSN 2522-8587
  • Buy this book on publisher's site