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)
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Front Matter
Editors and Affiliations
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.
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
Industry Sectors: Aerospace, Chemical Manufacturing, Consumer Packaged Goods, Electronics, Energy, Utilities & Environment, Engineering, IT & Software, Materials & Steel, Oil, Gas & Geosciences