IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency
Intelligent Methods for the Factory of the Future
Editors: Niggemann, Oliver, Schüller, Peter (Eds.)
- 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
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- About this book
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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.
- About the authors
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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.
- Table of contents (7 chapters)
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Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems
Pages 1-17
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Social Science Contributions to Engineering Projects: Looking Beyond Explicit Knowledge Through the Lenses of Social Theory
Pages 19-36
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Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps
Pages 37-54
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Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps
Pages 55-71
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A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes
Pages 73-91
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Table of contents (7 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency
- Book Subtitle
- Intelligent Methods for the Factory of the Future
- Editors
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- Oliver Niggemann
- Peter Schüller
- Series Title
- Technologien für die intelligente Automation
- Series Volume
- 8
- Copyright
- 2018
- Publisher
- Springer Vieweg
- Copyright Holder
- The Editor(s) (if applicable) and The Author(s)
- eBook ISBN
- 978-3-662-57805-6
- DOI
- 10.1007/978-3-662-57805-6
- Softcover ISBN
- 978-3-662-57804-9
- Series ISSN
- 2522-8579
- Edition Number
- 1
- Number of Pages
- VII, 129
- Number of Illustrations
- 23 b/w illustrations, 29 illustrations in colour
- Topics
