IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency
Intelligent Methods for the Factory of the Future
Published on 31. August 2018
Book
Paperback/Softback
VII, 129 pages
978-3-662-57804-9 (ISBN)
Description
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.
More details
Series
Edition
2018 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Illustrations
23 s/w Abbildungen, 29 farbige Abbildungen
VII, 129 p. 52 illus., 29 illus. in color.
Dimensions
Height: 240 mm
Width: 168 mm
Thickness: 8 mm
Weight
248 gr
ISBN-13
978-3-662-57804-9 (9783662578049)
DOI
10.1007/978-3-662-57805-6
Schweitzer Classification
Persons
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.
Content
Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems.- Social Science Contributions to Engineering Projects: Looking Beyond Explicit Knowledge Through the Lenses of Social Theory.- Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps.- Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps.- A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes.- Validation of similarity measures for industrial alarm flood analysis.- Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause.