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Book/Printed Material IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future

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About this Item

Title

  • IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future

Summary

  • 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.

Names

  • Niggemann, Oliver. editor
  • Schüller, Peter. editor

Created / Published

  • Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer Vieweg, 2018.

Contents

  • 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.

Headings

  • -  Automation
  • -  Industrial safety
  • -  Input-output equipment (Computers)
  • -  Quality control
  • -  Reliability
  • -  Robotics
  • -  Quality Control, Reliability, Safety and Risk
  • -  Input/Output and Data Communications
  • -  Robotics and Automation

Notes

  • -  Description based on publisher-supplied MARC data.
  • -  Engineering (R0) (SpringerNature-43712)
  • -  Engineering (SpringerNature-11647)

Medium

  • 1 online resource (VII, 129 pages 52 illustrations, 29 illustrations in color.)

Digital Id

Library of Congress Control Number

  • 2019742942

Rights Advisory

Access Advisory

  • Unrestricted online access

Online Format

  • image
  • epub

Additional Metadata Formats

Rights & Access

The books in this collection are licensed under open access licenses allowing for the reuse and distribution of each book following the terms described in each license. Researchers should consult the Rights Advisory statement for each title and the accompanying license details for information about rights and permissions associated with each of the licenses.

More about Copyright and other Restrictions.

Cite This Item

Citations are generated automatically from bibliographic data as a convenience, and may not be complete or accurate.

Chicago citation style:

Niggemann, Oliver. Editor, and Peter. Editor Schüller. IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency: Intelligent Methods for the Factory of the Future. Berlin, Heidelberg: Springer Berlin Heidelberg: Imprint: Springer Vieweg, 2018. Image. https://aj.sunback.homes/item/2019742942/.

APA citation style:

Niggemann, O. E. & Schüller, P. E. (2018) IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency: Intelligent Methods for the Factory of the Future. Berlin, Heidelberg: Springer Berlin Heidelberg: Imprint: Springer Vieweg. [Image] Retrieved from the Library of Congress, https://aj.sunback.homes/item/2019742942/.

MLA citation style:

Niggemann, Oliver. Editor, and Peter. Editor Schüller. IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency: Intelligent Methods for the Factory of the Future. Berlin, Heidelberg: Springer Berlin Heidelberg: Imprint: Springer Vieweg, 2018. Image. Retrieved from the Library of Congress, <aj.sunback.homes/item/2019742942/>.