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IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

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Cover of 'IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency'

Table of Contents

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    Book Overview
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    Chapter 1 Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems
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    Chapter 2 Social Science Contributions to Engineering Projects: Looking Beyond Explicit Knowledge Through the Lenses of Social Theory
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    Chapter 3 Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps
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    Chapter 4 Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps
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    Chapter 5 A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes
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    Chapter 6 Validation of similarity measures for industrial alarm flood analysis?
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    Chapter 7 Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause
Attention for Chapter 7: Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause
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Citations

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3 Mendeley
Summary Dimensions citations
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Chapter title
Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause
Chapter number 7
Book title
IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency
Published by
Springer Vieweg, Berlin, Heidelberg, January 2018
DOI 10.1007/978-3-662-57805-6_7
Book ISBNs
978-3-66-257804-9, 978-3-66-257805-6
Authors

Paul Wunderlich, Oliver Niggemann, Wunderlich, Paul, Niggemann, Oliver

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 33%
Unknown 2 67%
Readers by discipline Count As %
Engineering 1 33%
Unknown 2 67%

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