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

Bibliographic Details
Main Author: Niggemann, Oliver.
Other Authors: Schüller, Peter.
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin / Heidelberg, 2018.
Edition:1st ed.
Series:Technologien Für Die Intelligente Automation Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • Table of Contents
  • 1 Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems. Utilization of Industrie 4.0 Technologies for Simplifying Data Access
  • 1 Introduction and Motivation
  • 2 Requirements for a System Architecture to Support Industrie 4.0 Principles
  • 3 State-of-the-Art of Industrie 4.0 System Architectures
  • 4 Concept of a Unified Data Transfer Architecture (UDaTA) in Automated Production Systems
  • 5 Evaluation
  • 5.1 Expert Evaluation
  • 5.2 Prototypical Lab-Scale Implementation
  • 6 Conclusion and Outlook
  • Acknowledgment
  • References
  • 2 Social Science Contributions to Engineering Projects: Looking Beyond Explicit Knowledge Through the Lenses of Social Theory
  • 1 Introduction
  • 2 Introducing our role(s) as social science researchers
  • 2.1 What do social scientists do?
  • 2.2 What did we do as IMPROVE (social science) researchers?
  • 3 Empirical findings on socio-technical arrangements in HMI supported operating of smart factory plants
  • 4 Social Theory Plugins
  • 4.1 A systems theory of (smart) factories
  • 4.2 Tacit knowledge beyond explicity
  • 4.3 Conceptualizing human-machine agency
  • 5 Summary and outlook
  • Acknowledgments
  • References
  • 3 Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps
  • 1 Introduction
  • 2 Methodologies
  • 2.1 Hybrid Timed Automata
  • 2.2 Self-Organizing Map
  • 2.3 Watershed Transformation
  • 3 Learning hybrid timed automata without discrete events
  • 4 Experiments
  • 4.1 Artificial test data
  • 4.2 High Rack Storage System
  • 4.3 Film-Spool Unwinder
  • 5 Conclusion
  • Acknowledgments.
  • References
  • 4 Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps
  • 1 Introduction
  • 2 Self-Organizing Map.
  • 2.1 Anomaly detection with quantization error
  • 2.2 Localization of anomalies
  • 2.3 SOM trajectory tracking with timed automata
  • 3 Experiments
  • 3.1 Quantization error anomaly detection and anomaly localization
  • 3.2 Trajectory tracking with automata
  • 4 Conclusion
  • Acknowledgments.
  • References
  • 5 A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes
  • 1 Introduction
  • 2 Fault detection with stochastic process models
  • 3 Fault detection for application cases with noisy measurements
  • 3.1 Probability density models
  • 3.2 Particle filter based fault detection
  • 3.3 Parallel implementation
  • 4 Evaluation and Discussion
  • 4.1 Fault detection results
  • 4.2 Runtime analysis
  • 5 Conclusion
  • Acknowledgments.
  • Appendix A: Fault detection for observable process variables
  • Appendix B: Metropolis Resampling
  • References
  • 6 Validation of similarity measures for industrial alarm flood analysis
  • 1 Introduction
  • 2 Clustering methodology
  • 2.1 Alarm log acquisition
  • 2.2 Flood detection and preprocessing
  • 2.3 Alarm flood clustering
  • 2.4 Distance matrix postprocessing
  • 3 Evaluation methodology
  • 3.1 Synthetic flood generation
  • 3.2 Cluster Membership of Synthetic Floods
  • 3.3 Cluster Stability
  • 4 Empirical evaluation results
  • 4.1 Visualization on a demonstrative set of 25 floods
  • 4.2 Clustering with synthetic floods on the full dataset
  • 5 Conclusion
  • Acknowledgement
  • References
  • 7 Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause
  • 1 Introduction
  • 2 State of the Art of Alarm Management
  • 3 Knowledge Representation
  • 4 Concept for Alarm Flood Reduction
  • 4.1 Learning Phase
  • 4.2 Operation Phase
  • 5 Conclusion
  • Acknowledgment
  • References.

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