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

• Superior document: Technologien Für Die Intelligente Automation Series ; v.8
• Author: Niggemann, Oliver.
• TeilnehmendeR: Schüller, Peter.
• Place / Publishing House: Berlin, Heidelberg : : Springer Berlin / Heidelberg,, 2018.; ©2018.
• Year of Publication: 2018
• Edition: 1st ed.
• Language: English
• Series: Technologien Für Die Intelligente Automation Series
• Physical Description: 1 online resource (132 pages)

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.