IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : : Intelligent Methods for the Factory of the Future.
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Superior document: | Technologien Für Die Intelligente Automation Series ; v.8 |
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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
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Physical Description: | 1 online resource (132 pages) |
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Niggemann, Oliver. IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future. 1st ed. Berlin, Heidelberg : Springer Berlin / Heidelberg, 2018. ©2018. 1 online resource (132 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Technologien Für Die Intelligente Automation Series ; v.8 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Schüller, Peter. Print version: Niggemann, Oliver IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency Berlin, Heidelberg : Springer Berlin / Heidelberg,c2018 9783662578049 ProQuest (Firm) Technologien Für Die Intelligente Automation Series https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=5496000 Click to View |
language |
English |
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eBook |
author |
Niggemann, Oliver. |
spellingShingle |
Niggemann, Oliver. IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future. Technologien Für Die Intelligente Automation Series ; 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. |
author_facet |
Niggemann, Oliver. Schüller, Peter. |
author_variant |
o n on |
author2 |
Schüller, Peter. |
author2_variant |
p s ps |
author2_role |
TeilnehmendeR |
author_sort |
Niggemann, Oliver. |
title |
IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future. |
title_sub |
Intelligent Methods for the Factory of the Future. |
title_full |
IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future. |
title_fullStr |
IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future. |
title_full_unstemmed |
IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future. |
title_auth |
IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future. |
title_new |
IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : |
title_sort |
improve - innovative modelling approaches for production systems to raise validatable efficiency : intelligent methods for the factory of the future. |
series |
Technologien Für Die Intelligente Automation Series ; |
series2 |
Technologien Für Die Intelligente Automation Series ; |
publisher |
Springer Berlin / Heidelberg, |
publishDate |
2018 |
physical |
1 online resource (132 pages) |
edition |
1st ed. |
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. |
isbn |
9783662578056 9783662578049 |
callnumber-first |
T - Technology |
callnumber-subject |
TH - Building Construction |
callnumber-label |
TH9701-9745 |
callnumber-sort |
TH 49701 49745 |
genre |
Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=5496000 |
illustrated |
Not Illustrated |
oclc_num |
1049975285 |
work_keys_str_mv |
AT niggemannoliver improveinnovativemodellingapproachesforproductionsystemstoraisevalidatableefficiencyintelligentmethodsforthefactoryofthefuture AT schullerpeter improveinnovativemodellingapproachesforproductionsystemstoraisevalidatableefficiencyintelligentmethodsforthefactoryofthefuture |
status_str |
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ids_txt_mv |
(MiAaPQ)5005496000 (Au-PeEL)EBL5496000 (OCoLC)1049975285 |
carrierType_str_mv |
cr |
hierarchy_parent_title |
Technologien Für Die Intelligente Automation Series ; v.8 |
is_hierarchy_title |
IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future. |
container_title |
Technologien Für Die Intelligente Automation Series ; v.8 |
author2_original_writing_str_mv |
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