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  4. Historical Data Storage Architecture Blueprints for the Asset Administration Shell
 
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2022
Conference Paper
Title

Historical Data Storage Architecture Blueprints for the Asset Administration Shell

Abstract
The concept of the Digital Twin (DT) and its implementation as Asset Administration Shell (AAS) is one of the key technologies for implementing Industry 4.0. By utilizing the AAS, use cases can be implemented with high interoperability. These use cases include optimizations and improvements of physical systems, like predictive maintenance or other artificial intelligence applications. For these use cases, historical data is key. However, there exists no guidance on how to handle historical data with the AAS. Furthermore, no integrated architecture exists that enables seamless storage and retrieval of historical data using the unified AAS meta-model and interface. Thus, we bridge this gap between use case and unified implementation by presenting multiple blueprints for data storage and retrieval motivated by use cases from Industry 4.0, healthcare, and civil construction. These data storage blueprints range from a mandatory change in the AAS infrastructure to augmentations of the existing AAS concepts. Furthermore, we showcase how the data model of the AAS can be utilized for a unified retrieval of historical data. In consequence, practitioners can quickly realize use cases that require historical data by tailoring and implementing the presented blueprints. Additionally, researchers can extend the presented guidance to further use cases, possibly from other domains.
Author(s)
Fischer, Rene-Pascal
Fraunhofer-Institut für Experimentelles Software Engineering IESE  
Schnicke, Frank  
Fraunhofer-Institut für Experimentelles Software Engineering IESE  
Beggel, Bastian
Oliveira Antonino, Pablo
Fraunhofer-Institut für Experimentelles Software Engineering IESE  
Mainwork
ETFA 2022, 27th International Conference on Emerging Technologies and Factory Automation  
Conference
International Conference on Emerging Technologies and Factory Automation 2022  
DOI
10.1109/ETFA52439.2022.9921613
Language
English
Fraunhofer-Institut für Experimentelles Software Engineering IESE  
Keyword(s)
  • Architecture

  • Asset Administration Shell

  • Data Storage

  • Digital Twins

  • Historical Data

  • Industry 4.0

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