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  4. Implementing a Metadata Manager for Machine Learning with the Asset Administration Shell
 
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2022
Conference Paper
Title

Implementing a Metadata Manager for Machine Learning with the Asset Administration Shell

Abstract
With the rise of Industry 4.0, businesses are increasingly turning to Machine Learning to leverage data for improving quality and productivity. However, one open challenge when embracing Machine Learning in this context is the integration of cloud infrastructures, as well as the heterogeneity of data, interfaces, and protocols in the production environment. To address this, we are developing a framework that aims to simplify the adoption of Machine Learning techniques for heterogeneous industrial automation systems. One of the core features of this framework is the ability to handle data about production devices -- a scenario that is naturally suited to the use of Asset Administration Shells. However, the implementation of a system that uses Asset Administration Shells comes with its own set of challenges, such as the abstraction of details from users and the representation of device topologies. Thus, this paper introduces the concepts and implementation of a Metadata Manager component in the aforementioned framework that uses Asset Administration Shells as its basis. We further examine the Metadata Manager's current structure with unit testing, derive planned extensions, and discuss future directions from the Industry 4.0 perspective.
Author(s)
Sawczuk da Silva, Alexandre
Fraunhofer-Institut für Kognitive Systeme IKS  
Van, Hoai My  
Fraunhofer-Institut für Kognitive Systeme IKS  
Weiß, Gereon  
Fraunhofer-Institut für Kognitive Systeme IKS  
Mainwork
ETFA 2022, 27th International Conference on Emerging Technologies and Factory Automation  
Project(s)
Multi-Stage Automated Continuous Delivery for AI-based Software & Services Development in Industry 4.0
Funder
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie  
Conference
International Conference on Emerging Technologies and Factory Automation 2022  
Workshop on Implementing Asset Administration Shells 2022  
Open Access
DOI
10.1109/ETFA52439.2022.9921671
10.24406/h-428123
File(s)
Download (461.95 KB)
Rights
Under Copyright
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • asset administration shell

  • BaSyx

  • AASX Package Explorer

  • production plant metadata

  • digital twin

  • industry 4.0

  • Machine Learning

  • ML

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