Sten Grüner, Mario Hoernicke, Katharina Stark, N. Schoch, Nafise Eskandani, J. Pretlove
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引用次数: 0
Abstract
Abstract In this work we emphasize the role of the engineering process within the industrial automation domain and its underrepresentation in the Industry 4.0 community possibly explained by discrete roots of Industry 4.0. Towards this aim, we revisit the value chain on the leading picture of Industry 4.0 and indicate gaps for “design-to-order” products and projects where requirement artifacts like Piping and Instrumentation Diagrams (P&IDs) or tag lists are exchanged prior to selecting, ordering and building the actual plant. After the understanding of the importance of the engineering process, we explore the opportunities of using Industry 4.0 technology stack to embed engineering information into the digital twin of a process plant. We underline possible synergies with the current developments of the Industry 4.0 community, like the Data Exchange in the Process Industry (DEXPI) and the Module Type Package (MTP) submodel template definitions. Finally, we present possible best practices for embedding existing engineering-related standards into the Industry 4.0 ecosystem and propose tactics and mechanisms for information modeling to accomplish this task in a most efficient and reusable way.
期刊介绍:
Automatisierungstechnik (AUTO) publishes articles covering the entire range of automation technology: development and application of methods, the operating principles, characteristics, and applications of tools and the interrelationships between automation technology and societal developments. The journal includes a tutorial series on "Theory for Users," and a forum for the exchange of viewpoints concerning past, present, and future developments. Automatisierungstechnik is the official organ of GMA (The VDI/VDE Society for Measurement and Automatic Control) and NAMUR (The Process-Industry Interest Group for Automation Technology).
Topics
control engineering
digital measurement systems
cybernetics
robotics
process automation / process engineering
control design
modelling
information processing
man-machine interfaces
networked control systems
complexity management
machine learning
ambient assisted living
automated driving
bio-analysis technology
building automation
factory automation / smart factories
flexible manufacturing systems
functional safety
mechatronic systems.