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A Generic DigitalTwin Model for Artificial Intelligence Applications

: Niggemann, Oliver; Diedrich, Alexander; Kühnert, Christian; Pfannstiel, Erik; Schraven, Joshua

Postprint urn:nbn:de:0011-n-6391823 (472 KByte PDF)
MD5 Fingerprint: ed7db4b0bed5d755703cb973deafdaa4
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Erstellt am: 3.9.2021

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
4th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2021. Proceedings : Online, 10 - 13 May 2021, Victoria, BC, Canada
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-6654-3045-6
ISBN: 978-1-7281-6207-2
International Conference on Industrial Cyber-Physical Systems (ICPS) <4, 2021, Online>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
artificial intelligence applications; cyber-physical system; machine learning; generic DigitalTwin model; AI reference model; AITwin

Services for Cyber-Physical Systems based on Artificial Intelligence and Machine Learning require a virtual representation of the physical. To reduce modeling efforts and to synchronize results, for each system, a common and unique virtual representation used by all services during the whole system life-cycle is needed-i.e. a DigitalTwin. In this paper such a DigitalTwin, namely the AI reference model AITwin, is defined. This reference model is verified by using a running example from process industry and by analyzing the work done in recent projects.