Graph Learning in Machine‐Readable Plant Topology Data
Abstract: Digitalization shows that data and its exchange are indispensable for a versatile and sustainable process industry. There must be a shift from a document‐oriented to a data‐oriented process industry. Standards for the harmonization of data structures play an essential role in this change. In engineering, DEXPI (Data Exchange in the Process Industry) is already a well‐developed, machine‐readable data standard for describing piping and instrumentation diagrams (P&ID). In this publication, industry, software vendors, and research institutions have joined forces to demonstrate the current developments and potentials of machine‐readable P&IDs in the DEXPI format combined with artificial intelligence. The aim is to use graph neural networks to learn patterns in machine‐readable P&ID data, which results in the efficient engineering and development of new P&IDs.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Graph Learning in Machine‐Readable Plant Topology Data ; day:03 ; month:05 ; year:2023 ; extent:13
Chemie - Ingenieur - Technik ; (03.05.2023) (gesamt 13)
- Urheber
- DOI
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10.1002/cite.202200223
- URN
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urn:nbn:de:101:1-2023050315194433665950
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:46 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Oeing, Jonas
- Brandt, Kevin
- Wiedau, Michael
- Tolksdorf, Gregor
- Welscher, Wolfgang
- Kockmann, Norbert
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