Tool Chain to Extract and Contextualize Process Data for AI Applications

Summarizing a key use case of a research workstream of the German publicly funded KEEN project, methods and tool chains are demonstrated to extract and to contextualize process data in an automated way based on engineering information. The contextualized process data serves as a high‐quality data so...

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Bibliographic Details
Published inChemie ingenieur technik Vol. 95; no. 7; pp. 1070 - 1076
Main Authors Sherpa, Lincoln, Müller-Pfefferkorn, Ralph, Enste, Udo, Tolksdorf, Gregor, Kawohl, Michael, Wiedau, Michael
Format Journal Article
LanguageEnglish
Published 01.07.2023
Subjects
Artificial intelligence
Data contextualization
Data management
Process industry
Tool chain
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Summary:Summarizing a key use case of a research workstream of the German publicly funded KEEN project, methods and tool chains are demonstrated to extract and to contextualize process data in an automated way based on engineering information. The contextualized process data serves as a high‐quality data source for machine learning methods. The article covers the applied basic methodical approaches, design decisions and the results of a successful pilot installation of the developed tool chain. For the application of AI in the process industry, contextualized data is of added value. By building‐up a tool chain from source systems providing structural and raw data up to dashboards visualizing key performance indicators of production sites, many steps of the contextualization and AI application for relevant use‐cases can be automatized.
ISSN:0009-286X
1522-2640
DOI:10.1002/cite.202300004