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.
|Number of pages
|Chemie Ingenieur Technik
|Published - 21 Apr 2023
Research priority areas of TU Dresden
Subject groups, research areas, subject areas according to Destatis
ASJC Scopus subject areas
- artificial intelligence, Data contextualization, Data management, Process industry, Tool chain, Artificial intelligence