Comprehensive understanding and the interpretation of shared data poses a significant challenge for involved parties having different backgrounds. To overcome this, a metadata schema called ProMetaS was created. The development process of ProMeta...
Artikel
Tool Chain to Extract and Contextualize Process Data for AI Applications
Von Wiley-VCH zur Verfügung gestellt
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.
Abstract
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.
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