Researchers in the High-Voltage Engineering Division of the Faculty of Electrical Engineering and Computer Science conduct numerous experiments every day to evaluate the properties of electrically insulating materials and components. What has often delighted visitors to cultural events in the hall—namely, the vivid effects of dynamic high-voltage experiments—can lead to problems, particularly when direct current is passed through the insulators. Interesting arcs are created, but in this context, they are harmful to the material.

How can this “big data” be analyzed? Daniel Fiß from the Institute for Process Engineering, Process Automation, and Measurement Technology aims to develop a closed mathematical method based on fuzzy set theory for analyzing experimental data while accounting for uncertainties. The future tool will then provide insights into the reliability of the data.

This is a promising collaboration that will also enable the professional development of the two staff members, Stefan Kühnel (EI) and Daniel Fiß (IPM). The staff members and the participating professors, Stefan Kornhuber (EI) and Alexander Kratzsch (IPM), met for a kickoff meeting. The project was titled: “Contribution to the Further Development and Use of Big Data Analysis Methods for Transient Measurement Data in Process Engineering.”

The joint project is supported by Zittau/Görlitz University of Applied Sciences with funds intended to improve basic research infrastructure.