BMGF joint project AIMEE

AI based Monitoringand ExperimentalEvaluation

Modern test benches for experimental validation provide extremely large, mostly time-based and usually heterogeneous amounts of data, the processing and, above all, the evaluation of which are hardly possible in an all-encompassing manner using conventional knowledge-based methods or, due to the size and complexity, not possible at all. In particular, correlations and error models that are not yet known thus elude analysis. For the use of AI methods, e.g. from the field of Deep Learning, this offers an extraordinarily interesting, still little researched and above all future-relevant application.

At the University of Kassel, a new type of high-performance test bench for electrical machines is currently under construction, which will use extensive measurement technology to provide very large heterogeneous data volumes of both time-based electromagnetic, electrical, acoustic, mechanical and thermal variables, as well as non-time-based, characterizing or parametric data, and will also allow measurements in previously little researched borderline areas.

The BMBF joint project AIMEE addresses the processing and evaluation of heterogeneous, high-volume datasets from the testbed in order to make the applicability of innovative methods directly experienceable and interpretable for students by means of practical examples in the AI laboratory. This creates the necessary prerequisite for learners (undergraduates, graduate students, and professionals in continuing education programs) to study and apply the various AI methods with extensive and defined data sets using practical examples.  In addition, the highly interesting and rare opportunity for teaching and research opens up to create new data sets at will and to adapt the boundary conditions of data creation, i.e. measurement runs and structure, to the requirements of AI methods and method development. The close cooperation of software, hardware and application result in an excellent basis for teaching, are of great interest for the regional economy and have a high scientific connectivity.

01.11.2019 - 31.10.2021

• Department of Vehicle Systems and Fundamentals of Electrical Engineering, Prof. L. Brabetz (project coordination)
• Department of Intelligent Embedded Systems, Prof. B. Sick
• Department of Digital Technology, Prof. P. Zipf

German Aerospace Center e.V., DLR Project Management Agency, Berlin

Federal Ministry
for Education
and Research