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 is hardly possible with conventional knowledge-based methods or, due to the size and complexity, not possible at all. In particular, unknown correlations and error models elude analysis. For the use of AI methods, e.g. from the field of deep learning, this offers an extremely interesting, still little researched and, above all, future-relevant application.

A new, high-performance test bench for electric drive machines has recently been in use at the University of Kassel, which uses extensive measurement technology to generate very large heterogeneous data volumes of time-based electromagnetic, electrical, acoustic, mechanical and thermal variables, as well as non-time-based, characterizing or parametric data, and also allows measurements in previously little-researched border 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, doctoral 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.