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Development of a Machine Learning Method to Calculate Real-time 3D Deflections in a Dynamic Magnetic Field

Development of a Machine Learning Method to Calculate Real-time 3D Deflections in a Dynamic Magnetic Field
Typ:Masterarbeit
Datum:sofort
Betreuer:

Martin, Dominik

Links:Ausschreibung

Trelleborg is developing a contactless 3D deflection measuring system. The system allows capturing static and dynamic deflections as a basis for complex condition monitoring and predictive maintenance approaches – especially in railway applications.

The task contains:

  • Development of a Machine Learning model which is able to infer 3D deflections by combining different sensor signals 
  • Gaining knowledge of the physical system behavior as well as correlations in the sensor values
  • Validating approach on laboratory tests and benchmarks 

Current status:

  • First prototype of sensor hardware available and tested
  • Extensive datasets for analysis available
  • Improved sensor hardware available in 08/2019
  • First approach of algorithm as base for improvements / benchmarking
  • Wide range of tools and hardware for additional tests available

Goals:

  • Real-time capable prediction model / algorithm
  • Measuring accuracy of 0.1 mm
  • Development of proper calibration and commissioning processes 

If you are interested, send a short letter of motivation, your CV and a transcript of records to dominik.martin∂kit.edu.