Robert-Bosch-Campus 1 Robert Bosch GmbH 71272Renningen
Hybrid machine learning models for early detection and prediction of rolling bearing damage
Due to high loads, rolling bearings are the components that limit the service life in a large number of machines. Therefore, condition monitoring of bearings is useful in many cases. For this purpose, measurement data is recorded and analysed with the help of evaluation methods. So-called hybrid models combine machine learning and physics-based methods. Here, the research objective is to develop hybrid models for damage detection and remaining useful life prediction of rolling bearings.
PhD student at Robert Bosch GmbH in cooperation with the Institute for Product Development and Machine Elements
10/2020 – 12/2020
Project-related work as a Software Engineer at the engineering office dwsquare PartG mbB
04/2018 – 10/2020
Master's studies Mechanical and Process Engineering at the TU Darmstadt, topic of the master's thesis: Development of a simulation model for calculating the damage-relevant effects of an unlubricated rolling contact using the example of a track roller
10/2017 – 03/2018
Intern at Robert Bosch GmbH: Structural dynamics and acoustics simulations of electric motors
10/2014 – 09/2017
Bachelor studies Mechanical and Process Engineering at the TU Darmstadt, topic of the bachelor thesis: Analysis of cage movement in rolling bearings via laser sensing
08/2006 – 06/2014
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