Student Mechanical Engineering, Computer Science, Safety Engineering or similar (f/m/x)

Student Mechanical Engineering, Computer Science, Safety Engineering or similar (f/m/x)

For our Institut of Flight Systems in Braunschweig, we are looking for a Student in Mechanical Engineering, Computer Science, Safety Engineering, or a similar field (f/m/x) to work on Explainable AI for Deep Learning-based health indicator construction for RUL prediction on ball bearings.

Condition monitoring systems for safety-critical flight control components are being developed at DLR's Institute of Flight Systems in Braunschweig. To predict the Remaining Useful Life (RUL) of electromechanical flight control actuators (EMA), it is necessary to monitor mechanical components such as the ball bearings, using acceleration measurements on the EMA housing. Due to continuous adjustments of flight control surfaces combined with excessive loads during flight operation, the degradation behavior of the ball bearings becomes apparent in the monitored data. This degradation can then be modeled using deep learning-based health indicators as a basis for RUL prediction. However, due to the black box characteristics of deep learning models, the trustworthiness of the results is limited. Improving this trustworthiness is particularly relevant for safety-critical systems.

As part of a master's thesis, explainability approaches are to be investigated to improve the trustworthiness of the results for deep learning-based health indicator construction methods. A literature review of existing deep learning-based health indicator construction methods for ball bearings and applicable explainable AI methods shall be carried out first. Subsequently, the explainable AI methods shall be implemented in Python on run-to-failure data sets for rotating ball bearings, and the results shall be visualized. The aim is to better understand the degradation behavior of the ball bearings and the inherent uncertainties, and to increase the accuracy of the modeled health indicator.

In our department, you will be part of a dynamic and scientifically innovative team. You will benefit from the existing expertise and infrastructure and contribute to its continuous development. In addition to your thesis, employment on a part-time basis is possible. Do you have the necessary degree of personal responsibility and share our high standards for the scientific quality of your work? We offer you the ideal environment for personal and professional development at an internationally high level.

• Current enrollment in a Master’s program in Mechanical Engineering, Computer Science, Safety Engineering, or a related field.
• Good programming skills in Python.
• Good knowledge in the field of artificial intelligence.
• Very good English language skills.
• Vergütung: Depending on qualifications and assignment of tasks, up to pay group E5 TVöD.