Student (f / m / x) - Explainable AI for Deep Learning-based Remaining Useful Life Prediction

Welcome to the Institute of Flight Systems Engineering. Our work focuses on the interaction between aircraft configuration, pilots and modern flight system technology. From flight dynamics to unmanned aerial vehicles, from simulation to real flight tests - we analyse, test and develop innovations that will shape the flying of the future.

What to expect

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, e.g. by using acceleration measurements on the EMA housing. Due to continuous flight control surfaces adjustments 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 modelled using deep learning-based health indicators as a basis for the 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 such safety-critical systems. As part of a master's thesis, explainability approaches are therefore to be investigated.

Your tasks

• Literature review of existing deep learning-based health indicator construction methods
• Identificaiton, preprocessing and preparation of existing databases
• Implementation of explainable AI methods in Python for run-to-failure data sets of rotating ball bearings
• Visualization of the results
• Characterization of the degradation behavior and the inherent uncertainties of the estimation process

Your profile

We offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 2397) please contact :

Lauri Bodenröder

Tel. : 0531 295-1146