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

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

Job Description

Req ID: 2397

Place of work: Braunschweig

Starting date: ab sofort

Career level: Student employment, Student research project and final thesis

Type of employment: Part time

Duration of contract: 6 Monate

Remuneration: Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund)

Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e. V.; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our 11,000 employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge? Then this is your place!

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
• 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
• good knowledge in the field of mechatronic systems and their dynamic behavior
• very good English language skills

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