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

Deutsches Zentrum für Luft- und Raumfahrt (DLR); Wolfsburg, Göttingen, Hannover; Vor Ort; EUR 30.000 - 50.000; Teilzeit

Zusammenfassung

Ein innovatives Forschungsinstitut sucht einen Masterstudenten in Maschinenbau, Informatik oder Sicherheitsingenieurwesen, um an erklärbaren KI-Methoden für die Gesundheitsindikator-Konstruktion zu arbeiten. Diese spannende Position bietet die Möglichkeit, tiefgehende Einblicke in die Zuverlässigkeit von Deep Learning-Methoden zu gewinnen und deren Anwendung auf sicherheitskritische Systeme zu erforschen. Sie werden Teil eines dynamischen Teams, das an der Spitze der technologischen Entwicklungen steht, und profitieren von einem inspirierenden Umfeld, das Ihre persönliche und berufliche Entwicklung fördert.

Qualifikationen

• Aktuelle Immatrikulation in einem Master-Studiengang in Maschinenbau oder Informatik.
• Gute Programmierkenntnisse in Python und Kenntnisse in KI.

Aufgaben

• Untersuchung von Erklärbarkeitsansätzen zur Verbesserung der Vertrauenswürdigkeit von Ergebnissen.
• Durchführung einer Literaturrecherche zu bestehenden Methoden der Gesundheitsindikator-Konstruktion.

Kenntnisse

Python Programmierung

Künstliche Intelligenz

Englischkenntnisse

Ausbildung

Master in Maschinenbau

Master in Informatik

Master in Sicherheitsingenieurwesen

Jobbeschreibung

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.