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Course paper / final thesis, Student placement, Internship

Knowledge-based methods for the parametrization of probabilistic expert systems for failure diagnostics of technical assets

Starting date

as of now

Duration of contract

6 Months (15-20 hrs/wk)

Remuneration

by agreement

Type of employment

Full-time

"Cutting-edge research requires excellent minds – particularly more females – at all levels. Launch your mission with us and send in your application now!" Prof. Pascale Ehrenfreund - Chair of the DLR Executive Board

Your mission:

Mobility has a high priority in our society. People want to reach their destination safely, comfortably and quickly. Goods must be transported cost-effectively over short and long distances. The consequences of mobility can be seen in environmental pollution, accidents and traffic jams, which increase with the ever-growing volume of traffic. These are the challenges we face at the Institute of Transportation Systems. We develop solutions for the safe and efficient mobility of the future.

The globalization of the economy and the increasing need for mobility are leading to an enormous increase in the volume of traffic, which is mainly absorbed by roads. In order to counter this trend, the competitiveness of rail as a mode of transport must be increased. The key to this is the economic and efficient use of rail networks and technical and operational interoperability. In the context of railway automation, we conduct research into the development and application of innovative technologies, methods and concepts for the railway system. In this way, we contribute to operational, technical and economic optimization. Our aim is to make rail transport safe, efficient and competitive and to promote European harmonization.

In the field of asset and system monitoring we are developing models for failure diagnostics of railway infrastructures (e.g., railway switches). By that, maintenance processes can be optimized and the availability of the infrastructure is maximized.
During your mission you will support us in the knowledge-based construction of such probabilistic models (Bayesian networks). You will review and develop methods for systematically collecting relevant parameters and information from experts. In this context, you will have to consider the challenges generated by the complexity of the assets as well as the practical difficulties of measuring subjective probabilities in expert interviews (consistency, accuracy, …). Using the example of a railway switch, you will be able to demonstrate and evaluate your final approach. With regard to the documentation of your results, you will organize them in the form of a report or thesis depending on your preferences.

Your qualifications:

  • Bachelor's degree (Traffic Engineering / Mechanical Engineering / Industrial Engineering / or similar)
  • Technical and mathematical comprehension
  • Grounded knowledge in stochastics (Conditional probabilities, Bayes' Theorem, …)
  • Very good language skills (English and/or German)
  • Motivation and ability to work in a team
  • Knowledge and experience in reliability analysis (FMEA, fault tree analysis, …) appreciated
  • Programming skills (preferably Python)

Your benefits:

Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (m/f/non-binary). Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

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Technical contact

Dr. Thorsten Neumann
Institute of Transportation Systems

Phone: +49 30 67055-208

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Vacancy 36875

HR department Braunschweig

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DLR site Braunschweig

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DLR Institute of Transportation Systems

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