Master thesis: Development of an AI-based approach for the condition monitoring of plain bearings in innovative wind turbine drive trains

Webseite Chair for Wind Power Drives

The Chair for Wind Power Drives researches the behavior of drive systems in modern multi-megawatt wind turbines. The research objectives are to increase the availability, robustness and energy efficiency of wind turbines and to reduce the cost of electricity. For this purpose, software development tools and modern system test benches are used in combination.
In order to reduce the electricity generation costs of modern wind turbines (WT), the power density of the planetary gearboxes in WT is to be increased. For this purpose, the use of compact planetary gears with plain bearings is a suitable solution. Therefore, research is being carried out at the CWD on the use of plain bearing of planetary gears in wind turbine gearboxes. A major challenge is the fail-safe operation of the plain bearings. This requires the best possible knowledge of the bearing and operating condition, which can be recorded by the sensors of a condition monitoring system (CMS). At present, no market-ready CMS are available for plain bearing applications in wind turbines.
Therefore, the aim of this work is the development of a CMS approach for journal bearings based on structure-borne noise and temperature measurements. For the evaluation of the measurement results, the suitability of artificial neural networks is to be evaluated.

Tasks:

  • Research of the state of the art
  • Execution of vibration and temperature measurements on a plain bearing test rig
  • Evaluation of the measurement data and classification of the operating conditions by means of EHD simulations
  • Development of an AI-based approach for the detection of critical operating conditions using neural networks

Your profile:

  • Motivation to work independently and on one’s own responsibility, ability to communicate and work in a team, as well as a confident command of the English language
  • Geübter Umgang mit Matlab und/oder Python
  • Vorkenntnisse im Bereich Neuronale Netze sind wünschenswert, aber nicht zwingend erforderlich

We offer:

  • Scientific work in a highly motivated, interdisciplinary team
  • A bachelor thesis with industry-related topics
  • Practical work on a bearing test rig
  • Scientific work in a highly motivated, interdisciplinary team
  • Pleasant working atmosphere and intensive supervision

 

We look forward to your application by email:

Thomas Decker, M. Sc. RWTH
Chair for Wind Power Drives

Campus-Boulevard 61, 52074 Aachen
thomas.decker@cwd.rwth-aachen.de

Um sich für diesen Job zu bewerben, sende deine Unterlagen per E-Mail an thomas.decker@cwd.rwth-aachen.de