Bachelor’s thesis / Master’s thesis: Data-driven prediction of the risk of failure of planetary journal bearings in wind turbines based on surface acoustic wave and temperature measurements

The Chair for Wind Power Drives (CWD) researches the behavior of drive systems in modern multi-megawatt wind turbines (WTs). Research objectives include increasing the availability, robustness, and energy efficiency of WTs, as well as reducing the levelized cost of electricity. To achieve this, state-of-the-art engineering software and system test benches are utilized.

To reduce the levelized cost of electricity in modern WTs, efforts are being made to increase the power density of planetary gearboxes in wind turbines. The use of journal bearings, which have been used in industry for several years, is a suitable option for this purpose. However, there is currently no real-time condition monitoring system (CMS) capable of detecting and thus avoiding operating points with a high risk of failure for journal bearings at an early stage. The CWD is therefore conducting research into a modern CMS network. Preliminary work has shown that surface acoustic wave (SAW) and temperature sensors can reliably detect mixed friction. Building on this measurement technology and existing algorithms, further development is now planned with the aim of industrial applicability.

The idea behind this student project is to develop a resource-efficient monitoring algorithm that processes measurement results from various sensors (SAW, temperature, etc.) in real time in order to estimate the risk of failure in journal plain bearings.

Tasks:

• Research on the state of the art and familiarization with  the method
• Support in the planning and execution of experiments on a component test rig (three-wheel chain)
• Analysis of the obtained measurement data
• Development of an algorithm to estimate the risk of failure of planetary journal bearings

Requirements:

• Motivation to work independently and on one’s own responsibility, ability to communicate and work in a team, as well as a secure command of the German or English language
• Interest in wind energy, gearbox, and journal bearing technology
• Programming experience in Python is desirable

What we offer:

• Scientific work in a highly motivated, interdisciplinary team
• Work on a topic with high industrial relevance
• Exciting combination of theory and practice
• Pleasant working atmosphere and intensive supervision
• Option to participate in a scientific publication
• Immediate start possible

Auf deine aussagekräftige Bewerbung per E-Mail freut sich:

Tim Scholz, M. Sc. RWTH
Chair for Wind Power Drives

Campus-Boulevard 61, 52074 Aachen
tim.scholz@cwd.rwth-aachen.de