Context – Condition monitoring of gears is extremely important in a number of industrial applications including wind turbines and automotive. A plethora of diagnostics indicators, based on signal processing and machine learning applied on indirect measurement techniques (e.g. acceleration, sound, rotational speed, etc.) have been proposed to monitor the damage evolution in gears but rarely are compared with a direct damage measurement based on camera images. In this project simultaneous direct (via a visual sensor) and indirect measurements will be captured and the effectiveness of novel indirect diagnostic indicators will be evaluated based on the ground truth.

Your tasks – The envisioned Ph.D. focuses on the development of a condition monitoring strategy for lubricated gears based on a visual camera-based system. The Ph.D. student will be involved in the design of the setup, data acquisition, labeling, and interpretation of the data.

To apply, please complete the application form at https://forms.gle/Ksvrdt3nx38xKGvT8

1. You hold a Master's degree in (Electro-)Mechanical Engineering.
2. You have a strong motivation for conducting scientific research at a high level.
3. You possess good analytical and technical skills
4. You have an affinity with Tribology.
5. You take responsibility for the development of your project in a well-structured, thorough way, and you’re able to solve problems independently. You display creativity in solving problems, generating ideas, or finding new ways of working.
6. You have an open personality and willing to contribute to the team and participate in didactic projects.
7. You have excellent communication skills in English, both orally and written.