Within the project, the research team will study and develop supporting methodologies for the estimation of the Remaining Useful Life (RUL) of bearings and gears of a drive train. A Digital Twin of the drive train is proposed as a solution to the lack of data, which could be used for training of a purely data driven RUL estimation approach. The investment of such additional modelling effort is expected to lead το higher estimation accuracy.
Your job content is to study and develop estimation methods for the varying condition of the bearing or gear, based on information within the electrical power signals of the electrical actuator, being a component in the drivetrain. For this purpose, a digital twin of this physical actuator will be developed which together with the physical twin is observed during both healthy and degraded state of the bearing or gear. From the observations, physical and digital features are to be extracted and compared, in order to quantify the uncertainties in the actuator part of the digital twin. Such information will allow the team to integrate the electrical actuator into the Digital Twin and to develop more accurate prognostic techniques aimed at estimating the RUL of bearings and gears.
Profile of the candidate
1. You hold a MSc degree in Electro-Mechanical Engineering, or Mechanical Engineering with courses in electrical drives and instrumentation.
2. You have a strong motivation for conducting scientific research at a high level.
3. You possess good analytical, and technical skills related with data-acquisition systems.
4. You have excellent communication skills in English, both orally and written. You can present scientific results before the team in an understandable manner.
5. You take responsibility for the development of your part in the project in a well-structured, thorough way, and you’re able to solve smaller problems independently. You display creativity in solving larger problems, generating ideas or finding new ways of working together with the team.
6. You have an open personality and willing to contribute to the team.
How to apply
Send your CV and motivation letter to Prof. Frederik De Belie (Frederik.DeBelie@UGent.be).
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