Project Details
Description
This project aims to develop methods for assessing the health and degradation of rotating machines. We merge the state-of-the-art in signal processing, with physical modelling, and machine learning (ml) in a hybrid approach
| Acronym | FWOSBO34 |
|---|---|
| Status | Finished |
| Effective start/end date | 1/01/20 → 31/12/23 |
Keywords
- dynamic envirnments
- companies
- condition monitoring
Flemish discipline codes in use since 2023
- Acoustics, noise and vibration engineering
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Leveraging signal processing and machine learning for automated fault detection in wind turbine drivetrains
Jamil, F., Peeters, C., Verstraeten, T. & Helsen, J., 11 Sept 2025, In: Wind Energy Science. 10, 9, p. 1963–1978 16 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile2 Citations (Scopus) -
Experimental investigation of the relation between operating conditions and offshore wind turbine drivetrain dynamics
Kestel, K., Peeters, C., Vratsinis, K., Matthys, J. J., Sterckx, J., Daems, P.-J. & Helsen, J., 5 Nov 2024, EERA DeepWind Conference 2024. IOP Publishing, p. - 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference paper
Open Access -
Offshore field experimentation for novel hybrid condition monitoring approaches
Kestel, K., Jamil, F., Matthys, J. J., Vratsinis, K., Sterckx, J., Marini, R., Peeters, C. & Helsen, J., Apr 2024, In: Journal of Physics: Conference Series. 2745, 1, 12 p., 012009.Research output: Contribution to journal › Article › peer-review
Open AccessFile7 Downloads (Pure)