Researcher
Cédric Peeters
- Keywords:Mechanical engineering
- Disciplines:Acoustics, noise and vibration engineering
Affiliations
- Acoustics & Vibration Research Group (Research group)
Member
From15 Sep 2019 → Today - Engineering Technology (Department)
Member
From1 Nov 2023 → Today - Engineering Technology (Department)
Member
From1 Oct 2020 → 30 Sep 2023 - Engineering Technology (Department)
Member
From1 Dec 2019 → Today - Engineering Technology (Department)
Member
From15 Sep 2019 → 30 Sep 2020 - Engineering Technology (Department)
Member
From15 Sep 2019 → 30 Nov 2019 - Applied Mechanics (Department)
Member
From1 Jan 2019 → 14 Sep 2019 - Faculty of Engineering (Faculty)
Member
From25 Nov 2015 → 21 Nov 2019 - Applied Mechanics (Department)
Member
From15 Sep 2015 → 14 Sep 2019
Projects
1 - 1 of 1
- Condition monitoring of slow-rotating components in wind turbine drivetrains using novel signal processing approaches based on the instantaneous angular speed signalFrom1 Oct 2020 → 30 Sep 2023Funding: FWO fellowships
Publications
1 - 10 of 26
- Fatigue crack detection in planetary gears: Insights from the HUMS2023 data challenge(2024)
Authors: Cédric Peeters, Wenyi Wang, David Blunt, Timothy Verstraeten, Jan Helsen
Pages: 1-26 - Early Fault Detection Using Deep Learning on Compressed Cyclic Spectral Coherence Maps(2023)Series: Proceedings of the ASME Turbo ExpoVolume: 14
Authors: Fabian Ramiro Perez Sanjines, Cédric Peeters, Timothy Verstraeten, Ivo Vervlimmeren, Ann Nowe, Jan Helsen
Number of pages: 9 - Informed sparsity-based blind filtering in the presence of second-order cyclostationary noise(2023)
Authors: Kayacan Kestel, Cédric Peeters, Jérôme Antoni, Quentin Leclère, Francois Girardin, Jan Helsen
- Wind turbine drivetrain fault detection using physics-informed multivariate deep learning(2023)
Authors: Faras Jamil, Cédric Peeters, Timothy Verstraeten, Jan Helsen
Number of pages: 9 - Enhancing the Performance of the Multi-Order Probabilistic Approach in Angular Speed Estimation through Adaptive Window Selection(2023)
Authors: Georgios Eftichios Protopapadakis, Cédric Peeters, Quentin Leclère, Jan Helsen
Number of pages: 11 - Wind Turbine Drivetrain Fault Detection Using Multi-Variate Deep Learning Combined With Signal Processing(2023)Series: Proceedings of the ASME Turbo Expo
Authors: Faras Jamil, Francisco Javier Jara Avila, Konstantinos Vratsinis, Cédric Peeters, Jan Helsen
Number of pages: 7 - Fleet-based early fault detection of wind turbine gearboxes using physics-informed deep learning based on cyclic spectral coherence(2023)
Authors: Fabian Ramiro Perez Sanjines, Cédric Peeters, Timothy Verstraeten, Jérôme Antoni, Ann Nowe, Jan Helsen
- Signal processing informed deep learning for failure detection in a fleet of multi-stage planetary gearboxes with limited knowledge about characteristic frequencies(2023)
Authors: Jan Helsen, Fabian Ramiro Perez Sanjines, Faras Jamil, Jéröme Antoni, Cédric Peeters
Pages: 663-668Number of pages: 6 - The design of optimal indicators for early fault detection using a generalized likelihood ratio test(2023)Edition: 2024Volume: -
Authors: Kayacan Kestel, Jérôme Antoni, Cédric Peeters, Quentin Leclère, Ted Ooijevaar, Jan Helsen
Pages: -Number of pages: 10 - Generalized likelihood ratio-based condition indicator maximization via Rayleigh quotient iteration(2022)
Authors: Kayacan Kestel, Cédric Peeters, Jérôme Antoni, Quentin Leclère, Francois Girardin, Robert Brijder, Jan Helsen
Pages: 1-14Number of pages: 14