Modal identification methods using LiDAR-based full-field measurements: application to fault detection in onshore and offshore wind turbines

Association Bernard Gregory; Lyon; Sur place; EUR 23 000 - 28 000; Plein temps

Résumé du poste

Association Bernard Gregory is seeking candidates for a PhD position focused on modal identification methods using LiDAR-based full-field measurements. The project addresses fault detection in both onshore and offshore wind turbines, integrating OMA techniques with LTP system modeling.

The role requires a University Master degree in Applied Mathematics or Mechanical Engineering and English at CEFR B2 level.

Qualifications

• University Master degree in Applied Mathematics or Mechanical Engineering.
• English level B2 (CEFR).

Responsabilités

• Develop modal identification methods for LiDAR-based measurements.
• Integrate modeling of Linear Time-Periodic (LTP) systems for rotating structures like wind turbines.
• Define detectability thresholds for wind turbine defects under uncertainty.

Connaissances

System identification

OMA

SSI

LiDAR

Video analysis

UAV

SHM

Wind turbines

LTP systems

Formation

Master degree in Applied Mathematics or Mechanical Engineering

Outils

LiDAR

Video

UAV

Description du poste

Modal identification methods using LiDAR-based full-field measurements: application to fault detection in onshore and offshore wind turbines

25/08/2026 Contrat doctoral

Modal identification methods using LiDAR-based full-field measurements: application to fault detection in onshore and offshore wind turbines

System identification, OMA (Operational Modal Analysis), SSI (Stochastic Subspace Identification), LiDAR, video, UAV (Unmanned Aerial Vehicle), SHM (Structural Health Monitoring), onshore/offshore wind energy, LTP structures (Linear Time-Periodic systems), fault detection

Structural integrity is a major challenge for the reliability and durability of wind turbines. Operational Modal Analysis (OMA) is currently a reference method for characterizing the dynamic state of structures from vibration measurements, but it still relies mainly on networks of accelerometers, whose spatial coverage remains limited. The emergence of full-field optical techniques, such as high-resolution video or LiDAR, offers the possibility of accessing rich and distributed vibrational fields, while posing new challenges for their integration within the OMA framework.
The proposed PhD will address three main objectives: (i) developing modal identification methods that can be applied to massive optical measurements obtained from LiDAR systems; (ii) integrating the modeling of Linear Time-Periodic (LTP) systems, which is essential for the analysis of rotating structures such as wind turbines, building on recent developments in the literature; (iii) defining detectability thresholds for typical wind turbine defects - blade misalignments, aerodynamic and inertial imbalances, loss of stiffness in the tower or blades - while accounting for uncertainties.

02/11/2026

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site.
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions.

Academic requirements University Master degree in Applied Mathematics or Mechanical Engineering
Language requirements English level B2 (CEFR)