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

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.

Keywords: 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

• Academic supervisor    Dr. Laurent MEVEL, INRIA, I4S
• Doctoral School    ED644 MathSTIC Bretagne Océane, Université de Rennes
• IFPEN supervisor    Dr. Olivier LEPREUX, olivier.lepreux@ifpen.fr
• PhD location    IFPEN, Lyon, France 
• Duration and start date    3 years, starting in the fourth quarter 2026 (Novembre 2)
• Employer    IFPEN
• Academic requirements    University Master degree in Applied Mathematics or Mechanical Engineering
• Language requirements    English level B2 (CEFR)

To apply, please send your cover letter and CV to the IFPEN supervisor indicated here below.