Multi Point Sensing and Defect Detection of Wind Turbine Blades: An Approach Using Fibre Bragg Gratings

Structural Health Monitoring (SHM) of Wind Turbine Blades (WTBs) plays a critical role in ensuring safe and cost-effective operations of these complex structures. However, detecting manufacturing-induced defects and operation-induced damages poses significant challenges. As stated by Güemes et al., 2018, defects are not direct physical parameters but rather local changes in material properties that degrade structural performance. Detecting these localised defects and subtle variations is particularly difficult with current SHM systems, which often lack sufficient spatial resolution and sensitivity.This thesis investigates the application of Fibre Bragg Grating (FBG) sensors for multi-point sensing along the blade and explores advanced sensor data processing techniques to enhance early-stage defect detection. The main contributions of this work are: (1) a comprehensive review of common defects and damages in WTBs, as well as current condition monitoring, sensing, and non-destructive testing techniques; (2) the application of surface-bonded wavelength division multiplexed (WDM) FBG sensors for defect detection in laboratory-scale wind turbine blades and composite cantilever beams; and (3) the validation of FBG strain measurements against expected values derived from numerical simulations and analytical calculations.The results of this work show that FBG sensors can successfully detect defects in wind turbine blades, with good agreement between measured and predicted strain values in both defect-free and defective composite cantilever beams. A novel investigation of the average measured strain over defects revealed that while smaller sensing lengths improve strain measurement accuracy, an optimal sensor length must be chosen to balance sensitivity with the number of sensors required for effective coverage of the blade. Additionally, the performance of FBG sensors in detecting defects was influenced by the complexity and nature of the defect, as well as sensor placement, but the FBG arrays effectively differentiated between healthy and defective blades. To support these contributions, a LabVIEW-based data acquisition system was developed, along with an automated Gaussian FBG peak-fitting algorithm. Additionally, laboratory-scale composite wind turbine blades (1.8 m) with and without an artificial defect were manufactured to validate the sensor system.This work demonstrates that FBG sensors can reliably detect defects at an early stage in wind turbine blades. By leveraging these findings, the integration of FBG sensors into SHM systems could significantly enhance the operational efficiency and maintenance of wind turbine blades and related components.