Wavelet Energy Transmissibility Analysis for Wind Turbine Blades Fault Detection

Wind turbines (WT) are increasingly deployed worldwide to harvest wind power from nature, and WT blades are the most crucial components among the WT systems. WT blades are subject to non-stationary time-varying loads and the load information is usually unknown or hard to obtain. This poses great challenges to blade condition monitoring and fault detection. To avoid WT malfunctions and further economic loss, Transmissibility Functions (TF) based approaches have been developed with the purpose of precisely detecting the incipient WT blades defects. In this paper, a recently proposed Wavelet Energy TF (WETF) method which has been successfully applied to WT bearings is transferred to WT blades fault detection. This technique can remove the impacts of external varying loads, requires no excitation information, and demonstrates robustness to noise. The effectiveness of the WETF method for WT blade fault detection is validated on three naturally-damaged industrial-scale WT blades, and its superiority over the conventional Fourier TF (FTF) method is also demonstrated.