Residual and Wavelet based Neural Network for the Fault Detection of Wind Turbine Blades

When wind turbine blade faults are not detected at an early stage, they can become costly to fix as the damage could worsen over time and the maintenance costs of the blades could increase. This paper investigates a monitoring method for efficient and accurate damage detection and fault diagnosis of wind turbine blades by using pictures of the blades. The method uses a Residual Neural Network, a model of Convolutional Neural Networks, with an integration of wavelet-based layers. This approach aims to improve the health monitoring system of the wind turbine blades and the accuracy of fault detection to reduce the monitoring cost and the operation interruptions of the wind turbines due to severe damage to the blades.