As a critical component for capturing wind energy, wind turbine blades may subject different degrees of damage due to blade manufacturing and operating load, which directly affects the reliability of wind turbine operation. For preventing quality and safety accidents, a fast and easy non-implantable detection method is needed to identify the damages. According to the physical correlation between blade damage and blade operation noise, a blade damage detection method based on acoustic signal and convolutional neural network (CNN) is proposed. The method converts the time-series acoustic signal into a two-dimensional spectral picture and combines the healthy spectral picture to generate a residual spectral picture. Then, the residual spectrogram is used to train the convolutional neural network and detect the damage. The analysis results show that the algorithm eliminates the influence of the inherent blade sweeping sound generated by the impeller rotation on the damage identification and improves the identification accuracy. The algorithm analysis was carried out with the actual measured data of a local wind turbine, and the results showed that the classification accuracy of the algorithm reached 96.9%, which verified the effectiveness and accuracy of the detection method based on convolutional neural network.

wind turbine blade  /  damage detection  /  acoustic signal  /  convolutional neural network