Fault Diagnosis of Wind Turbine Blades Based on Image Fusion and ResNet

Abstract

In the diagnosis of wind turbine blade faults, the information provided by a single sensor is limited. To address this issue and take advantage of complementary features among multiple fault information sources, while enhancing fault diagnosis accuracy, a method for diagnosing wind turbine blade faults is proposed. This method combines Image Fusion Convolutional Neural Network (IFCNN) with the ResNet network. Firstly, the time-frequency representation of vibration data is obtained using wavelet transform. The time-frequency representation and blade fault images are input into the IFCNN to obtain fused images containing two categories of fault features. Next, the ResNet convolutional neural network is employed to automatically extract non-linear features from the fused images, establishing a classification model for blade fault images. Experimental results demonstrate that, with limited training data, the classification accuracy of this method can reach 86.7%, outperforming fault diagnosis models trained with single fault information. This approach offers a new perspective and method for the fusion of multiple fault information in the field of wind turbine blade fault diagnosis