Research on wind turbine blade fault detection based on DenseNet-TL combined with ELM

Aiming at the safety hidden danger caused by blade faults that are difficult to be detected, the fault prevention detection technology based on blade image intelligent processing is investigated. A wind turbine blade fault prevention detection method is designed to analyses wind turbine blade images by combining DenseNet, Transfer Learning (TL) and Extreme Learning Machines (ELM), and collect image samples as a training set. The image samples are collected as training set, and the image features are effectively extracted using the improved DenseNet, which is combined with Extreme Learning Machines to improve the classification accuracy of the detection. 8000 images were collected, and the analysis results for the test set of images show that the detection accuracy of this model is higher than that of the DenseNet, ResNet and AlexNet models of migration learning, reaching more than 99%, and obtaining a more accurate preventive detection of wind turbine blade faults.