Image-Model-Based Fault Identification for Wind Turbines Using Feature Engineering and MuSnet

This articleproposes an intelligent algorithm for wind turbine faults identification based on the image model of the dynamic process and the deep convolutional neural network. First, feature engineering is designed to generate the image model of a dynamic process. We performed variable refinement and data normalization preprocessing on the dataset. Then the time series data are reconstructed in the Gram angle field to form an image model. Next, an identification algorithm adapted to the image model, called multistream self-fusion net (MuSnet), is proposed. Inside the MuSnet, we use the multistream self-fusion module to replace the original convolution operation in the low-level convolution. This allows the features of heterogeneous information in the image model to be better extracted. Multiple evaluation metrics in the experiment show that the proposed method has advantages of high recognition accuracy, ability to train with fewer samples, speeding up the convergence of learning, and high robustness for wind turbine fault identification.