Gated Fusion Based Transformer Model for Crack Detection on Wind Turbine Blade

Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades. The cracks on the blades can endanger the shafting of the generator set, the tower and other components, and even cause the tower to collapse. To achieve high-precision wind blade crack detection, this paper proposes a crack fault-detection strategy that integrates Gated Residual Network (GRN), a fusion module and Transformer. Firstly, GRN can reduce unnecessary noisy inputs that could negatively impact performance while preserving the integrity of feature information. In addition, to gain in-depth information about the characteristics of wind turbine blades, a fusion module is suggested to implement the information fusion of wind turbine features. Specifically, each fan feature is mapped to a one-dimensional vector with the same length, and all one-dimensional vectors are concatenated to obtain a two-dimensional vector. And then, in the fusion module, the information fusion of the same characteristic variables in the different channels is realized through the Channel-mixing MLP, and the information fusion of different characteristic variables in the same channel is realized through the Column-mixing MLP. Finally, the fused feature vector is input into the Transformer for feature learning, which enhances the influence of important feature information and improves the model’s anti-noise ability and classification accuracy. Extensive experiments were conducted on the wind turbine supervisory control and data acquisition (SCADA) data from a domestic wind field. The results show that compared with other state-of-the-art models, including XGBoost, LightGBM, TabNet, etc., the F1-score of proposed gated fusion based Transformer model can reach 0.9907, which is 0.4%–2.09% higher than the compared models. This method provides a more reliable approach for the condition detection and maintenance of fan blades in wind farms. © 2023, Tech Science Press. All rights reserved.