Damage identification for UAV composite propeller blades based on transmissibility probabilistic distance and attention bidirectional temporal convolutional network

Abstract

Damage identification of composite propeller blades is critical to the operational safety of unmanned aerial vehicles (UAVs). The transmissibility function (TF) was employed to characterize the damage and its probabilistic distance was used to deal with the uncertainty problem. The damage indicator fusing multiple TFs in a specific frequency band was proposed to detect the blade damage. In addition, to localize the damage in the blade, an attentional bidirectional temporal convolutional network (ABiTCN) model was developed, in which the Squeezeand-Excitation (SE) attention module was introduced to enhance the model’s capability to learn critical features. The proposed method was investigated by numerical and experimental cases. The results showed that the proposed damage indicator has more sensitivity to weak damage and better robustness than the common indicator. The established ABiTCN model predicted the positions of the damages with 91.89 % accuracy, which outperforms other methods in terms of accuracy and convergence speed.