Damage-related imbalance identification for UAV composite propeller blades based on bidirectional temporal convolutional network and a flexible sensing system

Abstract

Damage to the composite propeller blades could lead to rotational imbalance, which seriously affects the operational safety of unmanned aerial vehicles (UAVs), therefore, a novel method combining the Teager energy operator and bidirectional temporal convolutional network is proposed for detecting, localizing, and quantifying the damage-related imbalance in the blades. A flexible sensing system that contains MEMS accelerometers, signal conditioning, and wireless transmission is integrated with the composite propeller for in-situ signal acquisition of the propeller blades. Teager energy operator (TEO) is applied to demodulate and enhance the pulse compositions in vibration signals and singular value decomposition (SVD) is employed to suppress random noise, resulting in denoised Teager energy spectrums for model input. Temporal convolutional network (TCN) has been widely used in sequence signal modeling because the causal dilated convolution could learn the context information of sequence signals while maintaining the advantages of parallel computing. To fully extract the signal features, bidirectional temporal convolutional network (BiTCN) models are established to learn both the forward and backward signal features. Experimental verification results show that the proposed method detects the existence of imbalance with 100% accuracy, and the accuracies of localization and quantization are 99.65% and 98.61%, respectively, which are much higher than those of the models with the original signal as input. In addition, compared with the other four different algorithms, BiTCN is superior in terms of convergence speed and prediction accuracy.