Few-shot fatigue damage evaluation of aircraft structure using neural augmentation and deep transfer learning

Abstract

To solve the problems of few-shot samples, different structural degradation trends and poor damage evaluation effect in fatigue damage evaluation of aircraft structure, an intelligent evaluation method based on neural augmentation and deep transfer learning (NA-DTL) is proposed in this paper. Firstly, the fatigue damage is divided into three risk levels according to the length of crack, and conditional variational autoencoder (CVAE) and one-dimensional convolutional neural network (1-DCNN) are constructed to form the neural augmentation model for collaborative optimization of augmentation network and classification network. Subsequently, CVAE is used to generate massive fatigue damage samples, which can provide data support for building of crack length evaluation model. In addition, model-based transfer learning method is applied for damage evaluation according to the trained 1-DCNN. The fatigue crack growth dataset of aircraft aluminum lap joint is utilized to verify the effectiveness of the proposed method. The results show that the proposed method can achieve more accurate evaluation results compared with other models.