A hybrid FCN-BiGRU with transfer learning for low-velocity impact identification on aircraft structure

Abstract

In the work process of an aircraft, its external structure frequently encounters some low-velocity impact events, resulting in some barely visible impact damages inside the structure. Therefore, impact localization and reconstruction are critical for the structure’s health monitoring and reliability analysis. However, traditional inversion methods usually perform poorly in identifying random impact loads due to a series of problems. Inspired by the superior model properties of deep learning algorithms, a novel feature learning-based technique for impact load reconstruction and localization is presented. The proposed method consists of two parts, the first of which is utilized to localize the impact loads and is referred to as one-dimensional full convolution network. The other part aims at reconstructing the impact load and is called attention mechanism-full convolutional network-bidirectional gating recurrent unit-multilayer perceptron. Moreover, a transfer learning technique is introduced to optimize the model structure and process in light of the suggested innovative network model. The experimental piece of this research was an aircraft cutting segment whose structural surface was equipped with a number of piezoelectric sensors. These sensors received impact response signals with differences in amplitude from hammer strikes at various positions. In the work, the process of locating first and then reconstructing the impact load history was followed. And the influence of implementing the transfer learning mechanism on the performance of the impact load history reconstruction model was investigated as well. Three experiments and repeated cross-validation verified the efficacy of the proposed method. The findings indicated that the proposed technique could accurately and quickly detect impact loads at various locations. Simultaneously, it also allowed a perfect reconstruction of the impact loads of different impact energies.