Abstract
The significance of implementing online structural health monitoring (SHM) for aerospace structures under harsh service environments cannot be overemphasized. Deep learning has demonstrated a promising and effective means to achieve accommodate such a need. However, as envisaged, the performance of deep learning-facilitated SHM heavily relies on the scale of training dataset, degrading the practicability of the approach. To address this, we propose an improved prototype network and data augmentation methods for few-shot SHM using guided waves. In the improved prototype network, the weighted Euclidean distance is used for damage classification. An attention module is established to predict the weight coefficients. The Davies–Bouldin Index (DBI) is used in the loss function to better separate the embedding vectors of different classes. Time masking and frequency masking are proposed for data augmentation of guided wave signals. As bolt joints are widely used in aerospace structures, the proposed approach is experimentally validated by quantifying the degree of bolt loosening in multibolt connection structures. The results are compared against those obtained from other classical few-shot learning (FSL) methods.
Published Version
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