Damage localization and quantification of composite stiffened panels via CVAE-based data expansion together with hierarchical feature fusion and multi-domain adaptation

Abstract

With the wide application of composite materials in critical load-bearing structures, various damages with complex distribution and forms are prone to occur in the service process. Because of the significant differences in signal characteristics of different damages and the limited number of samples, it is a severe challenge to realize high-precision damage location and quantification. To solve this problem, this article proposes a damage location and quantification method of composite stiffened plates based on conditional variational autoencoder (CVAE) together with hierarchical feature fusion and multi-domain adaptation. Specifically, CVAE is first used to perform structured data expansion on finite Lamb wave signals to construct a high-fidelity training dataset, which effectively relieves the constraint of data scarcity on the model performance. Secondly, a feature extraction network integrating the Inception module and the multi-head attention mechanism is designed to capture the hierarchical and multi-scale features of damage signals and fuse local-global information, and simultaneously predict the damage location and damage size under the multi-task learning framework, so as to promote information sharing and collaborative optimization among tasks. Finally, the unsupervised domain adversarial neural network mechanism is introduced, and the unsupervised adversarial optimization of cross-domain feature distribution is realized through the feature alignment between the source domain and the unlabeled target domain, so that the model still has excellent robustness and generalization performance under unseen location and cross-domain conditions. The results demonstrate the effectiveness and superiority of the proposed method in damage localization and quantification, which provides an efficient and reliable solution for intelligent health monitoring of composite stiffened plates.