Damage Quantification Monitoring of CFRP Structures Cross-Area Based on Transfer Learning

Given the widespread application of carbon fiber reinforced polymer (CFRP) structures in aerospace and automotive manufacturing, the critical role of structural health monitoring (SHM) is increasingly evident. Traditional damage quantification methods exhibit poor transferability and reusability in different structural areas, which are difficult to adapt to the complex and diverse damage characteristics of CFRP structures. To address this challenge, a multi-scale adaptive cross-area damage quantification method based on transfer learning (MSACADQ-TL) is proposed in this paper. Firstly, a multi-scale feature extraction framework is presented, which decomposes signals from various paths into multiple frequency domain sub-signals and analyzes the dynamic trends of these signals across different signal scales, providing richer damage feature information. Moreover, the difference between the source domain and the target domain is minimized to enhance transferability of features. Furthermore, this paper applies a damage quantification based on the Bidirectional Gated Recurrent Unit (BiGRU) model to achieve cross-area damage quantification. To validate the proposed method's effectiveness, this paper designed three different cross-area damage transfer scenarios. Experimental results demonstrate that the MSACADQ-TL model outperforms other comparison methods in damage quantification tasks, which is effective for quantification damage in CFRP structures.