A Multimodal Gated Recurrent Unit Neural Network Model for Damage Assessment in CFRP Composites Based on Lamb Waves and Minimal Sensing

Carbon fiber-reinforced polymer (CFRP) composites are widely used in aerospace due to their outstanding mechanical properties. However, composite damage detection and localization techniques based on minimal actuator-receiver sensing pairs are still challenging. Based on Lamb wave (LW), current damage detection and localization methods rely on numerous sensors and baseline signals to extract damage information. This study introduces a novel approach for damage detection and localization in CFRP composites using a multimodal gated recurrent unit neural network (MGNN) model. The local maximum energy of the LW is obtained through the continuous wavelet transform (CWT) using complex Morlet wavelets. MGNN combines the time-domain LW-based and CWT-energy signals as an input, dramatically improving the anti-interference and damage feature extraction accuracy. In the MGNN framework, multiple feature mappings are aggregated to establish correlations between damage coordinates and features. This enables damage assessment of composite panels even with a limited dataset, utilizing a minimal sensing actuator-receiver pair configuration. Numerous experiments demonstrate the superior performance of the proposed method compared to other current models. Additionally, anti-interference and ablation experiments substantiate the effectiveness and more robust anti-interference capability of the proposed method. These findings reduce the complexity of sensing networks and detection costs for LW-based methods, thereby significantly improving the accuracy and reliability of composite damage detection.