The damage localization method in the composite panel by multifrequency guided wave signal using parallel network

Fiber-reinforced composite materials find extensive application in the realm of aeronautics; nonetheless, during operational phases, structural integrity may be compromised due to external impacts or extreme environmental conditions. Consequently, the imperative is the necessity of vigilantly monitoring the structural state. The structural health monitoring (SHM) technology based on ultrasonic guided waves can achieve long-distance and wide-ranging damage detection utilizing a minimal number of cost-effective piezoelectric transducers. However, traditional damage identification methods necessitate the analysis of the multi-modal characteristics of guided waves and the deployment of dense sensor arrays. In this paper, we propose a network model that leverages multi-frequency guided-wave signals to achieve spatiotemporal parallelism, incorporating residual blocks and convolutional attention mechanisms (GCRA). This model accurately estimates damage locations with an R² value of 0.9147. The average error for the six randomly selected damage locations is 0.4275 cm. The results demonstrate that the GCRA facilitates the identification of damaged positions with a high degree of confidence.