Intelligent guided wave damage detection and assessment of stiffened plate

In this paper, an intelligent guided wave damage detection method based on convolutional neural network algorithm is established to realize efficient identification and precise positioning of debond damage in stiffened plates. Based on numerical simulation and experimental study on the propagation characteristics of guided wave in T-stiffened plate, the Lamb wave responses of different damaged samples are obtained by the method of single-point excitation and multi-point reception, and a fusion database is formed after preprocessing. The convolutional neural network (CNN) deep learning detection algorithm is used to extract and learn damage-related features in the fusion database, and the performance of the network is tested with untrained data. The results show that the 7-layer CNN with Adam as the optimizer can detect damage samples in the database with an accuracy of 99%. The CNN-based intelligent guided wave detection method for stiffened plates can not only identify debonding damage, but also accurately locate it.