建立了基于卷积神经网络算法的智能导波损伤检测方法,可实现加筋板中脱黏损伤的高效识别和精准定位。在数值模拟和试验研究T型筋加筋板中导波传播特性的基础上,通过单点激发多点接收的方法获取不同损伤样本的兰姆波响应,经预处理之后组成融合数据库。利用卷积神经网络(CNN)深度学习检测算法,抓取和学习融合数据库中与损伤相关的特征,并使用未经训练的数据测试网络性能。结果表明,以Adam为优化器的7层CNN对数据库中损伤样本的检测精度达99%;基于CNN的智能导波检测方法不仅能够识别加筋板中的脱黏损伤,而且能够准确定位。

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.