Sun Fuqiang, Wang Ning, He Jingjing, Guan Xuefei, Yang Jinsong
Science and Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Beijing 100191, China.
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
Materials (Basel). 2017 Jun 12;10(6):648. doi: 10.3390/ma10060648.
Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.
据报道，兰姆波是用于各种应用场景无损评估（NDE）的有效工具。然而，由于兰姆波传播和损伤检测的潜在机制复杂，使用兰姆波方法进行准确可靠的损伤量化仍然是一项实际挑战。本文提出了一种使用最小二乘支持向量机（LS-SVM）和遗传算法（GA）的兰姆波损伤量化方法。提出了三个损伤敏感特征，即归一化振幅、相位变化和相关系数，以描述由损伤引起的兰姆波特性变化。鉴于常用的数据驱动方法，利用所提出的三个损伤敏感特征，实现了基于GA的LS-SVM模型来评估裂纹尺寸。采用GA方法优化模型参数。使用带有自然形成疲劳裂纹的试样测试数据和搭接接头部件测试数据，对基于GA的LS-SVM结果进行了验证。还包括不同载荷和制造商的案例，以进一步验证所提出的裂纹量化方法的稳健性。
