Lamb波多特征参数的复合材料损伤程度 评估方法*

航空航天飞行器、风电等工程机械结构的安全性保障至关重要，复合材料等新材料的大量应用，进一步提升了对结构健康监测的难度和要求。裂纹、脱层等复合材料结构典型损伤形成机理复杂、易扩展，损伤程度累积严重时会造成结构的突然失效。面向复合材料结构的损伤监测与诊断，现有的方法大多很难实现对结构损伤程度的跟踪评估，进而无法对结构的安全性评价提供依据。采用Lamb波监测理论，基于其传播距离远、对小损伤敏感等特点，通过研究分析典型损伤对Lamb波结构响应信号传播的多个特征作用过程和机理，采用多尺度分析，提取结构损伤时频域多特征参数；借助于神经网络的非线性映射能力，构建多特征复合材料结构损伤程度评估模型，实现对不同程度下典型结构损伤的评估。在环氧玻璃纤维复合材料板结构上的实验验证结果表明：提取的Lamb波时频域多特征参数，对不同程度典型损伤有一定的敏感性，所构建的损伤程度评估系统可以较为有效的实现损伤的程度估计。

Research on damage degree evaluation method of composite material structure based on mult-feature of Lamb wave

It is particularly important to ensure the safety of engineering structures such as aerospace vehicles and wind turbines. The large number applications of composite materials have increased the difficulty and requirements of structural health monitoring. Typical damage formation mechanism of composite structures such as crack and delamination is complex and extending-easily. When the damage degree accumulates seriously, it will cause a sudden failure of the structure. For the damage monitoring and diagnosis of composite structures, most of the existing methods are difficult to track and evaluate the damage degree of structures, and thus can not provide the basis for the safety evaluation of structures. Based on the characteristics of long propagation distance and sensitivity to small damage, Lamb wave monitoring theory has been used. By analyzing the effect of damage on multiple features of the Lamb wave structure response signal, multi-scale analysis of wavelet transform is used to extract multi-feature parameters in the time-frequency domain of damage. With the help of the non-linear mapping ability of the neural network, a composite structures damage assessment model is constructed to realize the assessment of typical structural damage at different levels. The experiment result on the epoxy glass fiber board structure shows that the extracted Lamb wave multi-feature parameters in the time-frequency domain have certain sensitivity to the accumulated typical damage. The damage assessment model can effectively estimate the damage degree.