强噪声下碳纤维增强树脂复合材料结构Lamb波层析损伤成像方法

Lamb wave tomography damage imaging of carbon fiber reinforced polymer composite structures in strong noise environment

Lamb波因其检测范围广、对缺陷敏感性高等特点在复合材料无损检测中广泛应用。但强噪声环境给有效信号的提取带来难度,影响损伤位置判定精度。针对该问题,提出了一种在强噪声背景下基于计盒维数和Lamb波层析成像技术的损伤定位成像方法。首先通过仿真分析了Lamb波在碳纤维增强树脂(CFRP)复合材料板损伤前后传播的特性。在选定的复合材料板上均匀布置圆形传感器阵列,以粘结质量块改变结构局部刚度的形式模拟真实损伤;其次每个传感器依次作为激励器产生Lamb波,其他传感器采集有无损伤下的响应信号,采用小波变换进行信号去噪。将去噪后的信号添加不同等级的白噪声实现噪声干扰;最后采用计盒维数计算有无损伤的信号差异确定损伤因子,并通过概率成像算法实现损伤的定位成像。实验结果表明,在强噪声环境中单损伤与多损伤成像定位最大和平均误差分别为11.18 mm和6.88 mm,该方法无需信号降噪技术,且避免了多损伤时复杂反射信号的提取过程,在强噪声下复合材料损伤定位识别方面具有较大的潜力。

Lamb wave has been extensively used in nondestructive testing of composites due to its wide detection range and high sensitivity to defects. However, strong noise environment makes it difficult to extract effective signals, which affects the accuracy of damage location determination. To solve this problem, a damage location imaging method based on box-counting dimension and Lamb wave tomography technology in strong noise background was proposed. Firstly, the propagation characteristics of Lamb wave before and after damage of composite plate were analyzed by simulation. Circular sensor arrays were uniformly arranged on the carbon fiber reinforced polymer(CFRP) composite plate to simulate the real damage by changing the strain field of the structure with bonded mass blocks. Secondly, each sensor in turn acted as an exciter to generate Lamb wave. Other sensors collected the response signal without damage, and used wavelet transform to denoise the signal. Different levels of white noise were added to the denoised signal to realize noise interference. Finally, the damage factor was determined by calculating the difference of non-destructive signal by box-counting dimension, and the damage location imaging was realized by probability imaging algorithm. The experimental results show that the maximum and average errors of single-damage and multi-damage imaging locations are 11.18 mm and 6.88 mm respectively in strong noise environment. This method does not need signal denoising technology, and avoids the extraction process of complex reflection signals when multi-damage occurs. It has great potential in damage location and identification of composites under strong noise.