结合光纤布拉格光栅与主成分分析的损伤识别

Damage Identification Based on Fiber Bragg Grating and Principal Component Analysis

为了发展长标距光纤布拉格光栅(FBG)应变传感技术,采用主成分分析(PCA)方法剔除长标距FBG应变传感器动态响应数据中的冗余部分,获取重要数据特征,实现对结构损伤的识别。试验过程中,在钢梁表面上粘贴分布式长标距FBG传感器,在翼缘处采用分次切割的方式模拟不同位置和程度的损伤,使用激振设备通过扫频方式使梁振动,测得健康状态和不同损伤状态下FBG应变动态响应数据。使用PCA方法,分析健康状态和损伤状态下T2和Q统计量数据特征的变化,可以较为清楚地识别出结构的损伤情况。将2种统计量分析相结合,可以实现对损伤位置和损伤程度的识别。

In order to enhance the long-gauge Fiber Bragg grating(FBG)sensing technology,the principal component analysis(PCA)method was made attempt to eliminate redundant parts of the dynamic response data and obtain more important characteristic from Fiber Bragg Grating sensors for structural damage identification.During the testing process,the distributed long-gauge FBG sensors were stuck on the surface of the steel beam.The method of graded cutting in flange was used to simulate the damage conditions in different locations and degrees,and then the actuator made the beam vibrate by the way of sweep frequency.The dynamic response data were measured under healthy condition and differently damaged condition.The PCA method was applied in analyzing the change of the statistic characteristic value T2 and Q between healthy condition and damaged condition,which clearly identified the damage condition.The damage locations and degrees can be implemented by the combination of two statistic characteristics.