基于Lamb波和改进贝叶斯融合算法的CFRP边缘分层损伤分析

Analysis of edge delamination damage of CFRP based on Lamb wave and improved Bayesian fusion algorithm

贝叶斯融合法在CFRP上的损伤定位分析结果比较好,但在传感器网络边缘区域会出现比较明显的定位误差。为解决原有算法对传感器网络边缘的分层损伤定位不精确的问题,分析了两种不同损伤因子构成的贝叶斯融合算法在CFRP上的损伤定位效果,验证了以ToF损伤因子形成的贝叶斯融合算法对CFRP损伤定位的可行性,以及贝叶斯融合算法对边缘分层损伤的定位精度;在以损伤因子DI所形成概率成像算法的基础上,以衰减更快的指数权重代替线性权重,将改进的概率成像算法重新代入以贝叶斯为框架的算法中形成一种新的贝叶斯融合算法。结果表明,与现有的融合重构算法相比,改进的融合重构算法至少将损伤定位精度误差减少了58%,定位误差不大于5 mm。

Bayesian fusion method has good positioning results on damage of CFRP,but there will be obvious positioning errors in the sensor network edge area.In order to solve the problem that the original algorithm is inaccurate in the layered damage localization of the sensor network edge,firstly,the localization effect of Bayesian fusion algorithm composed of two different damage factors on damage of CFRP is analyzed in this paper,and the feasibility of Bayesian fusion algorithm formed by ToF damage factor on the damage localization and accurate for the edge layereddamage localization are verified.Then,on the basis of the probabilistic imaging algorithm formed by the damage factor DI,the linear weight is replaced by the exponential weight with faster attenuation,and the improved probabilistic imaging algorithm is re-substituted into the algorithm framed by Bayesian to form a new Bayesian fusion algorithm.The results show that compared with the existing fusion reconstruction algorithm,the improved fusion reconstruction algorithm reduces the error by at least 58%,and the positioning error is not more than 5 mm.