基于超声导波和优化贝叶斯融合算法的金属结构损伤识别方法研究

Research on Metal Structure Damage Identification Based on Ultrasonic Guided Walve and Optimal Bayesian Fusion Algorithm

金属板状结构作为高速列车等大型交通运输工具的关键结构,对其进行损伤检测一直是该领域的热点研究问题。文章提出了一种改进的基于贝叶斯框架下的椭圆轨迹法与基于概率的损伤重构算法的融合算法来解决原有算法存在的数据量大及外部误差影响定位精度等问题。在原有融合算法的基础上对相关系数进行分析,设定合理的相关系数阈值筛选得到有效路径,通过去除无关路径达到数据轻量化的目的。计算有效路径的飞行时间,将飞行时间与相关系数作为损伤特征输入贝叶斯框架中,进而得出损伤位置。为验证路径筛选的有效性及筛选后损伤定位的准确性,进行了仿真与试验。结果表明,改进算法不仅可以实现损伤的精确定位,而且可以减少数据处理量,损伤定位绝对误差小于2 cm,相对误差小于0.67%。

In the key structures of high-speed trains and other large-scale transportation vehicles,metal plate structure damage identifcation is a hot research issue.In this paper,an improved algorithm is proposed to solve the problems of large amount of data and external error affecting positioning accuracy,which is the fusion ellipse trajectory method and RAPID method based on Bayesian framework.On the basis of the original fusion algorithm,the correlation coefficient of the damage sensitive features is analyzed.After setting a reasonable threshold,the experimental data of correlation path are selected.Then,the flight time of the remaining path after screening is calculated,and the flight time and correlation coefficient are input into the Bayesian framework as eigenvalues to obtain the damage location.In order to verify the possibility of path screening and the accuracy of damage location after screening,this paper is verified by simulation and experiment.The results show that the absolute error of the improved algorithm is less than 2 cm,and the relative error is less than 0.67%.Therefore,the algorithm can accurately locate the damage and reduce the amount of data to be processed.