摘要
针对铝板早期疲劳损伤检测和损伤程度评估问题,本文提出了基于异常指数(AI)的铝板疲劳损伤量化评估方法。鉴于铝板疲劳损伤引起的结构系统响应的非线性非平稳和混沌动态特性,引入了Lamb波时/频变换与相空间重构理论对铝板损伤特征进行了多维度提取,并根据特征与损伤状态相关性及单调性进行了敏感特征筛选。将铝板损伤检测问题转换为在一组损伤敏感特征在状态描述空间中的二分类问题,采用自组织特征映射网络(SOM)对金属板正常状态和损伤异常状态进行辨识。为了进一步量化表征铝板损伤程度,采用SOM对损伤敏感特征进行了融合,采用AI值对铝板损伤状态进行了定量评估。仿真和实验的结果表明,本文提出的基于SOM的异常指数对铝板疲劳损伤演化具有较高的敏感性与较好的动态追踪能力,在铝板结构的健康监测与管理中既有较好的应用前景。
For the problem of early fatigue damage detection and damage degree assessment in aluminum plates,this paper proposes a damage quantitative assessment method based on anomaly index(AI).In view of the nonlinear nonstationary and chaotic dynamic characteristics of the structural system response caused by fatigue damage of aluminum plate,the signal time-frequency transformation and phase space reconstruction method are introduced to extract multidimensional damage features of aluminum plate,and the damage sensitive features are selected according to the monotonicity and the correlation between the features and damage degree.The aluminum plate damage detection problem is converted into a binary classification problem with a set of damage-sensitive features in the state description space,and a self-organizing feature mapping(SOM)network is used to identify the health status of aluminum plate.In order to further quantitatively characterize the damage degree of the aluminum plate,the SOM is used to fuse the damage sensitive features,and the AI values are used to quantitatively evaluate the damage state of the aluminum plate.The results of simulations and experiments showed that the SOM-based anomaly index proposed in this paper has high sensitivity and good dynamic tracking capability for fatigue damage evolution of aluminum plates,and has both good application prospects in the health monitoring and management of aluminum plate structures.
作者
刘小峰
张天瑀
柏林
Liu Xiaofeng;Zhang Tianyu;Bo Lin(School of Mechanical and Transportation Engineering,Chongqing University,Chongqing 400044,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2022年第10期115-122,共8页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(51975067,52175077)项目资助
关键词
超声导波
异常指数
损伤敏感特征
自组织特征映射网络
ultrasonic guided wave
anomaly index
damage sensitive features
self-organizing feature mapping network
