摘要
基于以应变和应变率为状态变量的系统随机状态空间模型,比拟基于数据驱动的位移模态参数随机子空间识别方法,建立基于数据驱动的应变模态参数随机子空间识别方法,用于环境激励下的结构应变模态参数识别,并通过数值算例和实例对识别方法进行验证。数值算例计算结果表明:应变模态参数随机子空间识别法可在各种噪声情况下较好地识别出结构的曲率模态振型,而且识别的曲率模态振型对局部损伤很敏感,具有较强的抗噪能力;实测算例识别的应变模态振型也与理论振型较吻合,从而进一步验证本研究识别方法的实用性。
Based on the stochastic state-space model with strains and strain change rates as state variables,the data based stochastic subspace method for strain modal parameter identification was established like the one with displacement modal parameter identification,which can be used in the strain modal parameter identification for the structure under ambient excitation.A numerical-simulation and an experimented example were used to verify the identification method.The results of numerical example show that the strain models identified by the method proposed in this work are in good agreement with the theory models,and is very sensitive to local damage of the structure even with high noise level,therefore,it can be used in health monitor successfully for real structures.The identified strain model of experimented example agrees well with the theory model,which further demonstrates the practicability of the proposed method.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2012年第9期3601-3608,共8页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(51078357)
关键词
应变模态
数据驱动
随机子空间
参数识别
strain modal
data-based method
stochastic subspace
parameter identification
