摘要
针对大型结构短样本模态参数识别,提出基于分层抽样的最优复Morlet小波短样本模态参数识别方法。先对结构响应信号进行分层抽样,用随机减量法提取每一层的自由衰减信号;再根据样本标准差确定每一层的层权,用最优复Morlet小波识别每一层的模态参数;最后用层权对模态参数进行加权得到最终的模态参数。工程应用结果表明,所提方法具有较高的识别精度,良好的低频密集模态解耦和高频虚假模态抑制能力。
A novel modal parameter identification method based on stratified sampling and optimism complex Morlet wavelet is proposed for short data sequences. Stratified sampling is applied to divide the structure response signal into different layers which called sub-samples with different thresholds,and then free decrement response signal of each layer is extracted by random decrement technique. The optimism complex Morlet wavelet transform is applied to identify modal parameter of each layer,and the weight of the layer is also determined based on the sample standard deviation. The modal parameter of the structure can be obtained by weighted calculation.The engineering application shows that the proposed method has the ability to identify modal parameter accurately,decouple low-frequency intensive modal composition and restrain high-frequency fake modal effectively.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第12期1381-1385,共5页
Journal of Chongqing University
基金
国家高技术研究发展计划(No.2009AA04Z411)
霍英东教育基金会资助(11057)
博士后科学基金资助项目(20080430749)
关键词
模态参数识别
MORLET小波
分层抽样
短样本
modal parameters identification Morlet wavelet stratified sampling short data sequences
