| 用分层抽样和复Morlet小波识别短样本模态参数 |
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| 引用本文： | 汤宝平,章国稳,孟利波,何启源.用分层抽样和复Morlet小波识别短样本模态参数[J].重庆大学学报(自然科学版),2009,32(12):1381-1385. |
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| 作者姓名： | 汤宝平 章国稳 孟利波 何启源 |
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| 作者单位： | 汤宝平,TANG Bao-ping(重庆大学,机械传动国家重点实验室,重庆,400030;重庆交通科研设计研究院桥梁结构动力学国家重点实验室,重庆,400067);章国稳,何启源,ZHANG Guo-wen,HE Qi-yuan(重庆大学,机械传动国家重点实验室,重庆,400030);孟利波,MENG Li-bo(重庆交通科研设计研究院桥梁结构动力学国家重点实验室,重庆,400067) |
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| 基金项目： | 国家高技术研究发展计划(863计划)，霍英东青年教师基金，中国博士后科学基金 |
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| 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.
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| 关 键 词： | 模态参数识别 Morlet小波 分层抽样 短样本 |
Modal parameters identification for short data sequences based on stratified sampling and optimism complex Morlet wavelet |
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| TANG Bao ping,ZHANG Guo wen,MENG Li bo and HE Qi yuan.Modal parameters identification for short data sequences based on stratified sampling and optimism complex Morlet wavelet[J].Journal of Chongqing University(Natural Science Edition),2009,32(12):1381-1385. |
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| Authors: | TANG Bao ping ZHANG Guo wen MENG Li bo and HE Qi yuan |
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| Institution: | State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, P. R. China; State Key Laboratory of Bridge Structure Dynamics, Chongqing Communication Research &Design Institute,Chongqing 400067, P. R. China;State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, P. R. China;;State Key Laboratory of Bridge Structure Dynamics, Chongqing Communication Research &Design Institute,Chongqing 400067, P. R. China;State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, P. R. China; |
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| Abstract: | 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. |
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| Keywords: | modal parameters identification Morlet wavelet stratified sampling short data sequences |
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