短样本条件下提高HHT识别模态参数精度的方法  

Method of Improving Precision of Hilbert-Huang Transform Based Modal Parameter Identification for Short Data Sequences

作　　者：汤宝平[1,2] 何启源[1] 孟利波 章国稳[1] 

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400030 [2]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

出　　处：《振动．测试与诊断》2011年第1期6-10,124,共5页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(编号:50875272;50735008);霍英东教育基金会11届青年教师基金资助项目(编号:11057);第43批博士后科学基金资助项目(编号:20080430749)

摘　　要：由于在短样本条件下Hilbert-Huang变换(Hilbert-Huang transform,简称HHT)识别模态参数的精度主要受经验模态分解(empirical modal decomposition,简称EMD)模式混合和随机减量法(random decrement technology,简称RDT)提取自由衰减响应时平均次数不足的影响,针对这两个影响因素,引入带宽限制信号抑制EMD的模式混合,提高EMD的精度;并引入分层抽样技术,提出基于拟合偏差和样本量的层权确定方法来进行多次识别,然后加权平均,提高RDT的总平均次数。仿真试验和应用实例表明,结合分层抽样的限制带宽EMD识别模态参数的方法能提高短样本条件下HHT识别模态参数的精度。Because the precision of modal parameter identification based on Hilbert-Huang transform is affected by the mode mixture of empirical mode decomposition(EMD) and average times of random decrement technology(RDT),a novel modal parameter identification method using stratified sampling and bandwidth restricted EMD is proposed for short data sequences.A bandwidth restricting signal was employed in the procedure of EMD to restrain the mode mixture and to increase the frequency precision of EMD.In order to obtain more sub-data sequences of RDT, stratified sampling,which took the place of random sampling and had the ability to improve precision of sampling and total average times,was employed to extract free decrement response signal.The method based on fitting difference and sample size was proposed to determine the layer's weight of stratified sampling.The simulation and the application demonstrated that the proposed method is effective and capable of improving precision of HHT-based modal parameter identification for short data sequences.

关 键 词：HILBERT-HUANG变换 模态参数识别 短样本 限制带宽经验模态分解 分层抽样 

分 类 号：TB123[理学—工程力学] TH7[理学—力学]