信号经验模式分解与间断频率

Signal empirical mode decomposition and intermittency frequency

基于环境激励条件下结构的模态参数识别问题需要处理采集的数据信号来得到所需的参数信息.经验模式分解(EMD)通过筛分过程将原始信号分解成若干个基本模式分量(IMF),可看作无需预设带宽的自适应高通滤波方法.通过设置间断频率可以避免模态混叠,使每一个基本模式分量表示结构的某一阶固有模态.采用信号实例说明该方法的主要计算过程,分解结果表明该方法能有效对信号进行分解,方便模态参数识别.

Ambient vibration based modal parameter identification needs to extract information from measurement data using signal process method. Empirical mode decomposition （EMD） is a signal processing technique to decompose data set into several intrinsic mode functions （IMF） by a sifting process. EMD can be regarded as an adaptive high pass filter without setting a bandwidth in advance. By using intermittency frequency, the sifting process can overcome the difficulty of mode mixing and decompose data to modal response functions. Case study shows the applicability of the present technique. The result indicates that the signal can be decomposed efficiently and prepared for modal identification.