针对目前高速旋转机械通过建模求解各阶固有频率复杂、困难问题，提出一种基于随机子空间（Stochastic Subspace Identification, SSI）和经验模式分解（Empirical Mode Decomposition, EMD）相结合的系统模态参数识别方法。该方法首先通过对转子系统运行工况下的振动响应进行分段滤波获取各阶模态自由衰减分量，然后引入EMD对处理后的信号进行分解，降低模态混叠及其他信号成分对模态参数识别的影响。其次，以EMD分解后的数据为基础，构建Hankel矩阵，通过SVD分解和卡尔曼状态滤波得到系统的随机状态模型，并对状态矩阵进行特征值分解，从而得到系统的一阶与二阶模态的固有频率。仿真分析和转子实验台的实际测试结果表明，该方法可以有效识别运行工况下转子系统的一、二阶固有频率，为后续系统特性分析和故障辨识提供新的思路。

Aiming at deficiency in identifying modal parameters of a high speed rotor system using modeling approach, a new method for modal parameters recognition of a rotor system based on the empirical mode decomposition (EMD) and the stochastic subspace identification (SSI) was proposed here. Free damped response components of the rotor system’s various modes were obtained with the narrow band-pass filtering algorithm from the rotor system’s vibration responses under operation condition, and then EMD was used to decompose the processed signals and decrease the effects of mode mixing and other signal components. The random state model matrix of the rotor system was derived from a Hankel matrix constructed after using EMD with the singular value decomposition (SVD) and Kalman state filtering. The first and second order modal frequencies of the rotor system were identified with eigenvalue decomposition of the random state model matrix. The effectiveness of the proposed method was verified with simulation analysis and actual tests on a rotor system rig.