齿轮噪声以及变转速运行是干扰滚动轴承故障诊断的常见因素，针对上述两种因素同时对滚动轴承故障诊断的干扰，引入了集合经验模态分解( EEMD) 方法以去除轴承信号中的齿轮噪声，将齿轮峰值啮合倍频( IDMM) 等效为滚动轴承的瞬时转频，阶比跟踪以去除转速变化对滚动轴承故障诊断的影响，并将此方法命名为基于EEMD 和IDMM的滚动轴承故障诊断方法． 仿真和实测信号分析证明该方法能有效去除齿轮噪声和转速变化对滚动轴承故障诊断的影响，同时提高故障特征阶比的幅值，节省转速测量装置．

In order to improve the fault diagnosis accuracy of rolling bearings that are commonly interfered by gear noise and variable rotating speed,a fault diagnosis method based on the ensemble empirical mode decomposition ( EEMD) and the instantaneous dominant meshing Multiply ( IDMM) is proposed.The proposed method employs EEMD to eliminate the gear noise in bearing vibration signals,transfers IDMM into an instantaneous rotating frequency,and uses the order tracking to overcome the decrease of diagnosis accuracy due to rotating speed variation.Simulated and experimental results show that the proposed method is effective in eliminating the interferences caused by gear noise and rotating speed variation,thus improving the characteristic order amplitude of faults.Accordingly,the installation of tachometers can be avoided.