旋转设备中变转速工作模式和齿轮噪源是影响滚动轴承故障诊断的关键性难题，现有的方法虽然取得一定进展，但是对辅助设备的依赖以及方法步骤繁琐等问题依然突出，因此提出了基于峰值啮合倍频（Instantaneous Dominant Meshing Multiply,IDMM)和经验模式分解（Empirical Mode Decomposition,EMD)的滚动轴承故障诊断方法。该方法首先采用峰值搜索算法从混合信号的时频图中提取齿轮啮合倍频趋势线，将该趋势线等效为轴承转频对混合信号进行等角度重采样；其次对重采样信号进行EMD分解得到本征模态函数（Intrinsic Mode Function,IMF)之和，计算各IMF分量与重采样信号的互相关系数后根据预设的互相关系数阈值选取合适的IMF分量；最后对选取的IMF分量进行包络谱分析，进而判断轴承是否发生故障。仿真和实测信号分析证明该方法在无转速测量装置的情况下能有效去除齿轮噪声对滚动轴承故障诊断的影响。

Time-varing rotational speed and gear noise are key problems of rolling element bearing fault diagnosis in rotating machinery.Although there are some effective algorithms for them,the problems that rely on auxiliary equipment and complicated steps are still outstanding.A new method of rolling element bearing fault diagnosis based on Instantaneous Dominant Meshing Multiply(IDMM) and Empirical Mode Decomposition (EMD) is proposed.The new method firstly extract IDMM from time-frequency representation by peak searching algorithm,IDMM is equivalent to bearing rotating frequency,signal resampling by IDMM and mixed signal.Then,the method decomposes resampled signal by EMD,calculates cross-correlation coefficient between IMFs and resampled signal and selects IMFs by threshold value of cross-correlation coefficient.Finally,the selected IMFs are analyzed with envelope spectrum.The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals.