针对齿轮故障特征往往被强背景噪声淹没的问题，提出一种改进EMD与形态滤波相结合的齿轮故障特征提取新方法。首先采用开－闭、闭－开级联而成的组合形态滤波器对原始故障信号进行消噪处理，然后通过EMD方法将包含在齿轮故障信号中的各个频率族信号分离，再采用互信息方法消除传统EMD分解结果中包含的虚假分量，最后利用分解得到的各阶固有模态函数为单一分量调制信号的特点，通过差值形态滤波的方式对分量信号进行解调以提取故障特征。齿轮故障实验信号的研究结果表明：该方法可有效的提取齿轮故障特征信息并抑制噪声，而且能够取得比传统包络解调分析更好的效果。

Fault feature was always hidden by strong noise background in gear fault signal. Based on improved EMD and morphological filter, a novel method was proposed to extract gear fault feature. Firstly, noise was reduced by morphological filter cascaded by closed and open. Secondly, mutual information was used to remove pseudo-components in EMD after frequency family was separated by EMD. Finally, difference morphological filter was used to demodulate component to extract fault feature since every component signal is a monocomponent demodulated signal. Experiment results show that the method is more effective than envelope demodulation in extracting fault feature and reducing noise.