摘  要：针对滚动轴承故障振动信号的非平稳性特点，提出一种改进经验模态分解（EMD）和形态滤波相结合来提取故障特征信息的方法。该方法首先在原信号中加入高频谐波并进行EMD分解，减小传统EMD分解中存在的模态混叠现象，然后从高频本征模态分量（IMF）中去除高频谐波得到故障冲击成分，经形态滤波消噪后进行频谱分析，提取出故障特征信息。信号仿真分析该方法的实施过程，并将该方法成功运用于滚动轴承内圈和外圈故障的诊断。实验结果表明该方法能够有效提取滚动轴承故障特征信息，实现故障诊断。
关键词：改进经验模态分解；形态滤波；滚动轴承；故障诊断
文献标志码：A       文章编号：1674-5124（2016）01-0121-05
Rolling bearing fault diagnosis based on improved EMD and morphological filter
WEN Cheng， ZHOU Chuande
（College of Mechanical and Power Engineering，Chongqing University of Science and Technology，
Chongqing 401331，China）
Abstract: A new technology is proposed to solve the non-stationarity in vibration signals of antifriction bearing faults in accordance with the improved empirical mode decomposition （EMD） and morphological filters. First， a high-frequency harmonic was added into the original signal and then decomposed by means of EMD to reduce the mode mixing phenomenon in traditional EMD. Next， the high-frequency harmonic was removed from the high-frequency intrinsic mode component （IMF） to obtain fault impact compositions. The fault characteristic information was extracted by spectrum analysis after morphological filter de-noising. At the same time， the above steps were simulated by signals. This method was applied to diagnose the faults in inner and outer races of antifriction bearings. The experimental results show that the method can extract the fault characteristics and diagnose the faults of antifriction bearings.
Keywords: improved empirical mode decomposition； morphological filter； rolling bearing； fault diagnosis