基于Teager能量算子和EEMD的滚动轴承故障诊断方法

针对应用集合经验模态分解(ensemble empirical mode decomposition,EEMD)方法难以提取强噪声背景下滚动轴承微弱故障特征的问题,提出了将最小熵反褶积(minimum entropy deconvolution,MED)和小波阈值去噪与EEMD相结合的改进方法.先采用MED对滚动轴承振动信号降噪,增强冲击特征;然后利用基于EEMD的小波阈值去噪方法处理降噪后信号得到一组固有模态分量(intrinsic mode function,IMF),并依据相关系数准则剔除虚假分量;对重构后信号进行Teager能量算子解调分析,提取其微弱故障特征.通过仿真信号和实验台信号验证了该改进方法的有效性.

Fault Diagnosis of Rolling Bearing Based on Teager Energy Operator and EEMD

As it was difficult to extract weak fault feature of rolling bearings with the method of ensemble empirical mode decomposition ( EEMD) , a modified EEMD was proposed. First the method of minimum entropy deconvolution ( MED ) was used to restrain the noise and highlight the impulse components of vibration signals. Second the signals were decomposed into different intrinsic mode function ( IMF) by using EEMD, and then the sensitive IMFS were selected and false IMFS were eliminated to reconstructed the new signals. Third the noise components of IMFS were restrained by the method of wavelet-threshold. The power spectrum could be used to obtain the weak fault features by using Teager Energy Operator at last. The diagnosis results with the simulation signals and experimental data of inner race faults, had indicated the effectiveness and accuracy of the method.