基于LMD和增强包络谱的滚动轴承故障分析

Fault Diagnosis for Roller Bearing Based on Local Mean Decomposition and Enhanced Envelope Spectrum

针对滚动轴承发生故障时振动信号幅值分布的峭度和歪度都会发生变化的特点,提出基于峭度-歪度的局部均值分解分量筛选准则,将峭度值和歪度绝对值最大的分量筛选出来并重构故障信号,以达到降噪的目的。对降噪后的信号进行增强包络谱分析,得到故障的特征频率。应用提出的新方法对实测的滚动轴承外圈、滚动体和内圈发生故障时的振动信号分别进行了分析。结果表明,基于峭度-歪度的局部均值分解分量筛选准则有效地降低了信号中的噪声,在此基础上应用增强包络谱有效地减少带内噪声影响,从而使故障特征信息凸现出来,有利于对滚动轴承的各种故障进行诊断。

To reduce signal noise, we proposed a kurtosis-skewness screening criteria (KSSC) of local mean decomposition (LMD). We chose the maximal kurtosis or absolute skewness values to reconstruct the signal, then analyzed the de-noising signal using the enhanced envelope spectrum (EES) to obtain the characteristic frequencies. Finally, we used the proposed method to analyze the vibration signals of roller bearings under outer raceway fault, ball fault and inner raceway fault. The signal noise was greatly reduced using the KSSC and EES, indicating promising applications for the diagnosis of all kinds of roller bearing faults. © 2017, Editorial Department of JVMD. All right reserved.