基于LMD和MSEE的滚动轴承复合故障特征提取方法

Extraction Method for Compound Fault Features of Rolling Bearings Based on LMD and MSEE

针对滚动轴承复合故障特征相近、不易区分的问题,提出了一种基于局域均值分解和多尺度熵能量的滚动轴承复合故障特征提取方法。首先,将信号进行LMD处理,得到一系列PF分量;然后,通过相关系数选择合适的PF分量计算能量并获得新的时间序列;最后,计算新时间序列的多尺度熵,与能量结合构建MSEE进行故障特征提取。机械故障模拟试验台的结果表明:该方法不仅降低了噪声干扰,而且提升了特征提取的精度,可以定量表征滚动轴承复合故障信号的特征,在滚动轴承复合故障信号中有良好的特征提取效果,与单独使用MSE和能量的特征提取方法相比,故障诊断率分别提升了8. 33%和11. 29%。

The compound fault features of rolling bearings are similar and not easy to distinguish,and a extraction method for compound fault features of rolling bearings is proposed based on local mean decomposition(LMD)and multi-scale entropy energy(MSEE).Firstly,the signal is decomposed by LMD,obtaining a series of PF components.Then,the appropriate PF components are selected by correlation coefficients to calculate energy,and the new time series are obtained.Finally,the multi-scale entropy of new time series is calculated and combined with energy to build MSEE for fault feature extraction.The results obtained by mechanical fault simulation test bench show that the method not only reduces noise interference,but also improves accuracy of feature extraction,which characterizes features of compound fault signal of rolling bearings quantitatively.The fault diagnosis rate of proposed method increases by 8.3%and 11.3%compared with feature extraction method using MSE and energy,which has favorable feature extraction effect in compound fault signal of rolling bearings.