局部均值分解方法及其在滚动轴承故障诊断中的应用

Local Mean Decomposition Method and Its Application to Roller Bearing Fault Diagnosis

研究了一种新的信号分析方法——局部均值分解(LMD)方法。LMD方法能自适应地将任何一个复杂信号分解为若干个瞬时频率具有物理意义的乘积函数(PF)分量之和,其中每个PF分量为一个包络信号和一个纯调频信号的乘积,因此LMD方法的本质是将多分量的信号分解为若干个单分量的调制信号,适合于处理多分量的调制信号。针对滚动轴承故障振动信号的调制特点,提出了基于LMD的滚动轴承故障诊断方法,对滚动轴承故障振动试验信号进行了分析,结果表明LMD能有效地应用于滚动轴承故障诊断。

A new signal analysis method, namely, the local mean decomposition（LMD） was studied. The LMD method could decompose any complicated signals into a set of product functions, each of which was the product of an envelope signal and a frequency modulated signal. Essentially the multi--component signal can be decomposed into a set of single--component modulated signals by LMD, therefore LMD was very applicable for processing multicomponent modulated signals. According to the modulating characteristics of the roller bearing fault vibration signals, the fault diagnosis method for roller bearings based on LMD was proposed. The analysis results from the actual roller bearing fault vibration signals demonstrate that the LMD method can be applied to the roller bearing fault diagnosis effectively.