基于LMD和阶次跟踪分析的滚动轴承故障诊断

变转速工况下的滚动轴承故障振动信号具有多分量调制以及故障特征频率受到转频调制的特点,从而导致故障特征提取困难。对此,将局部均值分解(local mean decomposition,简称LMD)与阶次跟踪分析相结合,提出了一种变转速工况下的滚动轴承故障诊断方法。首先,采用阶次跟踪采样将时域滚动轴承故障振动信号转换到角域;然后,对角域信号进行LMD分解得到若干个乘积函数(product function,简称PF)分量;最后,对各个PF分量的瞬时幅值进行频谱分析,判断滚动轴承的故障部位和类型。通过对滚动轴承实验故障振动信号的分析,结果表明该方法能有效地应用于变转速工况下的滚动轴承故障诊断。

Roller Bearing Fault Diagnosis Based on LMD and Order Tracking Analysis

The roller bearing fault vibration signals in the rotating speed-varying condition have characteristics that include multi-component modulation. Fault characteristic frequency is modulated by rotating frequency, which makes fault characteristics extraction difficult. In light of this, a new roller bearing fault diagnosis method is proposed that combines local mean decomposition (LMD) with order tracking analysis. First, the roller bearing fault vibration signals in the time domain are transformed into an angle domain using order tracking sampling. Then, the angle domain signals are decomposed by LMD to obtain a set of product functions (PFs). Finally, the instantaneous envelope of each PF is analyzed using the frequency spectrum to determine the roller bearing fault position and type. The results from the experimental fault vibration signals of the roller bearing demonstrate that the proposed method can be effectively applied to the roller bearing fault diagnosis in the rotating speed-varying condition.