基于局部均值分解与形态学分形维数的滚动轴承故障诊断方法

针对滚动轴承振动信号通常具有非线性与低信噪比特点，提出基于局部均值分解（Local Mean Decomposition，LMD）与形态学分形维数的滚动轴承故障诊断方法。采用LMD将滚动轴承振动信号分解为若干个乘积函数（Product Function，PF）分量，计算包含有滚动轴承故障特征的PF分量形态学分形维数，并将其用作特征量判断滚动轴承工作状态及故障类型。实验分析结果表明，该方法能有效用于滚动轴承的故障诊断。

A roller bearing fault diagnosis method based on local mean decomposition and morphological fractal dimension

The roller bearing vibration signals usually have the characteristics including nonlinearity and low signal to noise ratio. In view of this problem, a roller bearing fault diagnosis method based on local mean decomposition (LMD) and morphological fractal dimension is proposed. In this method, firstly, the roller bearing vibration signal is decomposed into a set of product functions (PFs) by LMD, and then the morphological fractal dimension of PF which contain the roller bearing fault characteristics is calculated, and it as the characteristic parameter to judging the roller bearing working conditions and fault types. The analytical results from the experimental roller bearing vibration signals indicate that this method can be applied to the roller bearing fault diagnosis effectively.