基于形态滤波优化算法的滚动轴承故障特征提取方法

针对轴承故障振动信号的非线性、非平稳性的特点,而且故障信号经常被各种噪声、干扰所淹没,提出了一种基于局部均值分解(local mean decomposition,LMD)与自适应多结构元素多尺寸差值形态滤波器相结合的方法。原始故障信号先经过局部均值分解得到若干乘积函数(product function,PF)分量,然后采用峭度值准则,选取峭度值最大的PF分量,再将其经过自适应多结构元素多尺寸差值形态滤波器进行滤波解调,最后解调结果进行频谱分析,提取故障特征。为了体现其可行性和优越性,与包络解调、LMD-形态闭运算和LMD-形态差值滤波三种方法进行了比较,仿真信号和实测轴承故障信号的分析结果表明,它具有更强的噪声抑制和脉冲提取能力,可以有效地提取滚动轴承故障特征信息,实现故障的精确诊断。

Method to Extract Fault Characteristics of Rolling Bearings Based on an Optimized Morphological Filter Algorithm

In view of the non-stationary and non-linear bearing fault vibration signals, this paper describes a new approach to extract rolling bearing fault features based on LMD (Local Mean Decomposition) and adaptive morphological filter of multi-structure elements and multi-scale. First, get product function (PF) components from the original fault signal by LMD. Then select the one with highest kurtosis and demodulate it with the adaptive morphological filter of multi-structure elements and multi-scale. At last, analyze the frequency of the demodulation results and extract the fault features. Stimulation experiments show that this method can better avoid the influence of noise and extract impulses from the original signals to effectively diagnose rolling bearing fault, comparing with three other methods which use envelope demodulation,LMD-morphological close method and LMD-morphological difference filter.