基于形态滤波与灰色关联度的滚动轴承故障诊断

针对滚动轴承故障振动信号的强噪声背景与故障样本不易大量获取的问题，提出一种基于形态滤波与灰色关联度的滚动轴承故障诊断方法。采用形态闭与形态开相减构成的差值滤波器对故障信号进行滤波，不需要考虑振动信号的频谱特征与分布，即能够有效的提取故障频率特征；灰色关联度分析方法对小样本模式识别问题具有良好的分类效果，适用于滚动轴承的故障模式识别。首先对故障信号进行形态滤波，然后提取滤波后信号的故障特征频率的归一化幅值作为特征向量，最后通过待识别样本与标准故障模式的关联度来对故障模式进行分类。实例表明该方法能够取得良好的效果。

Fault diagnosis of rolling element bearing based on morphological filter and grey incidence

Considering the strong noise background and lack of sample in fault diagnosis of rolling element bearing, a rolling element bearing fault diagnosis method was proposed based on morphological filter and grey incidence. Fault characteristic can be extracted by morphological difference filter without considering frequency spectrum of vibration signal. The method of grey incidence has good performance in small-sample classification and can be used in recognition of rolling element bearing fault pattern. The fault signal was filtered by morphological filter, the unitary magnitude of characteristic frequency extracted was considered as characteristic vector and then the fault pattern was classified by use of grey incidence between unknown pattern and standard fault pattern. Experiment results show the effectiveness of the method.