形态滤波在滚动轴承故障声发射信号处理中的应用

Morphology filters for analyzing roller bearing fault using acoustic emission signal processing

滚动轴承的状态监测和故障诊断意义重大。为有效诊断轴承早期的微弱故障,将形态滤波方法用于轴承故障声发射信号的处理,提出采用多尺度形态开闭和闭开组合的滤波器对信号降噪处理,采用闭运算对降噪后的信号进行形态滤波解调得到明显的故障特征频率,并对比故障振动信号和声发射信号的处理效果。研究表明:形态滤波用于轴承故障声发射信号特征提取效果明显,适用于轴承的状态监测和故障的早期诊断。

Condition monitoring and fault diagnostics are very important for roller bearing production. Morphological filtering was used to process the acoustic emission signals to find the small faults in an early stage. The morphological filter was combined with multi-scale opening-closing and closing-opening models to eliminate noise, with the morphological closing operation then used to demodulate the de-noised signals and obtain the defect characteristic frequency. Comparison of the predicted effects of the vibration signals and the acoustic emission signals shows that the morphological filtering satisfactorily extracts the defect characteristics of the acoustic emission signals for early fault diagnosis of roller bearings