基于ITD和灰色关联度的轴承故障诊断方法

Fault Diagnosis Method for Rolling Bearings Based on ITD and Grey Incidence Degree

根据滚动轴承故障信号的非平稳特点及振动信号的强噪声背景,提出一种基于固有时间尺度分解(ITD)和灰色关联度的轴承故障诊断方法。首先利用ITD法将轴承振动信号分解为若干个固有旋转分量,进而提取故障特征参数,然后通过计算标准故障模式与待识别样本的灰色关联度对轴承故障类型进行判断。实例表明,ITD法可较好地分解轴承故障振动信号,结合灰色关联度可成功用于轴承故障在线监测与诊断,有效识别轴承工作状态。

According to the strong noise background of vibration signals and the non-stationary characteristics of fault signals for rolling bearings,a fault diagnosis method for bearings is proposed based on intrinsic time-scale decomposi-tion（ITD）and grey incidence degree.Firstly,the vibration signals for bearings is decomposed into a finite number of proper rotation components to extract fault feature parameter using the ITD method,then fault type of bearings is judged by calculating the grey incidence degree between unknown sample and standard fault pattern.The results show that ITD method can effectively decompose fault vibration signals for bearings and this method which combined with grey inci-dence degree can be successfully applied in the on-line monitoring and diagnosis of fault for bearings to identify work-ing conditions of bearings effectively.