基于EMD和自适应STFT的货车滚动轴承故障诊断

Fault Diagnosis of Freight Car Rolling Element Bearings with EMD and Adaptive Short_Time Fourier Transform

提出了一种结合经验模态分解(EMD)和自适应短时傅里叶变换(STFT)的铁路货车滚动轴承故障诊断方法.该方法应用EMD方法把滚动轴承振动信号分解成有限个平稳的内蕴模型函数(IMF);根据滚动轴承故障信号的分布特征,应用以三阶B样条函数作为窗函数、在不同频段自适应选取窗长的自适应STFT对第一个IMF分量进行时频分析和故障信息提取.本文还对197726型货车滚动轴承在外圈剥离、内圈剥离两种故障状态下的振动信号做了分析,结果表明,该方法可以有效地在时频域上获取故障信息,为铁路货车滚动轴承故展诊断提供了一种新的方法.

In this paper, a freight car rolling element bearings fault diagnosis method based on Empirical Mode Decomposition （EMD） and adaptive short-time Fourier transform （STFT） is proposed. Vibration signals collected from the rolling element bearings are firstly decomposed into a finite number of stationary Intrinsic Mode functions （IMFs）. With the distributing characteristic of the fault signals, the adaptive transform using cubic B-splines as the window functions and optimizing the window bandwidth along the frequency axis is then applied to the time-frequency analysis and feature enhancement of the first IMF. The results of the analysis of vibration signals of 197726 type rolling element bearings with outer-race and inner-race faults show that the method can perform effectively in fault diagnosis of freight car rolling element bearings.