基于时序AR与灰色GM模型的滚动轴承故障诊断研究

Roller Bearing Fault Diagnosis based on Grey AR Combination Model

针对滚动轴承故障诊断方法存在的问题,将自回归(AR)模型和灰色理论应用到滚动轴承的故障诊断中,建立了灰色时序组合模型。该方法先把轴承振动信号分解成不同特征时间尺度的固有模态函数,从而把非平稳信号处理转化为平稳信号处理问题,然后选取表征轴承故障的IMF分量,通过灰色GM(1,1)模型模拟数据宏观变化趋势,并用时序AR(p)模型建立了残差序列以模拟数据微观变化趋势。通过对实测数据进行检验与比较,证明该组合模型具有很好的分析效果。

Aiming at the problems of roller bearing fault diagnosis,gray theory and auto-regressive combination forecasting model is put forward,and the combination model has been build. The methodology developed decomposes the signal in intrinsic oscillation modes first,to translate the non-stationary signals into stationary signals. Then the autoregressive (AR) model of the selected IMF is established. The rough trend of the wear particle content change can be reflected through gray theory,and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of graphic data,the result shows that the combination model has a better forecasting result.