基于实时监测的轴承寿命预测方法

从凯斯西储大学轴承数据中心提取了斯凯孚(SKF)轴承内圈、滚动体和外圈不同故障尺寸的原始振动信号，故障尺寸分别为0.007，0.014和0.021 in（1 in=2.54 cm)，对其进行经验模态分解(EMD)，发现共有17个本征模函数。进行主成分分析(PCA)，发现内圈和外圈故障尺寸与第一主成分和第二主成分的关系可通过主成分拟合公式进行准确拟合，因此,通过实时监测轴承振动信号并进行信号分析，可获得内圈和外圈的故障尺寸，利用Pairs-Erdogan公式和有限元仿真方法对含故障轴承的剩余寿命进行了预测。该研究对预防由含裂纹轴承导致的机械事故具有重要的意义。

Bearing life prediction based on real-time monitoring

In this study, the original vibration signals of an Svenska Kullager-Fabriken (SKF) bearing's inner race, rolling element, and outer race with three fault sizes were extracted from Case Western Reserve University Bearing Data Center. The fault sizes were 0.007, 0.014, and 0.021 in(1 in=2.54 cm). By empirical mode decomposition (EMD), 17 intrinsic mode functions were found. Principal component analysis (PCA) was then conducted, and it was found that the relationship between the fault sizes of the inner and outer races and the first and second principal components could be accurately fitted by principal component fitting formulas. Therefore, the fault sizes can be obtained by real-time monitoring of vibration signals and signal analysis. Finally, the residual life of the bearing with a fault was predicted using the Paris-Erdogan formula and finite element simulation. This study has a significant meaning for prevention of equipment accidents caused by bearing cracks.