能量比在轴承故障评估中的应用研究

Application of Energy Ratio in Bearing Fault Assessment

提出了一种新的用于轴承故障评估的特征提取方法,用AR模型将振动信号分离为确定性信号与随机信号,将随机信号与确定性信号的能量比作为反映轴承损伤发展过程的特征。应用该方法对凯斯西楚大学轴承预置故障试验数据和IMS中心轴承全寿命数据进行了分析。结果表明:能量比在定工况、变工况条件下较传统特征能够更为有效地反映轴承的损伤发展过程。

A newly developed feature extraction method is presented for bearing fault assessment. The AR model is used to separate the vibration signal into deterministic periodic signal and random signal. The energy ratio between random signal and deterministic periodic signal is calculated and taken as the feature of bearing damage development. At the end, the Case Western Reserve University＇s bearing preset fault data and the IMS center＇s run -to -failure data are analyzed by using this method. The results show that bearing fault features obtained by using traditional vibration analysis methods fail to show the bearing damage process while the fault features extracted using the proposed method give consistent bearing degradation trends under varying operation condition.