一种基于样本熵的轴承故障诊断方法

Sample entropy-based roller bearing fault diagnosis method

运用非线性动力学参数样本熵作为特征,对轴承正常、内圈故障、滚动体故障、外圈故障四种工况的振动信号进行分析识别。针对利用原始振动信号的样本熵只能在一个尺度域进行分析,无法准确区分轴承运行状况的问题,提出一种基于集成经验模式分解与样本熵的轴承故障诊断方法。首先利用集成经验模式分解方法将原始振动信号分解为有限个内蕴模式分量,从中选取包含故障主要信息的前几个内蕴模式分量的样本熵作为特征,然后利用支持向量机进行轴承故障诊断,这样可以在多个尺度对轴承信号进行分析,提高了轴承故障诊断的准确率。通过轴承故障实测信号的诊断实验,证明了该方法的可行性和有效性。

The nonlinear dynamic parameter sample entropy was used as a feature for roller bearing fault diagnosis.Vibration signals for normal bearings,those with inner race fault,ball one,and outer race one were used for analysis and diagnosis.The sample entropy of the original vibration signal could be analyzed only in one scale,but information about the characteristics of the vibration signal in different scales could give important information about the fault.A sample entropy method based on ensemble empirical mode decomposition（EEMD） was proposed here.Firstly,the original roller bearing vibration signal was decomposed with EEMD and the intrinsic mode functions containing the most information were chosen to calculate the sample entropy to form a feature vector.Then,SVM method was used as a classifier to identify different faults.Thus,the vibration signal could be analyzed in different scales to give more information about fault.Experimental results with real roller bearing data showed that the proposed method is effective.