作　　者： (刘正平）; (胡俊）; (胡俊锋）; (张龙）;

机构地区： 华东交通大学机电与车辆工程学院,江西南昌330013

出　　处： 《华东交通大学学报》 2017年第4期97-103,共7页

摘　　要： 针对滚动轴承故障诊断中因实测信号背景噪声较大、信噪比较低,导致表征轴承故障信息的周期冲击成分被淹没于噪声中无法有效提取故障冲击特征的问题,提出一种基于MED和Teager能量算子的故障诊断模型。首先对信号最小熵解卷积(MED)处理消除采样过程中传递路径的影响;而后以Teager能量算子对滤波信号的能量值进行跟踪,从信号能量的角度进一步消除滤波信号中的带内噪声,使故障冲击特征得到二次增强;最后通过包络谱分析得到诊断结果。仿真数据及实验数据分析验证了所提诊断模型的有效性。 In the fault diagnosis of rolling bearings,the background noise is big and the signal noise is low,which causes a problem that the periodic impact component of bearing failure information is submerged in the noise and the fault characteristics can not be extracted effectively. Aiming at such a problem, a fault diagnosis model based on MED and Teager energy operator was proposed in this study. Firstly,the influence of transfer path was eliminated in the process of sampling by the MED method. Then the energy values of the filtered signal were tracked by Teager energy operator to further eliminate the band noise signal, which enhanced the fault impact secondarily. Finally,the diagnosis results were obtained by envelope spectrum analysis. The validity of the proposed model was verified by simulation data and experimental data analysis.

关 键 词： 滚动轴承 最小熵解卷积 能量算子 故障诊断