摘要
将希尔伯特振动分解(HVD)应用于滚动轴承故障诊断。在介绍HVD方法原理基础上,与经验模式分解(EMD)进行对比表明,通过仿真信号可分析HVD更高频率分辨率,HVD能有效分解引起EMD模态混叠的含异常事件信号;将HVD用于滚动轴承故障信号分解,选含丰富故障信息分量进行包络分析,利用相应包络谱图识别轴承故障特征频率,进而识别故障模式,并实验验证该方法的有效性。
A new non-stationary signal processing technique called Hilbert vibration decomposition （HVD）was introduced to fault diagnosis of roller bearings.The HVD and empirical mode decomposition （EMD）are both based on Hilbert transform,and both methods can decompose multi-component signals adaptively.However,compared with EMD, the HVD method does not involve spline fitting and empirical algorithms and has a better frequency resolution.Moreover, the HVD method can decompose more effectively the multi-component signals which can cause mode mixing while decomposed by the EMD method.Based on this consideration,the HVD method was applied to the experimental data of roller bearing with induced faults.The envelope analysis was performed on the component including dominant fault information,and then the characteristic defect frequency of roller bearing was identified by means of the envelope spectrum.The experimental results validate the effectiveness of the proposed method for roller bearing fault diagnosis.
出处
《振动与冲击》
EI
CSCD
北大核心
2014年第14期160-164,共5页
Journal of Vibration and Shock
