摘要
在滚动轴承故障诊断研究中,同步提取变换(SET)在分析单分量信号时能显著提高时频可读性,但在分析多分量信号时存在时频模糊问题。为解决这一问题,首先通过变分模态分解(VMD)解调分离出信号的固有模态函数(IMF)分量;再对IMF分量进行希尔伯特变换,凸显故障特征频率;然后对变换后的IMF分量进行SET处理,实现时频聚焦提高时频分辨率,获得信号的时频解调谱,从而识别故障。经试验验证,该方法能准确提取信号的时频解调谱,识别故障频率及其倍频,诊断轴承故障。
In the research of rolling bearing fault diagnosis,synchronous extraction transform(SET)can significantly improve the time-frequency readability when analyzing single component signals,but it has the problem of time-frequency ambiguity when analyzing multi-component signals.In order to solve this problem,firstly,the intrinsic mode function(IMF)component of the signal is separated by the variational mode decomposition(VMD)demodulation.Then,the IMF component is transformed by Hilbert transform to highlight the fault characteristic frequency.Finally,the transform component is processed by SET to achieve time-frequency focusing,and obtain the time-frequency demodulation spectrum of the signal,so as to identify the fault.The experiment results show that the method can accurately extract the time-frequency demodulation spectrum of the signal,identify the fault frequency and its frequency doubling,and diagnose the bearing fault.
作者
唐衡
夏均忠
杨刚刚
孔有程
TANG Heng;XIA Junzhong;YANG Ganggang;KONG Youcheng(Fifth Team of Cadets,Army Military Transportation University,Tianjin 300161,China;Military Vehicle Engineering Department,Army Military Transportation University,Tianjin 300161,China)
出处
《军事交通学院学报》
2021年第3期25-31,共7页
Journal of Military Transportation University
