摘要
滚动轴承是异步电动机的重要部件,其运转状态直接影响到电机运行的安全性。因此在线监测轴承工作状态,及时发现轴承早期故障,对保证异步电动机长期可靠运行具有特别重要的意义。利用希尔伯特-黄变换对非平稳信号的敏感性,提出一种可有效提取轴承故障特征的新方法。对振动加速度传感器获取的轴承随机振动信号,进行经验模态分解,自适应地获得本征模态函数,继而对每组本征模态函数进行希尔伯特变换,得到时间-频率-能量三维希尔伯特谱,希尔伯特谱揭示了轴承不同工况的时频特征。试验结果进一步证明了该方法的有效性。
Rolling bearing is an important part of asynchronous motor, its running state has great effect on the safe operating of the motor. Therefore, it is significant to do some research on how to detect incipient faults in rolling bearing as soon as possible. A new method based on Hilbert-Huang Transformation （HHT） was developed. Firstly, Empirical mode decomposition（EMD） separated the stochastic vibration series to components with different time scale, say, intrinsic mode function（IMF）. Secondly, Hilbert transformation was applied to every IMF. Then, the spectrum in time and frequency domain, Hilbert spectrum, was got, it provided high-resolution time-frequency characteristics of hearing＇s different work states. It is demonstrated by the actual analysis that the new detection method is one effective way for the fault diagnosis of rolling bearing.
出处
《传感技术学报》
EI
CAS
CSCD
北大核心
2006年第3期655-657,661,共4页
Chinese Journal of Sensors and Actuators
