基于自相关和经验模态分解的转子碰摩故障分析

针对转子故障振动信号具有周期性的特点,提出一种用于分离转子故障振动信号的新方法,该方法首先应用自相关处理对振动信号进行降噪处理,然后采用经验模态分解(EMD)对振动信号进行分解,得出各个本征模态函数(IMF),并对IMF进行频谱分析,从频谱图上可以清晰地观察出转子的故障特征频率.试验结果表明,振动信号经自相关处理后起到了很好的降噪效果,碰摩所产生的冲击信号上下不对称;EMD分解方法能有效地突出故障特征频率成分,该方法可广泛用于旋转机械振动信号时频分析领域.

Analysis on Rotor Rubbing Fault Based on Autocorrelation and Empirical Mode Decomposition

Early rotor fault signal has a specific trait of periodicity,a comprehensive analysis method for rotor faults features extraction is proposed.Firstly,autocorrelation process is used to reduce the noise of vibration signal.Secondly,the vibration signal is conducted decomposition by using EMD (empirical mode decomposition).And each intrinsic mode function (IMF) is obtained.Thirdly,spectrum analysis is carried out to each IMF.The faults feature frequencies of rotor can be clearly observed.The experimental results show that the autocorrelation has stronger de-noising ability,and EMD can extract fault features of rotor faults better.The suggested method can be applied widely to vibration signal time-frequency analysis field in rotating machinery.