基于经验模式分解的全息谱故障识别方法

Rotor Fault Classification Using Empirical Mode Decomposition Based Holospectrum

由于实测信号中的一些噪声干扰会影响全息谱对信号分析的准确性,采用经验模式分解(empirical modede composition,简称EMD)方法进行信号滤波以提高识别的可靠性。应用EMD对信号分解,并结合互相关系数对内蕴模式分量(intrinsic mode function,简称IMF)进行滤波,在此基础上对信号进行重构,以降低噪声干扰,并对实际测试信号进行有效提纯。最后,对滤波后的转子信号进行全息谱分析,并通过分析实际转子碰摩信号来验证该方法的有效性。

Holospectrum is widely used to rotor condition classification.The accuracy is usually affected by the noise interference from measured signals.According to the practical problem for rotor signal analysis,empirical mode decomposition(EMD) was applied to signal filter to improve reliability for condition classification.Firstly,EMD was used for signal decomposition and intrinsic mode function(IMF) was obtained.Then,in order to suppress the noise interference,the IMF data were filtered by using the cross correlation function and the signals were reconstructed.Lastly,the reconstructed signal was employed for holospectrum analysis.A rotor rubbing fault was studied as an example to testify the effectiveness of the method.