基于PCA和MK-MOMEDA的特征频率提取算法及其应用

Feature Frequency Extraction Algorithm based on PCA and MK-MOMEDA and Its Application

针对柔性薄壁轴承故障特征频率提取的问题,提出了主成分分析(PCA)与多点最优调整的最小熵解卷积(MOMEDA)相结合的特征频率提取算法。算法中用PCA对原始信号作降噪处理,获得重构信号,利用多点峭度(MKurt)提取重构信号中的周期性冲击信号的周期,对理论周期进行修正,进而得到精确的解卷积周期,通过MOMEDA对重构信号进行增强,突出其周期性冲击,可以更有效地提取特征频率。将此方法应用到柔性薄壁轴承的故障特征频率提取上,并与最大相关峭度解卷积(MCKD)算法作对比。结果表明,该方法可将轴承故障冲击与因轴承长短轴交替而产生的周期性冲击分离,消除这种正常的周期性冲击的干扰,有效提取信号中的故障特征频率,效果优于最大相关峭度解卷积算法。

Aiming at the problem of extracting characteristic frequency of flexible thin-walled bearings,a feature frequency extraction algorithm combining principal component analysis(PCA)and multi-point optimally adjusted minimum entropy deconvolution(MOMEDA)is proposed.In the algorithm,PC A is used to perform noise reduction processing on the original signal to obtain a reconstructed signal.The multipoint kurtosis(MKurt)is used to extract the period of the periodic shock signal in the reconstructed signal,and the theoretical period is corrected to obtain an accurate deconvolution period,enhance the reconstructed signal through MOMEDA,highlight its periodic impact,and extract the characteristic frequency more effectively.This method is applied to the fault feature frequency extraction of flexible thin-walled bearings,and compared with the maximum correlation kurtosis deconvolution(MCKD)algorithm.The results show that this method can separate bearing fault shocks from periodic shocks caused by the alternation of the bearing’s long and short axes,eliminate the interference of such normal periodic shocks,and effectively extract the fault characteristic frequency in the signal.The effect is better than the maximum correlation kurtosis deconvolution algorithm.