基于谱相干滤波冲击增强的轴承故障诊断

针对优化特征改进包络谱(IESFO)存在早期故障弱特征提取能力较弱和对频率分辨率要求较高的不足,提出了基于谱相干滤波冲击增强的轴承故障特征提取方法。首先使用IESFO算法选取优化解调频带并进行带通滤波;然后对滤波后信号使用多点优化最小熵反褶积(MOMEDA)算法增强信号中轴承故障产生的冲击;最后进行包络分析。基于实测信号的研究结果表明,和现有方法相比,本方法在轴承性能退化过程中可以提前提取到轴承早期故障信息,且可用于提取滚动轴承复合故障信息。

Fault diagnosis of rolling element bearing using MOMEDA impact enhancement based on spectral correlation filtering

To further improve the newly proposed improved envelope spectrum via feature optimisation-gram ( IESFO), which may be invalid on early fault weak faulty characteristic extraction of rolling elements bearing and required high frequency resolution, the MOMEDA impact enhancement based on spectral correlation filtering is proposed to extract the fault characteristics of rolling element bearing. Firstly, the optimized demodulation frequency band is determined by IESFO algorithm for band-pass filtering. Then, the impact of bearing failure of the filtered signal is enhanced by the multipoint optimal minimum entropy deconvolution adjusted ( MOMEDA) algorithm. Finally, envelope analysis is performed. The research results based on actual measured signals show that the proposed method can detect the incipient fault information of bearings earlier than most existing methods during the performance degradation process of rolling elements bearing. Another, the proposed method can also be utilized to extract the characteristic frequency of bearing fault under compound fault condition.