基于信源估计和频域反卷积的滚动轴承故障特征分离与辨识

Fault Separation and Recognition of Rolling Bearings Based on Source Number Estimation and FDBD

针对轴承故障的振动特征由于受到强振源的抑制作用而增加了故障分离与辨识难度的问题,建立了基于信源估计和频域反卷积的故障诊断方法。利用小波包分解将信号分离成多个子带信号,并和奇异值分解相结合,解决欠定条件下的信号源数估计问题;根据估计的源数,选取相应维数的观测信号,通过短时傅里叶变换、复数域独立分量分析、相关排序、短时傅里叶逆变换,完成频域反卷积的分析过程,实现故障特征的分离与提取。仿真信号和实验数据均验证了该方法在故障特征分离与微弱特征辨识中的有效性。

According to the problems that the vibration features of bearing faults were hard to separate and recognize in strong vibration source inhibition, a diagnosis method was established based on source number estimation and FDBD algorithm. Wavelet packet decomposition was used to divide the signals into multiple sub band signals, and SVD was selected to estimate the signal source numbers in underdetermined conditions. The multiple dimension signals were constructed based on the source number estimation. The FDBD algorithm, which included STFT, fast-ICA in complex domain, rele- vance ranking and inverse STFT, was finally applied on fault feature separation and extraction. The effectiveness of the method was validated in fault feature separation and weak feature recognition by the simulation signals and experimental data of rolling bearing faults.