基于自相关与能量算子增强的滚动轴承微弱故障特征提取

Weak fault feature extraction of rolling bearing based on autocorrelation and energy operator enhancement

针对滚动轴承(rolling element bearings,REBs)早期故障振动信号冲击成分微弱,受噪声影响故障特征难以提取,提出了基于自相关和Teager能量算子增强的滚动轴承微弱故障特征提取法。利用自相关计算和经验模态分解(empirical mode decomposition,EMD),分别实现轴承振动信号整个频带随机噪声和低频噪声的抑制,突出故障冲击周期。同时,提出基于内禀模态函数(intrinsic mode function,IMF)能量比加权的互相关系数-峭度指标用于筛选最优IMF进行信号重构,强化重构信号中的故障信息。对重构信号作用Teager能量算子(Teager energy operator,TEO),得到故障冲击特征增强的瞬时能量序列,通过功率谱分析提取轴承故障特征频率。内圈故障仿真信号和滚动体故障实测信号分析表明,该方法能够有效抑制轴承振动信号噪声,对早期故障的微弱特征有显著增强作用。

Aiming at weak impact components in rolling bearing’s early fault vibration signal and difficult fault feature extraction due to noise influencing,a method for rolling bearing weak fault feature extraction based on autocorrelation and Teager energy operator enhancement was proposed.Autocorrelation calculation and empirical mode decomposition(EMD)were used to suppress random noise and low-frequency noise in the whole frequency band of bearing vibration signal,respectively and highlight fault impact period.At the same time,cross-correlation coefficient-kurtosis index based on IMF energy ratio weighting was proposed to screen out the optimal IMF for signal reconstruction and enhancing fault information in the reconstructed signal.Teager energy operator(TEO)was used to act on the reconstructed signal to obtain the instantaneous energy sequence with enhanced fault impact characteristics,and the bearing fault characteristic frequency was extracted through power spectral analysis.The analysis of inner race fault’s simulated signal and rolling element fault’s measured signal showed that the proposed method can effectively suppress bearing vibration signal noise and significantly enhance weak characteristics of early faults.