基于SSD和Teager能量算子的滚动轴承故障诊断方法

Rolling bearing fault diagnosis method based on SSD and Teager energy operator

针对滚动轴承早期故障冲击信号较难提取的问题,提出基于奇异谱分解(singular spectrum decomposition,SSD)和Teager能量算子的滚动轴承故障诊断方法。首先,利用SSD分解振动信号得到一组不同频带分布的奇异谱分量(singular spectrum component,SSC);其次,根据峭度准则选取最佳SSC分量,利用Teager能量算子计算该分量的瞬时能量信号并对其进行傅里叶分析,从而得到信号的Teager能量谱;最后,根据能量谱图提取故障特征频率。将该方法运用到仿真信号和滚动轴承实测信号中,并和包络谱、EMD及EEMD方法进行对比分析,结果表明,该方法能有效解调故障特征信息,准确识别轴承故障类型,诊断效果更佳。

Aiming at the problem that the early fault shock signals of rolling bearings are difficult to extract,a fault diagnosis method based on singular spectrum decomposition(SSD)and Teager energy operator was proposed.Firstly,SSD was used to decompose the fault signal into a set of singular spectrum components(SSC)of different frequency bands.Then the optimal SSC component was selected according to the Kurtosis criterion,and the Teager energy operator was used to calculate the instantaneous energy signal of the component and Fourier analysis was performed to obtain the Teager energy spectrum of the signal.Finally,the fault characteristic frequency was extracted according to the energy spectrum.The proposed method was applied in simulated signals and rolling bearing measured signals,and compared with the envelope spectrum,EMD and EEMD methods.The results showed that the proposed method could effectively demodulate the fault feature information,accurately identify the bearing fault type,and the diagnostic effect was better.