基于S变换与奇异值中值分解的滚动轴承故障诊断

Fault diagnosis of rolling bearing based on S transform and singular value median decomposition

为了有效地提取出滚动轴承故障信号的冲击特征,提出了一种基于S变换时频谱和奇异值中值分解(SVMD)算法的滚动轴承故障诊断方法。首先,利用S变换对滚动轴承原始振动信号进行了时频变换,得到了其时频系数矩阵,通过SVMD对时频系数矩阵进行了计算,筛选出合适的奇异值用以降噪;然后,通过仿真的方式,对结果进行了S逆变换,以获得信号的时域冲击特征;最后,以滚动轴承(型号N205)外圈、滚动体故障为例,进行了故障信号冲击特征提取实验,通过对轴承的外圈和滚动体故障数据分析处理,对基于ST-SVMD算法的有效性进行了验证。研究结果表明:通过采用基于ST-SVMD算法,得到了滚动轴承外圈的故障频率,该频率与该型号轴承特征频率基本一致;基于ST-SVMD算法,得到了滚动轴承滚动体的故障频率,该频率与该型号轴承特征频率基本一致;该结果证明,基于ST-SVMD算法在滚动轴承故障信号冲击特征的提取方面是有效的。

In order to effectively extract the impact characteristics of the fault signal of rolling bearing,a fault diagnosis method of rolling bearing based on S-transform time spectrum and singular value median decomposition(SVMD)algorithm was proposed.Firstly,time-frequency transformation was performed by the S transform on the vibration signal to calculate the time-frequency coefficient matrix.The time-frequency coefficient matrix was calculated through SVMD,and suitable singular values were screened for noise reduction.Then,by means of simulation,the S inverse transformation was performed on the result,the purpose of transformation was to obtain the time-domain impact characteristics of the signal.Finally,taking the outer ring and rolling element fault of rolling bearing(model N205)as an example,the impact feature extraction experiment of fault signal was carried out.The effectiveness of ST-SVMD algorithm was verified by analyzing and processing the fault data of outer ring and rolling element of bearing.The results show that,based on the ST-SVMD algorithm,the failure frequency of the outer ring of the rolling bearing is obtained,which is basically consistent with the characteristic frequency of the bearing.Based on the ST-SVMD algorithm,the fault frequency of rolling bearing is obtained,which is basically consistent with the characteristic frequency of the bearing.The results show that the ST-SVMD algorithm is effective in extracting the impact characteristics of rolling bearing fault signals.