基于VMD和谱峭度的滚动轴承早期故障诊断方法

Incipient fault diagnosis method for rolling bearing based on VMD and spectral kurtosis

针对滚动轴承故障信号为多分量非平稳振动信号、故障早期特征微弱诊断困难的问题,该文提出变分模态分解(VMD)结合谱峭度的滚动轴承早期故障诊断方法。首先对振动信号进行VMD分解得到若干分量信号,选择峭度最大分量作为最优分量,然后对最优分量进行快速谱峭度计算并进行带通滤波、凸显故障冲击成分,通过分析滤波信号包络谱中故障频率成分实现故障诊断。实验数据分析结果表明该方法能有效诊断轴承早期故障,有一定的工程应用价值。

As the problem of the fault signal of rolling bearing is a multi -component and non -stationary vibration signal, which is difficult to diagnose when the signal has weak initial features .A fault diagnosis method based on variational mode decomposition （ VMD ） and spectral kurtosis （ SK ） was proposed in the paper . Firstly, the vibration signal was decomposed into several component signals by VMD , and the component which had the maximum kurtosis and had the most fault impact components was selected as the optimal component . Then , the fast SK was computed to the optimal component for band pass filtering and highlighting the fault impact components . Finally, the fault was diagnosed by analyzing the fault frequency appeared in the filtered signal envelope spectrum . The experimental analysis results show that the proposed method can diagnose bearing incipient faults effectively and it has certain engineering application value .