基于变分模态分解与最小熵解卷积的齿轮故障诊断

Gear fault diagnosis based on variational mode decomposition andminimum entropy deconvolution approach

摘要:

为了准确地进行齿轮故障诊断,结合变分模态分解和最小熵解卷积,给出了一种新的故障诊断方法。首先,以包含啮合频率的分量的包络峭度最大作为原则,确定变分模态分解的分量个数；然后,将齿轮振动信号运用变分模态分解,得到多个分量；选取包含啮合频率的分量作为敏感分量；接着,应用最小熵解卷积,将敏感分量降噪；最后,应用包络分析技术进行故障诊断。通过齿轮断齿故障振动数据的分析,验证了方法的有效性。

Abstract:

In order to diagnose the gear fault accurately, a mode based on variational mode decomposition (VMD)and minimum entropy deconvolution (MED) was proposed. Firstly, the number of intrinsic mode functions (IMFs)was set based on the kurtosis of the IMF included mesh frequency maxima principle. Secondly, the vibration signal of gear was decomposed into some IMFs by VMD, then the IMF included mesh frequency was selected as the sensitive component. Thirdly, the sensitive component was de-noised by MED. Finally, the fault was diagnosed by the envelope aptitude spectrum. The analysis of broken tooth of gear fault data shows that the method can realize the fault diagnosis effectively.