基于VMD与多特征融合的齿轮故障诊断方法

Fault Diagnosis Method of Gear based on VMD and Multi-feature Fusion

针对实际中工况复杂难以提取齿轮故障特征频率的问题,提出一种变分模态分解(Variational Mode Decomposition,VMD)与多特征融合的齿轮故障诊断方法。首先,对机械振动信号进行VMD分解并得到一系列的模态,其次,计算高频段的前4个模态的排列熵(Permutation Entropy,PE)和能量,最后,将排列熵和能量构成的高维特征向量作为最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的输入,对齿轮故障类型进行模式识别。试验结果表明:VMD可以较好地将复杂多分量信号各成分分开;排列熵和能量特征可以从不同尺度揭示齿轮故障信息;基于VMD与多特征融合的智能故障诊断方法识别精度高,可以为齿轮故障预警和严重程度提供参考。

Aiming at the problem that working condition is complex in fact so that it is difficult to extract the gear fault feature frequency,a method of gear fault diagnosis based on variational mode decomposition（ VMD） and multi- feature fusion is proposed. Firstly,the mechanical vibration signal is decomposed into a series of modes by using VMD method. Then,the permutation entropy（ PE） value and energy of the first four modes of high frequency is calculated. Finally,the high- dimensional feature vectors composed of PE and energy are transformed as input of least squares support vector machine（ LS- SVM） to identify gear fault types.The experimental results indicate that VMD can better separate complex multi- components signal from each other,the PE and energy feature can reveal the gear fault information from different scales,the intelligent fault diagnosis method based on VMD and multi- feature fusion can identify the operating conditions of gear accurately and provide reference for the gear fault warning and severity.