VMD的AR模型和关联维数在齿轮故障特征提取中的应用

针对齿轮故障信号的非线性及常伴有大量噪声干扰的问题,提出一种基于变分模态分解(VMD)的自回归(AR)模型和关联维数相结合的故障特征提取方法。该方法采用VMD将齿轮振动信号分解为一系列固有模态函数(IMF),通过频域互相关系数准则选取对信号特征敏感的IMF分量进行信号重构,对重构信号建立AR模型,并以AR模型自回归参数的关联维数作为特征量对齿轮的工作状态和故障类型进行识别。通过实测齿轮振动信号的分析,证明了所提方法的有效性。

Application of VMD based AR mode and correlation dimension in fault feature extraction for gear

Aiming at the problem that gear fault signal is non-linear and always accompanied by noise,a fault feature extraction mathod for gear based on variational mode decomposition(VMD)based auto regressive(AR)mode and correaltion dimension was proposed.The gear vibration signal was decomposed by VMD and a number of intrinsic mode functions(IMFs)were obtained.The sensitive IMFs were selected by calculating the frequency domain cross correlation coefficient of each IMF component.The reconstructed signal based on the sensitive IMFs,which were selected by calculating the frequency domain cross correlation coefficient of each IMF component.The AR model of the reconstructed signal were constructed.Finally,the correaltion dimensions of auto-regressive parameters in AR model were calculated and as feature vector to identify the working state and fault type of gear.Through the analysis of the measured vibration signal of gear,the effectiveness of the proposed method was verified.