CS-VMD与Teager包络谱在齿轮故障特征提取中的应用

Application of CS-VMD and Teager envelope spectrum in gear fault feature extraction

针对传动系统中齿轮故障特征不明显、难以准确提取以及在进行模态分解时VMD输入参数需要人为设定的问题,提出一种CS-VMD与Teager包络谱相结合的齿轮故障特征提取方法。首先,使用CS算法对VMD方法的模态分解个数、二次惩罚因子和时间延迟进行参数自适应确定;其次,使用参数自适应确定的CS-VMD方法对齿轮故障仿真信号和实测信号进行模态分解;最后,对分解得到的各模态分量进行Teager包络谱分析,进而提取各模态分量中的齿轮故障特征。仿真与实测信号分析结果表明:所提出的方法可以有效地分解齿轮故障非平稳信号和提取齿轮故障特征频率,验证了该方法在齿轮故障特征提取中的可行性。

Aiming at the fault features of gears in the transmission system are not obvious,it is difficult to extract the fault features accurately and the input parameters of VMD need to be set artificially during the mode decomposition,a method of gear fault feature extraction based on the combination of CS-VMD and Teager envelope spectrum is proposed.Firstly,the number of mode decomposition,the secondary penalty factor and the time delay of VMD method are adaptively determined by CS algorithm;then the CS-VMD method with parameter self-adaptive determination is used to decompose the simulation signal and the measured signal of gear fault;Finally,Teager envelope spectrum of each obtained modal component is analyzed,so as to extract the fault features of gear in each modal component.Simulation and measured signal analysis results show that the proposed method can effectively decompose the non-stationary signals of gear faults and extract the feature frequencies of gear faults,which verifies the feasibility of this method in gear fault feature extraction.