基于变分模态分解与快速谱峭图的齿轮箱滚动轴承故障特征提取

Fault Feature Extraction of Gearbox Rolling Bearing based on VMD and Fastkurtogram

针对齿轮箱的滚动轴承故障信号因噪声干扰,难以进行有效提取的问题,提出了基于变分模态分解与快速谱峭图相结合的轴承故障特征提取方法。首先,利用变分模态分解(Variational Mode Decomposition,VMD)将振动信号分解成若干个本征模态分量(Intrinsic Mode Function,IMF),通过相关峭度计算选取故障信息最突出的分量信号;然后,利用快速谱峭图自适应地确定带通滤波;最后,对滤波后的信号进行平方包络谱分析,提取出故障信息。通过公开数据分析和齿轮箱轴承故障实验,证明了该方法的有效性和可行性。

The rolling bearing fault signal of the gearbox is difficult to extract effectively due to noise interference. A fault feature extraction method of gearbox rolling bearing based on VMD and fast-kurtogram is proposed. Firstly,the vibration signal of bearing is decomposed into several Intrinsic Mode Function(IMF) components by VMD,and the component signal with the most prominent fault information is selected by the correlated kurtosis,and then the bandpass filtering is adaptively determined by the fast-kurtogram. Finally,the filtered signal is subjected to squared envelope spectrum analysis to extract fault information. The effectiveness and feasibility of the proposed method are demonstrated by public bearing fault data analysis and gearbox bearing fault experiments.