基于改进VMD形态谱和FCM的滚动轴承故障诊断方法

Fault Diagnosis Method of Rolling Bearing Based on Improved VMD Morphological Spectrum and FCM

针对滚动轴承故障信号的非线性特性及不同故障类型信号具有不同形态特征的特点,提出一种基于改进变分模态分解(VMD)形态谱和模糊C均值聚类(FCM)算法相结合的故障诊断方法。采用VMD方法对滚动轴承振动信号进行分解,针对分解过程中关键参数的选取,提出相关参数选择方法,并计算各固有模态函数(IMF)的能量波动系数,以获得对信号特征信息敏感的模态分量进行重构。计算重构信号的形态谱以反映信号的形态特征。通过FCM算法实现滚动轴承工作状态和故障类型的诊断。运用该方法对实测滚动轴承振动信号进行分析,并将所提方法同基于原始振动信号、经验模态分解、总体经验模态分解形态谱的故障特征提取方法进行对比。结果表明:所提方法能够更加有效提取滚动轴承信号的故障特征,实现故障类型的准确诊断。

Aiming at the nonlinear characteristics of rolling bearing fault signal and the vibration signal with different types of faults will have different morphological features,a rolling bearing fault diagnosis method based on improved variational mode decomposition(VMD)morphological spectrum and fuzzy C-means clustering was proposed.In this method,the original rolling bearing vibration signals were decomposed by VMD method,aiming at the selection of key parameters in the decomposition process,the selection method of relevant parameters was proposed and the energy fluctuation coefficients of each intrinsic mode function(IMF)were calculated to obtain the modal components sensitive to the signal characteristic information for reconstruction.Then,the morphological spectrum of the reconstructed signal was calculated to reflect the morphological characteristics of the signal.Finally,the working state and fault type of rolling bearing were diagnosed by FCM algorithm.Through analyzing the rolling bearing vibration signals,the proposed method was compared with the fault diagnosis methods based on original vibration signal morphological spectrum,empirical mode decomposition morphological spectrum and ensemble empirical mode decomposition morphological spectrum.The results show that this method can effectively extract the fault characteristics of rolling bearing signals and the accurate diagnosis of fault types is realized.