基于EEMD的滚动轴承故障特征主元分析和PNN建模

EEMD-Based Failure Characteristics Principal Component Analysis of Rolling Bearing and PNN Modelling

利用滚动轴承各种工作状态下测量得到的声发射信号,建立了一种基于总体平均经验模态分解(EEMD)与概率神经网络(PNN)的滚动轴承故障特征提取和诊断方法。通过EEMD对信号进行自适应时频分解,在不同频段上分析本征模态函数(IMF)分量;计算IMF的能量值并做能量贡献分析,确定主元分量以组成故障特征向量;利用PNN网络实现故障特征向量与故障模式之间的函数映射,进行故障诊断。仿真结果和试验数据的对比证明了提出方法的有效性。

Using acoustic emission signal measured under different running state of rolling bearing,a fault feature extraction and diagnosis method for rolling bearing based on the ensemble empirical mode decomposition（EEMD）and the probabilistic neural network（PNN）was built.Firstly,the EEMD method was applied to adaptively decompose the signal in time-frequency domain,and the intrinsic mode functions（IMFs）in different frequency bands were analyzed.Then the energy of IMFs was computed,and a failure feature vector was formed by the principal components selected according to the energy contribution analysis.Using PNN to approximate the functional mapping between the feature vector and the fault mode,the failure diagnosis could be realized.Comparison between simulation results and experiment data proves that the proposed method is effective.