基于核主元分析和高斯混合模型的轴承转子状态评估

Condition Evaluation of Bearing-rotor Based on Kernel Principal Component Analysis and Gaussian Mixture Model

针对轴承转子运行状态评估问题,提出了基于核主元分析与高斯混合模型的新方法。采用小波包变换得到信号在各频带上的能量谱,然后通过核主元分析提取特征空间的主成分;并对其进行高斯混合模型建模。通过EM算法进行参数估计,由高斯混合模型的重合度对轴承转子的运行状态进行评估。通过仿真的轴承转子振动数据的验证发现,核主元分析能够使信号在特征空间的能量更加集中,在此基础上计算的高斯混合模型的重合度,能够更好地表征轴承转子的运行状态。

A new approach for condition evaluation of bearing-rotor based on Kernel Principal Compo- nent Analysis and Gaussian Mixture Model is proposed. The wavepacket energy spectrum of signal is ob- tained firstly. Then, the principal component in feature space is extracted by Kernel Principal Component Analysis. The processed data are used to model the Gaussian Mixture Model and the parameters are esti- mated by EM algorithm. The equipment condition is evaluated by overlap of Gaussian Mixture Models. The approach is validated by emulational bearing rotor＇ s vibration data. Testing results show that the kernel principat component analysis can concentrate the energy of the signal in feature space more efficient- ly. The performance of the overlap of gaussian mixture model based on the proposed method can evaluate bearing-rotor codition more saitable.