关键词: 岭回归, 基于岭回归的多变量预测模型分类方法（RVPMCD）, 滚动轴承, 故障诊断

Abstract: Aiming at the morbid problem on least-squares fit of variable predictive model, Ridge regression-Variable Predictive Model based Class Discriminate（RVPMCD） is put forward. By introducing the ridge parameter on the method, the mean square error on fitting and the effect of multicollinearity on parameter estimation are reduced, and the distortion phenomenon of the least squares regression fit parameter in the original method is improved, therefore, more accurate prediction models can be built up. The feature value of vibration signal of rolling bearings is extracted as feature vector. Then, the RVPMCD method is used to establish prediction model of training samples, and eventually pattern recognition would be carried out by using the established prediction models. The experimental results show that the classification method based on Ridge Regression-Variable Predictive Model can identify work status and fault type of rolling bearings more effectively.

Key words: ridge regression, Ridge regression-Variable Predictive Model based Class Discriminate（RVPMCD）, rolling bearing, fault diagnosis