计算机工程与应用Issue(12)：255-259,5.DOI:10.3778/j.issn.1002-8331.1307-0332
基于岭回归的RVPMCD滚动轴承故障诊断方法
Rolling bearing fault diagnosis method based on RVPMCD
摘要
Abstract
Aiming at the morbid problem on least-squares fit of variable predictive model, Ridge regression-Variable Predic-tive 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.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Key words
ridge regression/Ridge regression-Variable Predictive Model based Class Discriminate(RVPMCD)/rolling bearing/fault diagnosis分类
机械制造引用本文复制引用
杨宇,欧阳洪,潘海洋,程军圣..基于岭回归的RVPMCD滚动轴承故障诊断方法[J].计算机工程与应用,2015,(12):255-259,5.基金项目
国家自然科学基金（No.51175158，No.51075131）；湖南省自然科学基金（No.11JJ2026）；中央高校基本科研业务费专项基金资助项目。 （）
