CAJ | 학술논문

针对多变量预测模型模式识别方法中的最小二乘拟合可能出现病态的问题，提出了基于岭回归的多变量预测模型（Ridge regression-Variable Predictive Model based Class Discriminate，RVPMCD）分类方法，该方法通过引入岭参数，降低其均方拟合误差，减小自变量间复共线性关系对参数估计的影响，改善了原方法中最小二乘回归拟合参数失真的现象，从而有望建立更加准确的预测模型。对滚动轴承的振动信号提取特征值，组成特征向量，采用RVPMCD方法对训练样本建立预测模型，利用RVPMCD所建立的预测模型进行模式识别。实验分析结果表明，基于岭回归的多变量预测模型分类方法可以更有效地对滚动轴承的工作状态和故障类型进行识别。
침대다변량예측모형모식식별방법중적최소이승의합가능출현병태적문제，제출료기우령회귀적다변량예측모형（Ridge regression-Variable Predictive Model based Class Discriminate，RVPMCD）분류방법，해방법통과인입령삼수，강저기균방의합오차，감소자변량간복공선성관계대삼수고계적영향，개선료원방법중최소이승회귀의합삼수실진적현상，종이유망건립경가준학적예측모형。대곤동축승적진동신호제취특정치，조성특정향량，채용RVPMCD방법대훈련양본건립예측모형，이용RVPMCD소건립적예측모형진행모식식별。실험분석결과표명，기우령회귀적다변량예측모형분류방법가이경유효지대곤동축승적공작상태화고장류형진행식별。
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.