基于主成分分析和随机森林回归模型的工艺装备轴承剩余寿命预测

Prediction of Residual Life of Jig Rolling Bearing Based on PCA and Random Forest Model

为解决传统随机森林回归模型对工艺装备轴承剩余寿命预测准确率偏低的问题,提出一种将PCA(主成分分析)和随机森林回归模型相结合的工艺装备轴承剩余寿命预测方法。首先,应用时域分析法对特征集进行提取,并和样本对应的剩余寿命标签共同创建并形成训练集;然后,利用PCA算法对训练集中特征实施降维处理;最后,建立随机森林回归模型,输出工艺装备轴承剩余寿命。研究结果表明:基于PCA算法和随机森林回归模型的预测方法将预测准确度提高了约10%,证实了该方法的有效性和准确性。

To solve the low prediction accuracy problem of the conventional random forest regression model for the residual life of jig rolling bearings,a method for predicting the residual life of jig rolling bearings combining PCA(principal component analysis)and random forest regression model is proposed.First,the feature set is drawn by time-domain analysis method,and jointly forms a training set with residual life label corresponding to samples;then,PCA algorithm is used to implement dimension reduction processing of the training set features;finally,a random forest regression model is established to predict the residual life of jig rolling bearings.Research results show that:the method based on the combination of PCA algorithm and random forest regression model improves the model prediction accuracy by about 10%,which confirms the effectiveness and accuracy of the method.