计算机工程与应用2018，Vol.54Issue(6)：100-104,114,6.DOI:10.3778/j.issn.1002-8331.1610-0127
随机森林在滚动轴承故障诊断中的应用
Application of random forest on rolling element bearings fault diagnosis
摘要
Abstract
Due to selection difficulties for different bearing data feature, and low accuracy problems of single classifier method in the fault diagnosis of rolling bearing,this paper proposes a rolling bearing fault diagnosis algorithm with ran-dom forest based on Classification And Regression Tree(CART).Random forest is an ensemble learning method which contains a variety of classifiers. The accuracy of rolling bearing fault diagnosis is improved by"integrated"thought of random forest.First,time domain statistical indicators are extracted from the vibration signals of rolling bearings and will be used as feature vectors.Then,the random forest algorithm is utilized for the fault diagnosis of rolling bearing.Compared with the traditional algorithm(SVM,kNN and ANN)and single CART,diagnostic results proposed in this paper indicate that random forest algorithm has high diagnostic accuracy by using the bearing data of SQI-MFS experimental platform.关键词
滚动轴承/故障诊断/特征提取/随机森林Key words
rolling bearing/fault diagnosis/feature extraction/random forest分类
机械制造引用本文复制引用
张钰,陈珺,王晓峰,刘飞,周文晶,王志国..随机森林在滚动轴承故障诊断中的应用[J].计算机工程与应用,2018,54(6):100-104,114,6.基金项目
国家自然科学基金(No.NSFC 61403167). （No.NSFC 61403167）
