铁道科学与工程学报Issue(3)：636-642,7.
基于小波包和贝叶斯分类的机车走行部滚动轴承故障诊断研究
Fault diagnosis of loco motive running gear rolling bearing based on wavelet packet and bayesian classification
摘要
Abstract
The status of the locomotive running gear rolling bearing is directly related to the locomotive perform-ance and the safe operation of the train.Aiming at solving such problem as low accuracy of the fault diagnosis and long model -construction time of locomotive running gear rolling bearing,this paper proposes a fault -diag-nosis method based on wavelet packets and Bayesian classification.The method need to construct fault feature set through wavelet packet transform,and make the use of rough set and principal component analysis to reduce the dimension,and then input the fault feature sets of dimension reduction before and after to the bayesian classifica-tion model to achieve fault diagnosis in turns,and finally make a comparison among the bayesian classification method and the neural network and least squares support vector machine method.The simulation results show that the time of building model with the method of naive bayes classification is shorter,and the classification ac-curacy is higher.关键词
机车走行部/滚动轴承/故障诊断/小波包/贝叶斯分类Key words
locomotive running gear/rolling bearing/fault diagnosis/wavelet packet/bayesian classification分类
信息技术与安全科学引用本文复制引用
陈二恒,贺德强,刘建仁,向伟彬,周继续..基于小波包和贝叶斯分类的机车走行部滚动轴承故障诊断研究[J].铁道科学与工程学报,2015,(3):636-642,7.基金项目
国家自然科学基金资助项目（51165001）；广西自然科学基金面上资助项目 （）
