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基于贝叶斯推断LSSVM的滚动轴承故障诊断
引用本文:杨正友,彭涛,李健宝,钟云飞.基于贝叶斯推断LSSVM的滚动轴承故障诊断[J].电子测量与仪器学报,2010,24(5):420-424.
作者姓名:杨正友  彭涛  李健宝  钟云飞
作者单位:1. 湖南工业大学电气与信息工程学院,株洲,412008
2. 湖南工业大学电气与信息工程学院,株洲,412008;北京理工大学自动化学院,北京,100081
基金项目:国家自然科学基金,中国博士后科学基金,湖南省科技厅科技计划,湖南省教育厅科技计划 
摘    要:针对传统最小二乘支持向量机分类器的参数选择具有随意性和不确定性等不足,采用贝叶斯推断方法通过三级分层推断优化确定最小二乘支持向量机的各参数,有效提高了最小二乘支持向量机的建模效率.将基于贝叶斯推断最小二乘支持向量机分类方法应用于滚动轴承故障诊断中,实验仿真结果表明该方法能有效地识别滚动轴承的故障,且训练时间和测试时间均小于传统最小二乘支持向量机方法。

关 键 词:滚动轴承  故障诊断  最小二乘支持向量机  贝叶斯推断

Bayesian inference LSSVM based fault diagnosis method for rolling bearing
Yang Zhengyou,Peng Tao,Li Jianbao,Zhong Yunfei.Bayesian inference LSSVM based fault diagnosis method for rolling bearing[J].Journal of Electronic Measurement and Instrument,2010,24(5):420-424.
Authors:Yang Zhengyou  Peng Tao  Li Jianbao  Zhong Yunfei
Affiliation:1.School of Electrical and Information Engineering,Hunan University of Technology,Zhuzhou,412008,China;2.College of Automatic Control,Beijing Institute of Technology,Beijing,100081,China)
Abstract:In order to remedy the randomicity and uncertainty in parameters selection,the least squares support vector machines(LSSVM) classifier's parameters are optimally selected by the Bayesian inference with three levels hierarchy,and the modeling efficiency is availably improved.Then,the Bayesian inference LSSVM classification method is applied to the fault diagnosis of rolling bearing.The experiment simulation results show that the proposed approach can identify availably the faults and has shorter training and testing time than traditional LSSVM.
Keywords:rolling bearing  fault diagnosis  least square support vector machine  Bayesian inference
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