Fault Diagnosis of Motor Bearing Based on the Bayesian Network

被引：8
|
作者：
Li, Zhongxing [1 ]
Zhu, Jingjing [1 ]
Shen, Xufeng [1 ]
Zhang, Cong [1 ]
Guo, Jiwei [1 ]
机构：
[1] Jiangsu Univ, Sch Automot & Traff Engn, Zhenjiang 212013, Jiangsu, Peoples R China
来源：
INTERNATIONAL WORKSHOP ON AUTOMOBILE, POWER AND ENERGY ENGINEERING | 2011年 / 16卷
关键词：
Bayesian Network; fault diagnosis; symptom parameters;
D O I：
10.1016/j.proeng.2011.08.1046
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
On the basis of the analysis of vibration characteristics of motor bearing faults and the influence from testing noise on site, calculation of the symptom parameters representing the vibration signals according to the measured vibration signals is proposed, and the sensitivity analysis is carried out to these parameters which can refine effective symptom parameters. As there are limitations for motor bearing fault intelligent diagnosis methods based on genetic algorithm and neural network, while Bayesian network has a good learning, inference and astringency. Therefore, the effective combination of the symptom parameters and Bayesian network is made and a new intelligent diagnosis method is posed. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Society for Automobile, Power and Energy Engineering
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页数：9
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