[1]王杰,王晓换..滚动轴承故障诊断虚拟系统的实现[J].郑州大学学报(工学版),2010,31(02):120.[doi:10.3969/j.issn.1671-6833.2010.02.029]
　WANG Jie,Wang Xiaochange.Implementation of a virtual system for rolling bearing fault diagnosis[J].Journal of Zhengzhou University (Engineering Science),2010,31(02):120.[doi:10.3969/j.issn.1671-6833.2010.02.029]
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滚动轴承故障诊断虚拟系统的实现()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
31卷
期数:
2010年02期
页码:
120
栏目:
出版日期:
2010-02-02

文章信息/Info

Title:
Implementation of a virtual system for rolling bearing fault diagnosis
作者:
王杰; 王晓换.
郑州大学,电气工程学院,河南,郑州,450001, 郑州大学,电气工程学院,河南,郑州,450001
Author(s):
WANG Jie; Wang Xiaochange
关键词:
滚动轴承;  故障诊断;  虚拟系统;  粗糙集;  BP神经网络
Keywords:
DOI:
10.3969/j.issn.1671-6833.2010.02.029
文献标志码:
A
摘要:
针对滚动轴承,实现了一种粗糙集理论和神经网络技术相结合的新型的故障诊断虚拟系统.该系统利用粗糙集对知识的约简能力,对采集的故障征兆数据进行预处理,即采用竞争学习神经网络把连续属性离散化,将结果导入Rosetta软件中逐步分析处理,得到最小条件属性集,在此基础上构建BP神经网络进行故障识别,将网络输出送回LabView进行显示.实例分析表明,该系统可以提高滚动轴承故障诊断的收敛速度,在期望误差相同的情况下,网络训练时间减小了176步.
Abstract:
For rolling bearings, a new type of fault diagnosis virtual system combining rough set theory and neural network technology is realized. The system uses the knowledge reduction ability of rough set to preprocess the collected fault symptom data, that is, the competitive learning neural network is used to discretize the continuous attributes, and the results are gradually analyzed and processed into Rosetta software to obtain the minimum conditional attribute set, on this basis, the BP neural network is constructed for fault identification, and the network output is sent back to LabView for display. Case analysis shows that the system can improve the convergence speed of rolling bearing fault diagnosis, and the network training time is reduced by 176 steps under the same expected error.

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更新日期/Last Update: 1900-01-01