[1]郝伟,徐敏,张瑞林.神经网络聚类方法在旋转机械故障诊断中的应用研究[J].郑州大学学报(工学版),1995,16(04):7-12.
　Hao Wei,Xu Min,Zhang Ruilin.Application research of neural network clustering methods in rotating mechanical failure diagnosis[J].Journal of Zhengzhou University (Engineering Science),1995,16(04):7-12.
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神经网络聚类方法在旋转机械故障诊断中的应用研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
16卷
期数:
1995年04期
页码:
7-12
栏目:
出版日期:
1995-04-28

文章信息/Info

Title:
Application research of neural network clustering methods in rotating mechanical failure diagnosis
作者:
郝伟; 徐敏; 张瑞林
上海交通大学，郑州工学院
Author(s):
Hao Wei;  Xu Min;  Zhang Ruilin
Shanghai Jiaotong University, Zhengzhou Institute of Technology
关键词:
神经网络; 聚类学习方法; 旋转机械故障诊断
Keywords:
Neural network;  clustering learning method;  rotating mechanical failure diagnosis
文献标志码:
A
摘要:
在基于神经网络的聚类学习方法。分有监督学习方法和无监督学习方法。本文采用无监督学习方法对旋转机械中常见故障的分类进行了较为详细的研究，以此分类结果来达到故障诊断的目的，文中还具体描述了该算法的实现方法。研究结果表明，该方法克服了有监督学习方法的旋转机械故障诊断技术的某些缺陷，是进行大型旋转机械故障诊断的一种行之有效的方法。
Abstract:
In a clustering learning method based on neural networks. There are supervision learning methods and unsupervised learning methods. This article uses unsupervised learning methods to conduct more detailed research on the classification of common faults in rotating machinery. With this classification result, the purpose of fault diagnosis is also described in the article to describe the implementation method of the algorithm. The results show that this method overcomes some defects of rotating mechanical failure diagnostic techniques with supervision and learning methods, and is an effective method for the diagnosis of large -scale rotation mechanical failure.

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更新日期/Last Update: 1900-01-01