北京航空航天大学学报 ›› 2009, Vol. 35 ›› Issue (8): 1005-1008.

• 论文 • 上一篇    下一篇

基于粗神经网络的民用飞机故障诊断

刘永建, 朱剑英, 夏洪山   

  1. 南京航空航天大学 民航学院, 南京 210016
  • 收稿日期:2008-06-27 出版日期:2009-08-31 发布日期:2010-09-16
  • 作者简介:刘永建(1972-),男,陕西扶风人,博士生,lyj3924111@sina.com.

Fault diagnosis for civil aviation aircraft based on rough-neural network

Liu Yongjian, Zhu Jianying, Xia Hongshan   

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2008-06-27 Online:2009-08-31 Published:2010-09-16

摘要: 针对传统神经网络故障诊断过程中网络训练时间长、结构复杂以及仅能进行单值输入的缺陷,设计了一种基于粗神经网络的民用飞机故障诊断系统.将粗糙集理论应用在神经网络的前端对民用飞机故障样本数据进行约简处理,以此去除冗余属性的干扰,克服了无关样本数据对网络学习性能的影响,简化了网络结构;利用粗神经元代替传统神经元,提高了网络性能,扩展了网络的应用范围.通过对空中客车A320飞机的故障诊断试验验证了该方法的有效性.

Abstract: To solve the defects of traditional fault diagnosis neural network, such as long training time, complex structure and single-valued input, a fault diagnosis system for civil aircraft based on rough-neural network was proposed. Rough set theory was applied to the front-end neural network to reduce the data of civil aircraft fault sample so as to remove the disturbance of redundant attributes, and overcome the impaction of unrelated data that imposed on the performance of network learning, simplify network structure. Secondly, by using the rough neurons instead of the traditional neurons, the performance of network was improved, and the scope of the application of network was expanded. The effectiveness of this method was verified by Airbus A320 aircraft fault diagnosis test .

中图分类号: 


版权所有 © 《北京航空航天大学学报》编辑部
通讯地址:北京市海淀区学院路37号 北京航空航天大学学报编辑部 邮编:100191 E-mail:jbuaa@buaa.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发