北京航空航天大学学报 ›› 2010, Vol. 36 ›› Issue (3): 261-264.

• 论文 • 上一篇    下一篇

航空液压泵柱塞游隙增大故障诊断

赵四军, 王少萍, 尚耀星   

  1. 北京航空航天大学 自动化科学与电气工程学院, 北京 100191
  • 收稿日期:2009-02-24 出版日期:2010-03-31 发布日期:2010-09-13
  • 作者简介:赵四军(1979-),男,河南南阳人,博士生, windowsunzhao@yeah.net.
  • 基金资助:

    航空科技创新基金资助项目(08D51010); 863高科技计划资助项目(2009AA04Z412); 111计划资助项目

Fault diagnosis for piston head looseness of aero hydraulic pump

Zhao Sijun, Wang Shaoping, Shang Yaoxing   

  1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2009-02-24 Online:2010-03-31 Published:2010-09-13

摘要: 柱塞游隙增大是航空液压泵典型的渐进性故障之一,其故障特征模糊,样本有限,故障数据充满噪声,对其进行精确的故障诊断十分困难,因此提出了一种基于简约支持向量机的故障诊断方法.利用粗糙集对故障特征变量进行简约,去除冗余信息,在保证分类质量不变的前提下寻求覆盖系统故障特征的最小属性集合;将简约后的数据样本用来训练支持向量机进行故障分类.使用训练完成后的简约支持向量机进行故障诊断的实验结果表明,此种诊断方法适合于航空液压泵柱塞游隙增大的高精度故障诊断.

Abstract: Piston head looseness is a typical progressive failure of aero hydraulic pump. It is difficult to make precise fault diagnose because the fault feature is misty, the fault samples are insufficient and the measurable signals are full of structure coupling and noise besides failure feature. In order to solve above problems, a fault diagnosis method based on contracted support vector machine(SVM) was proposed. In the new method, rough set was utilized to reduce the fault characteristic value and eliminate redundancy in order to find the minimal attribute describing system fault characters on the premise of unchanged classification quality. The sample data disposed by rough set were used to train SVM to realize fault diagnosis. The experiment result of the trained contracted SVM shows that this diagnosis method is suitable for the high-precision fault diagnosis of the aero hydraulic pump.

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