基于粗糙集和支持向量机的航空液压泵故障诊断

赵四军; 王少萍; 吴柯锐

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基于粗糙集和支持向量机的航空液压泵故障诊断

更新时间：2026-09-14

- 基于粗糙集和支持向量机的航空液压泵故障诊断
- 基于粗糙集和支持向量机的航空液压泵故障诊断
- Issue 3, (2010)
- 作者机构：
  
  1. 北京航空航天大学自动化科学与电气工程学院
  2. 空军驻山西地区军事代表室
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
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赵四军, 王少萍, 吴柯锐. 基于粗糙集和支持向量机的航空液压泵故障诊断[J]. 2010, (3).
DOI：

赵四军, 王少萍, 吴柯锐. 基于粗糙集和支持向量机的航空液压泵故障诊断[J]. 2010, (3). DOI：

摘要

针对航空液压泵故障样本有限、故障数据同时具有不完备和冗余的特点

分析了液压泵柱塞球头游隙增大故障的原因和特点

对比了故障泵相对于正常泵的频域信号特征

提出了一种基于粗糙集和支持向量机的液压泵故障诊断方法.首先利用粗糙集对故障特征变量进行约简

去除冗余信息

在保证分类质量不变的前提下寻求描述系统故障特征的最小属性集合;然后再将经过粗糙集约简后的数据样本用于训练支持向量机来进行小样本的故障诊断.试验结果表明:采用粗糙集和支持向量机相结合的诊断方法适合于航空液压泵的高精度故障诊断.

Abstract

Taking into account that fault samples of the aero hydraulic pump were insufficient and the fault data thereof were characterized by imperfection and redundancy

the reasons and characters of gap increase of the piston ball-head of a hydraulic pump were analyzed

the frequency domain signals of fault pumps and those of normal pumps was compared

and a fault diagnosis method based on rough set and Support Vector Machine(SVM) was put forward.The rough set was utilized to reduce the fault characteristic value and eliminate redundancy

thereby finding the minimal attribute to describe system fault characters on the premise of unchanged classification quality.And then

the sample data disposed by rough set were used to train SVM to realize fault diagnosis of small samples.The experiment result shows that the method

adopting both rough set and SVM

is suitable for high-precision fault diagnosis of the aero hydraulic pump.

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