Fault Diagnosis of Engine Misfire Based on Genetic Optimized Support Vector Machine

LU Di; DOU Wen-juan

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Fault Diagnosis of Engine Misfire Based on Genetic Optimized Support Vector Machine

更新时间：2026-07-10

- Fault Diagnosis of Engine Misfire Based on Genetic Optimized Support Vector Machine
- Vol. 17, Issue 1, Pages: 35-38(2012)
- 作者机构：
  
  哈尔滨理工大学电气与电子工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TK407
- Published：2012
- 稿件说明：
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LU Di, DOU Wen-juan. Fault Diagnosis of Engine Misfire Based on Genetic Optimized Support Vector Machine[J]. 2012, 17(1): 35-38.
DOI：

LU Di, DOU Wen-juan. Fault Diagnosis of Engine Misfire Based on Genetic Optimized Support Vector Machine[J]. 2012, 17(1): 35-38. DOI：

摘要

故障样本的缺乏严重制约智能故障诊断的发展

支持向量机算法的提出有效地解决了小样本学习问题.然而支持向量机算法中两个参数惩罚因子C和核参数γ对故障样本的准确识别起着决定性作用.针对参数较难选择问题

采用遗传算法对支持向量机中的两个参数进行全局寻优.把汽车在典型故障下尾气中各气体的体积分数作为训练样本

样本经过主成份分析实现降维和去相关.用处理过的样本和最优参数建立基于支持向量机的多元分类器模型

进行故障类别诊断.使用LIBSVM工具箱进行仿真

结果表明经遗传算法优化后的支持向量机对于小样本故障诊断有很高的准确率.

Abstract

The development of intelligent fault diagnosis is restricted by the shortage of the fault sample

in this paper

a new method-support vector machine is proposed

and then Genetic Algorithms is used to optimize the important parameters-penalty factor C and kernel parameter γ.In this paper

the engine fault diagnosis is regarded as training samples.The training samples realized dimension reduction and decorrelation by principal component analysis.Support vector machine model of engine fault diagnosis ...

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Related Institution

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