RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM

LIU ZhiChuan; TANG LiWei; CAO LiJun

doi:10.16579/j.issn.1001.9669.2016.01.008

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RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM

更新时间：2022-09-22

- RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM
- Journal of Mechanical Strength Vol. 38, Issue 1, Pages: 38-43(2016)
- 作者机构：
  
  1. 军械工程学院火炮工程系
  2. 中国人民解放军73903部队
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2016.01.008
  CLC：
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LIU ZhiChuan, TANG LiWei, CAO LiJun. RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM. [J]. 38(1):38-43(2016)
DOI：

LIU ZhiChuan, TANG LiWei, CAO LiJun. RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM. [J]. 38(1):38-43(2016) DOI： 10.16579/j.issn.1001.9669.2016.01.008.

摘要

为了解决齿轮箱故障诊断模式识别过程中参数难以确定的问题,引入了一种基于等距特征映射(Isometric feature mapping,Isomap)和改进遗传算法(Improved genetic algorithm,IGA)优化支持向量机(Support vector machine,SVM)参数的方法。首先在自适应最优邻域参数下,对齿轮箱振动信号高维特征集数据进行等距特征映射,通过改进的遗传算法优化支持向量机的惩罚参数和核函数参数,最终实现对降维后数据的识别分类。将所提方法应用于齿轮箱故障诊断,结果表明,所提方法具有较高的诊断正确率,与传统的支持向量机方法相比有更好的诊断效果。

Abstract

A method for optimizing support vector machine（ SVM） parameters was proposed based on isometric feature mapping（ Isomap） and improved genetic algorithm（ IGA）,to solve the problem that parameters are difficult to choose in the process of gearbox fault diagnosis and recognition. Firstly Isomap was used for high-dimensional feature data of gearbox vibration signal under the automatic optimal neighborhood parameter value. Then optimized the castigation parameter and nucleus function parameter of SVM by improved genetic algorithm. Finally the recognition and classification of lower dimensional data were realized by SVM. The proposed method was applied to gearbox fault diagnosis. The result shows that the method has higher diagnosis accuracy,and it has better diagnosis effect compared with traditional SVM method.

关键词

等距特征映射遗传算法支持向量机齿轮箱故障诊断

Keywords

IsomapGenetic algorithmSupport vector machine（SVM）GearboxFault diagnosis

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