GEARBOX DIAGNOSIS BASED ON SVM OPTIMIZED BY DOUBLE GROUP COEVOLUTION FRUIT FLY OPTIMIZATION ALGORITHM

LEI Biao; HUI EnMing; GUAN HaiYing; WANG XiaoJun

doi:10.16579/j.issn.1001.9669.2022.03.035

您当前的位置：

首页 >

文章列表页 >

GEARBOX DIAGNOSIS BASED ON SVM OPTIMIZED BY DOUBLE GROUP COEVOLUTION FRUIT FLY OPTIMIZATION ALGORITHM

更新时间：2022-09-22

- GEARBOX DIAGNOSIS BASED ON SVM OPTIMIZED BY DOUBLE GROUP COEVOLUTION FRUIT FLY OPTIMIZATION ALGORITHM
- Journal of Mechanical Strength Vol. 44, Issue 3, Pages: 753-757(2022)
- 作者机构：
  
  1. 内蒙古机电职业技术学院机电工程系
  2. 华中科技大学国家数控系统工程技术研究中心
  3. 武汉华中数控股份有限公司
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2022.03.035
  CLC：
扫描看全文
LEI Biao, HUI EnMing, GUAN HaiYing, et al. GEARBOX DIAGNOSIS BASED ON SVM OPTIMIZED BY DOUBLE GROUP COEVOLUTION FRUIT FLY OPTIMIZATION ALGORITHM. [J]. 44(3):753-757(2022)
DOI：

LEI Biao, HUI EnMing, GUAN HaiYing, et al. GEARBOX DIAGNOSIS BASED ON SVM OPTIMIZED BY DOUBLE GROUP COEVOLUTION FRUIT FLY OPTIMIZATION ALGORITHM. [J]. 44(3):753-757(2022) DOI： 10.16579/j.issn.1001.9669.2022.03.035.

摘要

为提高果蝇算法(FOA)对支持向量机(SVM)参数优化的效果，对FOA的进化策略进行了改进，提出了双种群协调进化果蝇算法(DGCFOA)。将DGCFOA用于SVM参数优化并进行变速箱的故障诊断，诊断结果表明，DGCFOA算法能够搜寻到更优的SVM参数，相比于FOA,明显提升了变速箱故障诊断的精度。此外，与其他一些方法的对比结果也显示出DGCFOA得到的诊断精度更高，优势较为明显。

Abstract

In order to improve the optimization effect of the fruit fly optimization algorithm（FOA） on support vector machine（SVM） parameters, the evolution strategy of FOA was improved, and the duoble group coevolution fruit fly optimization algorithm（DGCFOA） was proposed in this paper. The DGCFOA was used to optimize the parameters of SVM and then used to gearbox fault diagnosis. Diagnosis results show that DGCFOA algorithm can obtained better SVM parameters when compared with FOA, it significantly improved fault diagnosis accuracy of gearbox. In addition, the diagnosis results also show that DGCFOA has higher diagnostic accuracy and more obvious advantages when compared with some other methods.

关键词

果蝇算法协同进化支持向量机变速箱故障诊断

Keywords

Fruit fly optimization algorithmCoevolutionSupport vector machineGearboxFault diagnosis

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

APPLICATION OF IMPROVED GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS

APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS

A FAULT DIAGNOSIS METHOD BASED ON SUPPORT VECTOR MACHINE OPTIMIZED BY IMPROVED FRUIT FLY OPTIMIZATION ALGORITHM

A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE

OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSIS

Related Author

No data

Related Institution

School of Energy and Power Engineering, University of Shanghai for Science and Technology

Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering

Department of Public Science,Henan Vocational College Of Nursing

School of Information Engineering,ZhengZhou Shengda University of Economics,Business ＆ Management

School of Electrical Engineering Jiujiang Vocational and Technical College

⁰