RAILWAY VEHICLES PANTOGRAPH FAULT DIAGNOSIS BASED ON VARIATIONAL MODE DECOMPOSITION DISPERSION ENTROPY AND SVM

PAN Ning

doi:10.16579/j.issn.1001.9669.2022.03.034

您当前的位置：

首页 >

文章列表页 >

RAILWAY VEHICLES PANTOGRAPH FAULT DIAGNOSIS BASED ON VARIATIONAL MODE DECOMPOSITION DISPERSION ENTROPY AND SVM

更新时间：2022-09-22

- RAILWAY VEHICLES PANTOGRAPH FAULT DIAGNOSIS BASED ON VARIATIONAL MODE DECOMPOSITION DISPERSION ENTROPY AND SVM
- Journal of Mechanical Strength Vol. 44, Issue 3, Pages: 747-752(2022)
- 作者机构：
  
  1. 郑州旅游职业学院信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2022.03.034
  CLC：
扫描看全文
PAN Ning. RAILWAY VEHICLES PANTOGRAPH FAULT DIAGNOSIS BASED ON VARIATIONAL MODE DECOMPOSITION DISPERSION ENTROPY AND SVM. [J]. 44(3):747-752(2022)
DOI：

PAN Ning. RAILWAY VEHICLES PANTOGRAPH FAULT DIAGNOSIS BASED ON VARIATIONAL MODE DECOMPOSITION DISPERSION ENTROPY AND SVM. [J]. 44(3):747-752(2022) DOI： 10.16579/j.issn.1001.9669.2022.03.034.

摘要

为提高轨道车辆受电弓的故障诊断精度，提出了基于变分模态分解(VMD)色散熵(DE)和支持向量机(SVM)的受电弓故障诊断方法。首先，以VMD为受电弓各运行状态信号的分解方法实现信号的多尺度自适应分解，得到若干个本征模态函数(IMF)分量。其次，以DE为特征提取方法计算各IMF分量的DE值，并组成故障特征向量。最后，以SVM为模式识别方法对故障特征进行识别，得到诊断结果。受电弓故障诊断实例验证了方法的有效性。

Abstract

In order to improve the fault diagnosis accuracy of railway vehicles pantograph, a fault diagnosis method of pantograph based on variational mode decomposition（VMD） dispersion entropy（DE） and support vector machine（SVM） was proposed in this paper. Firstly, VMD was used as the fault signal decomposition method to realized pantograph signal in different states adaptively decomposition, and obtained several intrinsic mode functions（IMF）. Secondly, DE was used as feature extration method to calculated each IMF DE value and obtained the fault feature vectors. Finally, SVM was used as pattern recgnition method to identified the fault features and the diagnosis results are obtained. An example of pantograph fault diagnosis shows the effectiveness of the method.

关键词

变分模态分解色散熵支持向量机受电弓故障诊断

Keywords

Variational modal decompositionDispersion entropySupport vector machinePantographFault 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

ROLLING BEARING FAULT DIAGNOSIS BASED ON VARIATIONAL MODAL DECOMPOSITION AND LFOA-RVM

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

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

Information Network Center,ZhengZhou Tourism College

⁰