基于改进VMD和APSO-SVM的高速列车轴承故障诊断

张青松; 张兵; 秦怡

doi:10.13890/j.issn.1000-128X.2022.01.006

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基于改进VMD和APSO-SVM的高速列车轴承故障诊断

智能化技术专栏 | 更新时间：2024-08-02

- 基于改进VMD和APSO-SVM的高速列车轴承故障诊断
- Bearing fault diagnosis for high-speed train based on improved VMD and APSO-SVM
- 机车电传动 2022年第1期页码：31-36
- 作者机构：
  
  西南交通大学牵引动力国家重点实验室，四川成都 610031
- 作者简介：
  
  张青松（1996—），男，硕士，研究方向为轨道车辆故障检测与诊断；E-mail：zqs1294525593@163.com
- 基金信息：
  
  国家重点研发计划资助项目(2017YFB1201103-06);四川省科技计划资助项目(2019YFG0102)
- DOI：10.13890/j.issn.1000-128X.2022.01.006
  中图分类号： U266.2;
- 纸质出版日期：2022-01-10，
  
  收稿日期：2019-12-17，
  
  修回日期：2021-09-29，
- 稿件说明：
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张青松, 张兵, 秦怡. 基于改进VMD和APSO-SVM的高速列车轴承故障诊断[J]. 机车电传动, 2022,(1):31-36.

ZHANG Qingsong, ZHANG Bing, QIN Yi. Bearing fault diagnosis for high-speed train based on improved VMD and APSO-SVM[J]. Electric drive for locomotives, 2022,(1):31-36.
张青松, 张兵, 秦怡. 基于改进VMD和APSO-SVM的高速列车轴承故障诊断[J]. 机车电传动, 2022,(1):31-36. DOI： 10.13890/j.issn.1000-128X.2022.01.006.

ZHANG Qingsong, ZHANG Bing, QIN Yi. Bearing fault diagnosis for high-speed train based on improved VMD and APSO-SVM[J]. Electric drive for locomotives, 2022,(1):31-36. DOI： 10.13890/j.issn.1000-128X.2022.01.006.

摘要

针对高速列车轮对轴承故障信息微弱难以提取的问题，提出一种结合变分模态分解 (Variational mode decomposition

VMD)和粒子群算法参数优化支持向量机(Adaptive Particle Swarm Optimization-Support Vector Machine

APSO-SVM)的高速列车轴承振动信号故障特征提取和识别模型。为了避免

值选取不合理而导致VMD欠分解和过分解，文章从能量熵变化率趋势的角度出发，提出了VMD分解层数

的选择原则，然后将VMD分解获得的故障特征输入支持向量机(SVM)中进行不同轴承故障的识别。试验结果表明，传统的SVM对滚动体故障和复合类型故障诊断效果较好，但对保持架故障的诊断效果相对较差。因此，利用自适应的粒子群优化算法（APSO）对SVM 的核心参数进行优化，从而进一步改善对保持架故障的识别精度，实现了对多种常见高速列车轮对轴承故障的有效识别。

Abstract

Aiming at the problem that the fault information of high-speed train wheel bearing is weak and difficult to extract

a fault feature extraction and recognition model for vibration signal of high-speed train bearing based on variational mode decomposition and adaptive particle swarm optimization-support vector machine was proposed. To avoid the under-decomposition or over-decomposition of VMD

the selection principle of

was suggested from the perspective of energy entropy change rate. The fault features obtained by VMD were input into SVM for different bearing fault identification. The experimental results show that traditional SVM has better effect on the diagnosis of rolling element fault and composite type fault

but the diagnosis effect on cage fault is relatively poor. Therefore

APSO algorithm was used to optimize the core parameters of the SVM

which further improved the recognition accuracy of cage fault and realized the effective identification for the fault bearings of high-speed train.

关键词

高速列车变分模态分解参数优化粒子群算法轮对轴承故障诊断

Keywords

high-speed trainVMDparameter optimizationPSObearingfault diagnosis

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