Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD

ZHANG Dongxing; YANG Gang; ZHOU Ao; QIN Limu; WEI Yuqian; YAN Lei

doi:10.13890/j.issn.1000-128X.2022.02.015

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Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD

Key Technology and Materials | 更新时间：2024-08-02

- Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD
- Electric drive for locomotives Issue 2, Pages: 105-112(2022)
- 作者机构：
  
  1.西南交通大学机械工程学院，四川成都　　610031
  2.西南交通大学唐山研究生院，河北唐山 063000
  3.中车青岛四方机车车辆股份有限公司，山东青岛 266111
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2022.02.015
  CLC： U231;U270.331⁺.2
- Published：10 March 2022，
  
  Received：10 December 2021，
  
  Revised：10 February 2022，
- 稿件说明：
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ZHANG Dongxing, YANG Gang, ZHOU Ao, et al. Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD. [J]. Electric drive for locomotives (2):105-112(2022)
DOI：

ZHANG Dongxing, YANG Gang, ZHOU Ao, et al. Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD. [J]. Electric drive for locomotives (2):105-112(2022) DOI： 10.13890/j.issn.1000-128X.2022.02.015.

摘要

针对城轨列车运行过程中轴箱轴承故障难以发现的问题，提出一种利用蝴蝶优化算法(Butterfly Optimization Algorithm

BOA)对变分模态分解(Variational Mode Decomposition

VMD）参数进行优化的轴承故障特征提取方法。首先构建基于轴承-车辆刚柔耦合的轴承故障动力学模型，提取轮轨激扰和轴承故障情况下的轴箱振动信号；然后利用蝴蝶优化算法对轴箱振动信号的VMD模态分量数和二次惩罚系数进行寻优，确定最佳参数组合；最后利用已确定的最佳参数对轴承振动信号进行VMD分解，得到不同本征模态分量(Intrinsic Mode Function

IMF)，并对最佳模态分量信号进行包络分析，识别到轴承故障时的特征频率。试验分析表明，基于优化参数的VMD分析方法能够有效提取轴承故障特征频率，通过经验模态分解(Empirical Mode Decomposition

EMD)分析方法对比，可以发现文章提出的分析方法效果更加有效。

Abstract

Aiming at the problem that axle-box bearing faults are difficult to find during the operation of urban rail trains

a bearing fault feature extraction based on variational mode decomposition (VMD) parameter optimization using butterfly optimization algorithm (BOA) was proposed. Firstly

a bearing fault dynamic model based on the rigid-flexible coupling of bearing-vehicle was constructed

and the vibration signal of the axle box under the wheel-rail disturbance and the faulty bearing was extracted. Then

the BOA algorithm is used to optimize the VMD modal component number and the second penalty coefficient of the axle box vibration signal

so as to determine the best parameter combination. Finally

by using the determined optimal parameters

the vibration signal of the bearing was decomposed by VMD to obtain different intrinsic mode components (intrinsic mode function

IMF)

and an envelope analysis was performed to find the eigen frequencies of bearing failures. Through the experimental analysis

it can be seen that the VMD analysis method of optimizing parameters can effectively find the characteristic frequency of bearing faults

and by comparing the EMD analysis method

it can be found that the analysis method proposed in this paper is more effective.

关键词

列车轴承故障特征提取变分模态分解蝴蝶优化算法故障诊断仿真

Keywords

train bearingfault feature extractionvariational mode decompositionBOAfault diagnosissimulation

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