优化VMD在高速列车牵引电机轴承故障诊断中的应用

王涛; 石永进; 李继伟; 王金平

doi:10.13890/j.issn.1000-128X.2022.04.026

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优化VMD在高速列车牵引电机轴承故障诊断中的应用

检测技术 | 更新时间：2024-08-02

- 优化VMD在高速列车牵引电机轴承故障诊断中的应用
- Application of optimized VMD in fault diagnosis of motor bearings of high-speed train
- 机车电传动 2022年第4期页码：180-186
- 作者机构：
  
  1.中车永济电机有限公司，山西　永济　　044502
  2.轨道交通牵引电机山西省重点实验室，山西永济;044502
- 作者简介：
  
  王　涛（1995—），男，硕士，主要研究方向为轴承振动分析和失效形式研究；E-mail: 575646156@qq.com
- 基金信息：
  
  国家重点研发计划项目(2020YFB2007900);中国中车重点项目(YJXM2020-049)
- DOI：10.13890/j.issn.1000-128X.2022.04.026
  中图分类号： U292.91⁺4;U260.331⁺.2
- 纸质出版日期：2022-07-10，
  
  收稿日期：2021-12-17，
  
  修回日期：2022-01-02，
- 稿件说明：
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王涛, 石永进, 李继伟, 等. 优化VMD在高速列车牵引电机轴承故障诊断中的应用[J]. 机车电传动, 2022,(4):180-186.

WANG Tao, SHI Yongjin, LI Jiwei, et al. Application of optimized VMD in fault diagnosis of motor bearings of high-speed train[J]. Electric drive for locomotives, 2022,(4):180-186.
王涛, 石永进, 李继伟, 等. 优化VMD在高速列车牵引电机轴承故障诊断中的应用[J]. 机车电传动, 2022,(4):180-186. DOI： 10.13890/j.issn.1000-128X.2022.04.026.

WANG Tao, SHI Yongjin, LI Jiwei, et al. Application of optimized VMD in fault diagnosis of motor bearings of high-speed train[J]. Electric drive for locomotives, 2022,(4):180-186. DOI： 10.13890/j.issn.1000-128X.2022.04.026.

摘要

针对强噪声背景下高速列车电机轴承早期故障信息提取困难的问题，提出了优化VMD算法。该算法依据频谱趋势估计算法获取振动信号频谱趋势，从而确定共振频带和分解层数；根据各频带边界和带宽，依据取值公式获取惩罚因子初始矩阵，最后将已知参数代入到VMD算法中实现对信号的分解。通过仿真信号和试验信号验证表明，该方法能在低信噪比条件下较为准确地识别到共振频带，并对信号进行准确的分解，提高了VMD算法的准确性和自适应性。

Abstract

Aiming at the difficulty in extracting early faults of high-speed train motor bearings under the background of strong noise

an optimized VMD algorithm was proposed. Firstly

the vibration signal spectrum trend was obtained according to the spectrum trend estimation algorithm

thereby the resonance frequency band and the number of decomposition layers were determined; then

according to the boundary and bandwidth of each frequency band

the initial matrix of the penalty factor was obtained according to the value formula; finally the known parameters were substituted into the VMD algorithm to realize the decomposition of the signals. Verification by simulation signals and experimental signals shows that the method can more accurately identify the resonance frequency band under the condition of low signal-to-noise ratio

and accurately decompose the signal

which improves the accuracy and adaptability of the VMD algorithm.

关键词

高速列车电机轴承变分模态分解频谱趋势故障诊断

Keywords

high-speed trainmotor bearingvariational modal decompositionfrequency spectrum trendfault diagnosis

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