Application of optimized VMD in fault diagnosis of motor bearings of high-speed train

WANG Tao; SHI Yongjin; LI Jiwei; WANG Jinping

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

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Application of optimized VMD in fault diagnosis of motor bearings of high-speed train

Detection Technology | 更新时间：2024-08-02

- Application of optimized VMD in fault diagnosis of motor bearings of high-speed train
- Electric drive for locomotives Issue 4, Pages: 180-186(2022)
- 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：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2022.04.026
  CLC： U292.91⁺4;U260.331⁺.2
- Published：10 July 2022，
  
  Received：17 December 2021，
  
  Revised：02 January 2022，
- 稿件说明：
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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 (4):180-186(2022)
DOI：

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 (4):180-186(2022) 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

references

朱文龙, 杨家伟, 关照议, 等. 牵引电机轴承故障诊断技术综述[J]. 控制与信息技术, 2021(5): 12-19.

ZHU Wenlong, YANG Jiawei, GUAN Zhaoyi, et al. Overview of traction motor bearing fault diagnosis technology[J]. Control and Information Technology, 2021(5): 12-19.

崔玲丽, 王婧, 邬娜, 等. 基于轴承故障信号特征的自适应冲击字典匹配追踪方法及应用[J]. 振动与冲击, 2014, 33(11): 54-60.

CUI Lingli, WANG Jing, WU Na, et al. Bearing fault diagnosis based on self-adaptive impulse dictionary matching pursuit[J]. Journal of Vibration and Shock, 2014, 33(11): 54-60.

雷亚国. 基于改进Hilbert-Huang变换的机械故障诊断[J]. 机械工程学报, 2011, 47(5): 71-77.

LEI Yaguo. Machinery fault diagnosis based on improved Hilbert-Huang transform[J]. Journal of Mechanical Engineering, 2011, 47(5): 71-77.

DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.

詹君, 程龙生, 彭宅铭. 基于VMD和改进多分类马田系统的滚动轴承故障智能诊断[J]. 振动与冲击, 2020, 39(2): 32-39.

ZHAN Jun, CHENG Longsheng, PENG Zhaiming. Intelligent fault diagnosis of rolling bearings based on the VMD and improved multi-classification Mahalanobis Taguchi system[J]. Journal of Vibration and Shock, 2020, 39(2): 32-39.

边杰. 基于遗传算法参数优化的变分模态分解结合1.5维谱的轴承故障诊断[J]. 推进技术, 2017, 38(7): 1618-1624.

BIAN Jie. Fault diagnosis of bearing combining parameter optimized variational mode decomposition based on genetic algorithm with 1.5-dimensional spectrum[J]. Journal of Propulsion Technology, 2017, 38(7): 1618-1624.

周福成, 唐贵基, 何玉灵. 基于改进VMD的风电齿轮箱不平衡故障特征提取[J]. 振动与冲击, 2020, 39(5): 170-176.

ZHOU Fucheng, TANG Guiji, HE Yuling. Unbalanced fault feature extraction for wind power gearbox based on improved VMD[J]. Journal of Vibration and Shock, 2020, 39(5): 170-176.

唐贵基, 王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报, 2015, 49(5): 73-81.

TANG Guiji, WANG Xiaolong. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing[J]. Journal of Xi'an Jiaotong University, 2015, 49(5): 73-81.

郑小霞, 陈广宁, 任浩翰, 等. 基于改进VMD和深度置信网络的风机易损部件故障预警[J]. 振动与冲击, 2019, 38(8): 153-160.

ZHENG Xiaoxia, CHEN Guangning, REN Haohan, et al. Fault detection of vulnerable units of wind turbine based on improved VMD and DBN[J]. Journal of Vibration and Shock, 2019, 38(8): 153-160.

马洪斌, 佟庆彬, 张亚男. 优化参数的变分模态分解在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2018, 29(4): 390-397.

MA Hongbin, TONG Qingbin, ZHANG Yanan. Applications of optimization parameters VMD to fault diagnosis of rolling bearings[J]. China Mechanical Engineering, 2018, 29(4): 390-397.

张超, 朱腾飞, 王大勇. 基于VMD能量熵与支持向量机的齿轮故障诊断[J]. 机械设计与研究, 2018, 34(2): 81-84.

ZHANG Chao, ZHU Tengfei, WANG Dayong. Fault diagnosis method of gear based on VMD energy entropy and LS-SVM[J]. Machine Design & Research, 2018, 34(2): 81-84.

李长青, 林建辉, 胡永旭. 优化参数VMD和MED在列车齿轮箱滚动轴承故障诊断中的应用[J]. 机车电传动, 2020(3): 142-147.

LI Changqing, LIN Jianhui, HU Yongxu. Application of optimization parameters VMD and MED in fault diagnosis of train gearbox rolling bearings[J]. Electric Drive for Locomotives, 2020(3): 142-147.

李华, 伍星, 刘韬, 等. 基于信息熵优化变分模态分解的滚动轴承故障特征提取[J]. 振动与冲击, 2018, 37(23): 219-225.

LI Hua, WU Xing, LIU Tao, et al. Bearing fault feature extraction based on VMD optimized with information entropy[J]. Journal of Vibration and Shock, 2018, 37(23): 219-225.

王昌明, 张征, 李峰, 等. 改进经验小波变换的齿轮箱故障诊断新方法与应用[J]. 噪声与振动控制, 2018, 38(5): 167-172.

WANG Changming, ZHANG Zheng, LI Feng, et al. A new method and its application for fault diagnosis of gearboxes based on improved empirical wavelet transform[J]. Noise and Vibration Control, 2018, 38(5): 167-172.

HUANG Y, LIN J H, LIU Z C, et al. A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis[J]. Journal of Sound and Vibration, 2019, 444: 216-234.

LI J M, YAO X F, WANG H, et al. Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2019, 126: 568-589.

刘泽潮, 林建辉, 丁建明, 等. 变步长频率加权能量算子在轴承故障诊断中的应用[J]. 振动与冲击, 2019, 38(7): 86-92.

LIU Zechao, LIN Jianhui, DING Jianming, et al. Application of variable step frequency weighted energy operator in bearing fault diagnosis[J]. Journal of Vibration and Shock, 2019, 38(7): 86-92.

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