[1]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,(03):142-147.[doi:10.13890/j.issn.1000-128x.2020.03.030]
 LI Changqing,LIN Jianhui,HU Yongxu.Application of Optimization Parameters VMD and MED in FaultDiagnosis of Train Gearbox Rolling Bearings[J].Electric Drive for Locomotives,2020,(03):142-147.[doi:10.13890/j.issn.1000-128x.2020.03.030]
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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()
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机车电传动[ISSN:1000-128X/CN:43-1125/U]

卷:
期数:
2020年03期
页码:
142-147
栏目:
试验检测
出版日期:
2020-05-10

文章信息/Info

Title:
Application of Optimization Parameters VMD and MED in FaultDiagnosis of Train Gearbox Rolling Bearings
文章编号:
1000-128X(2020)03-0142-06
作者:
李长青林建辉胡永旭
(西南交通大学牵引动力国家重点试验室,四川 成都 610031)
Author(s):
LI Changqing LIN Jianhui HU Yongxu
( State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, Sichuan 610031, China )
关键词:
高速列车列车齿轮箱滚动轴承最小熵解卷积变分模态分解参数优化离散差分进化算法故障诊断
Keywords:
high-speed train train gearbox rolling bearing minimum entropy deconvolution variational mode decomposition parameter optimize discrete differential evolution algorithm fault diagnosis
分类号:
U292.91+4;U260.332
DOI:
10.13890/j.issn.1000-128x.2020.03.030
文献标志码:
A
摘要:
针对强噪声情况下列车齿轮箱滚动轴承早期故障特征提取困难的问题,提出基于最小熵解卷积(minimum entropy deconvolution,MED)与参数优化变分模态分解(variational mode decomposition, VMD)相结合的故障诊断方法。首先利用MED对轴承振动信号进行降噪;其次,采用离散差分进化算法(discrete differential evolution algorithm, DDE)对VMD的参数进行优化搜索,并利用优化参数的变分模态分解算法对降噪后的故障信号进
Abstract:
Aiming at the problem of feature extraction of train gearbox rolling bearing’s incipient fault in the case of strong noise, a method of fault diagnosis based on minimum entropy deconvolution (MED) and parameter optimized variational mode decomposition (VMD) was proposed. Firstly, the bearing vibration signal was denoised by using MED. Then, the VMD parameters were optimized by discrete differential evolution algorithm(DDE), and the denoising signal was processed by VMD using the optimum parameters obtained by searching, a series of intrinsic mode functions were obtained. Finally, the optimal intrinsic mode function(IMF) was selected for envelopment analysis and getting the fault frequency. The experimental results showed that the proposed method could effectively extract the fault features of train gearbox rolling bearing and could be used to rolling bearing faulf diagnosis

参考文献/References:

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备注/Memo

备注/Memo:
作者简介:李长青(1993—),男,硕士研究生,主要研究方向为机械故障。
更新日期/Last Update: 2020-05-10