[1]朱 丹,苏燕辰,燕春光.基于 SVD-MOMEDA 的高速列车齿轮箱轴承故障诊断[J].机车电传动,2020,(02):144-148.[doi:10.13890/j.issn.1000-128x.2020.02.126]
 ZHU Dan,SU Yanchen,YAN Chunguang.Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA[J].Electric Drive for Locomotives,2020,(02):144-148.[doi:10.13890/j.issn.1000-128x.2020.02.126]
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基于 SVD-MOMEDA 的高速列车齿轮箱轴承故障诊断()
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机车电传动[ISSN:1000-128X/CN:43-1125/U]

卷:
期数:
2020年02期
页码:
144-148
栏目:
试验检测
出版日期:
2020-03-10

文章信息/Info

Title:
Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA
文章编号:
1000-128X(2020)02-0144-05
作者:
朱 丹 1苏燕辰 1燕春光 2
(1. 西南交通大学 机械工程学院,四川 成都 610031;2. 中车唐山机车车辆有限公司,河北 唐山 063035)
Author(s):
ZHU Dan 1 SU Yanchen 1 YAN Chunguang 2
( 1.School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China; 2. CRRC Tangshan Co., Ltd., Tangshan, Hebei 063035, China )
关键词:
高速列车故障诊断多点优化最小熵解卷积修正奇异值分解滚动轴承齿轮箱
Keywords:
high-speed train fault diagnosis MOMEDA SVD rolling bearing gearbox
分类号:
U292.91+4;TH133.33
DOI:
10.13890/j.issn.1000-128x.2020.02.126
文献标志码:
A
摘要:
针对强背景噪声环境下高速列车齿轮箱轴承故障信号难以检测的问题以及多点优化最小熵解卷积修正 (multipoint optimal minimum entropy deconvolution adjusted,MOMEDA) 方法受滤波器阶数、故障周期影响的问题,提出了基于奇异值分解 (singular value decomposition, SVD) 改进的 MOMEDA 的轴承故障诊断方法。首先采用 SVD 作为 MOMEDA 的前置滤波器滤除部分噪声,然后通过 MOMEDA 多点峭度谱追踪故障
Abstract:
Aiming at problems of high-speed train gearbox bearing fault signals being difficult to detect under strong noise background, and the problem that the multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) method was affected by the order of filter and the period of impulse signal, an improved MOMEDA method for bearing fault diagnosis based on singular value decomposition(SVD) was proposed. Firstly, SVD was used as the pre-filter of MOMEDA to filter the partial noise. Then, the fault period component was traced by MOMEDA multipoint kurtosis spectrum, and the optimal order of MOMEDA filter was solved iteratively by variable step search method. Finally, by using the periodic impulse in the signal track with MOMEDA, and the fault features with envelope spectrum were extracted. The simulation signal and the fault test data showed that this method could accurately diagnose the fault of the gearbox bearing of high-speed train, and the fault diagnosis effect was better than the complementary empirical mode decomposition method.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介:朱? 丹(1995—),女,硕士研究生,研究方向为信号分析与故障诊断。
更新日期/Last Update: 2020-03-10