[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):1.
 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):1.
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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:
1
栏目:
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
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:
 
分类号:
U292.91+4;TH133.33
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周期成分,采用变步长搜索法迭代求解MOMEDA 滤波器最优阶数,最后利用最优参数相对应的MOMEDA 增强信号中的周期性脉冲,并通过包络谱提取故障特征。仿真信号和试验数据分析表明:该方法能实现高速列车齿轮箱轴承故障的精确诊断,且故障诊断效果优于互补经验模态分解方法。
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.

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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