Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA

Dan ZHU; Yanchen SU; Chunguang YAN

doi:10.13890/j.issn.1000-128x.2020.02.126

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Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA

Test & Detection | 更新时间：2021-12-10

- Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA
- Electric Drive for Locomotives Issue 2, Pages: 144-148,152(2020)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128x.2020.02.126
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Dan ZHU, Yanchen SU, Chunguang YAN. Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA. [J]. Electric Drive for Locomotives (2):144-148,152(2020)
DOI：

Dan ZHU, Yanchen SU, Chunguang YAN. Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA. [J]. Electric Drive for Locomotives (2):144-148,152(2020) DOI： 10.13890/j.issn.1000-128x.2020.02.126.

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.

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 trainfault diagnosisMOMEDASVDrolling bearinggearbox

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