[1]魏勇召,赵明元,杨江天.信号自适应分解对比研究及其在机车轴承故障诊断中的应用[J].机车电传动,2017,(04):91.[doi:10.13890/j.issn.1000-128x.2017.04.023]
 WEI Yongzhao,ZHAO Mingyuan,YANG Jiangtian.Comparative Study of Adaptive Signal Decomposition Methods and Their Applications in Locomotive Rolling Element Bearing Fault Diagnosis[J].Electric Drive for Locomotives,2017,(04):91.[doi:10.13890/j.issn.1000-128x.2017.04.023]
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信号自适应分解对比研究及其在机车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:
2017年04期
页码:
91
栏目:
试验检测
出版日期:
2017-07-10

文章信息/Info

Title:
Comparative Study of Adaptive Signal Decomposition Methods and Their Applications in Locomotive Rolling Element Bearing Fault Diagnosis
文章编号:
1000-128X(2017)04-0091-04
作者:
魏勇召1赵明元2杨江天1
(1. 北京交通大学机械与电子控制工程学院,北京 100044; 2. 中车大同电力机车有限责任公司,山西大同 037038)
Author(s):
WEI Yongzhao1 ZHAO Mingyuan2 YANG Jiangtian1
( 1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China; 2. CRRC Datong Electric Locomotive Co., Ltd., Datong, Shanxi 037038, China )
关键词:
信号自适应分解局域均值分解轴承故障诊断DF4 型机车
Keywords:
adaptive signal decomposition local mean decomposition bearing fault diagnose DF4 locomotive
分类号:
U269.32+2;U260.331+.2
DOI:
10.13890/j.issn.1000-128x.2017.04.023
文献标志码:
A
摘要:
为有效提取机车轴承故障特征,开展信号自适应分解方法对比研究。分析了经验模态分解、局域均值分解和局部特征尺度分解3 种常用方法的局部均值计算、分解成分和分解能力。针对局域均值分解存在的问题,提出了改进方案并有效验证。进一步提出了先做改进局域均值分解,再采用维谱处理得到的乘积分量的机车轴承诊断的方法,成功用于DF4 型机车的故障诊断。
Abstract:
In order to extract the fault features effectively, a comparative study of adaptive signal decomposition methods was presented. The local mean computation, decomposed components and decomposition capacity of three conventional methods empirical mode decomposition, local mean decomposition and local characteristic-scale decomposition were analyzed and compared. Aiming at the problems of the local mean decomposition, an improved decomposition algorithm was proposed. The effectiveness of proposed improvements was testified by computer-generated signal. Furthermore, a fault diagnostic approach by jointly using improved LMD and -dimension spectrum was developed. The effectiveness and practicability of the proposed method was verified by DF4 locomotive running tests.

参考文献/References:

[1]李辉,刘义伦,唐德尧. 基于冲击特征的轴承保持架变形故障识别方法研究[J]. 机车电传动,2015(6):95-97.
[2]曾承志,姚兴佳,唐德尧,等. 多物理量融合的轴承齿轮故障诊断方法研究[J]. 机车电传动,2015(2):85-89.
[3]Wang Yanxue,He Zhengjia,Zi Yanyang. A Comparative Study on the Local Mean Decomposition and Empirical Mode Decomposition and Their Applications to Rotating Machinery Health Diagnosis[J]. Journal of Vibration and Acoustics, 2010,132(2):02101001-02101010.
[4]Huang N E ,Shen Z,Long S R,et al. The empirical mode decomposition and the Hibert spectrum for nonlinear and nonstationary time series analysis[J]. Proceedings of the Royal Society A: Mathematical,Physical and Engineering Sciences,1998, 454:903-995. [5]陈保家,何正嘉,陈雪峰,等. 机车故障诊断的局域均值分解解调方法[J]. 西安交通大学学报,2010,44(5):40-44.
[6]Smith Jonathan S. The local mean decomposition and its application to EEG perception data[J]. Journal of The Royal Society Interface,2005,2(5):443-454.
[7]程军圣,郑近德,杨宇. 基于局部特征尺度分解的经验包络解调方法及其在机械故障诊断中的应用[J]. 机械工程学报, 2012,48(19):87-94.
[8]程军圣,张亢,杨宇,等. 局部均值分解与经验模式分解的对比研究[J]. 振动与冲击,2009,28(5):13-16.
[9]王明达,张来斌,梁伟,等. 基于B 样条插值的局部均值分解方法研究[J]. 振动与冲击,2010,29(11):73-78.
[10]陈欣安. 信号自适应分解及其在轨道车辆故障诊断中的应用[D]. 北京:北京交通大学,2015.
[11]贾依娇. 局部均值分解在机车轴承故障诊断中的应用研究[D]. 北京:北京交通大学,2014.
[12]任达千,杨世锡,吴昭同,等. LMD 时频分析方法的端点效应在旋转机械故障诊断中的影响[J]. 中国机械工程,2012, 23(8):951-956.
[13]杨江天,周培钰. 经验模态分解和Laplace 小波在机车柴油机齿轮系故障诊断中的应用[J]. 机械工程学报,2011,47(7): 109-115.
[14]樊养余,陶宝祺,熊克,等. 舰船噪声的 1(1/2) 维谱特征提取[J]. 声学学报,2002,27(1):71-76.

备注/Memo

备注/Memo:
作者简介:魏勇召(1992-),男,硕士研究生,研究方向为机械故障诊断。
更新日期/Last Update: 2017-07-10