机车故障诊断的局域均值分解解调方法

陈保家; 何正嘉; 陈雪峰; 訾艳阳

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机车故障诊断的局域均值分解解调方法

更新时间：2025-07-09

- 机车故障诊断的局域均值分解解调方法
- Locomotive Fault Diagnosis Based on Local Mean Decomposition Demodulating Approach
- 西安交通大学学报 2010年44卷第5期页码：40-44
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2009-10-26. 作者简介: 陈保家(1977-),男,博士生;何正嘉(联系人),男,教授,博士生导师. 基金项目: 国家重点基础研究发展规划资助项目(2005CB724100);国家科技重大专项资助项目(2009ZX04014-015)
- DOI：
  中图分类号： TH165
- 网络首发：2010-05-10，
  
  纸质出版：2010
- 稿件说明：
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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.

Locomotive Fault Diagnosis Based on Local Mean Decomposition Demodulating Approach[J]. 2010, 44(5): 40-44.
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. DOI：

Locomotive Fault Diagnosis Based on Local Mean Decomposition Demodulating Approach[J]. 2010, 44(5): 40-44. DOI：

摘要

为了有效地识别机车走行部的早期故障

提高我国重载机车的运输能力

提出了一种针对机车故障振动信号的局域均值分解(LMD)解调诊断方法.LMD能够将多分量的调制信号自适应地分解成一系列乘积函数分量

分解与解调过程可同步完成.与Hilbert-Huang变换相比

LMD方法不需要通过Hilbert变换求解瞬时频率

从而避免了Hilbert变换加窗效应所带来的解调误差.由于不受Bedrosian和Nuttall定理的限制

不会出现负频率现象

通过滑动平均方法得到信号的局域均值和包络

因此不存在过包络、欠包络和断点效应.通过对实际机车走行部轴承和齿轮振动信号的分析

成功地提取了故障特征

与经验模式分解进行比较的结果说明

采用LMD方法提取尽可能多的有意义的调制分量

不仅避免了Hilbert变换加窗效应所带来的解调误差

而且更适合于多分量调制信号的处理.

Abstract

The identification of the locomotive bogies incipient faults is of great meaning to increase the heavy freight locomotive transport capacity and prevent severe accidents. According to the vibration signals of locomotive

a fault diagnosis method based on local mean decomposition(LMD)demodulating approach is proposed

which decomposes the signals adaptively into a set of product functions. Decomposition and demodulation are implemented together during the process. Compared with Hilbert Huang transform

LMD method calculates instantaneous frequency bypassing the Hilbert transform and involves no demodulation error of windowing effect. Breaking down the limitations of Bedrosian theorem and Nuttall theorem

the negative frequency does not exist. For the reason that the local mean and envelope are obtained by using sliding averaging

there are no phenomena about over enveloping

under enveloping and breakpoint effect. The method has been successfully applied in fault diagnosis to rolling bearing and gear of locomotive bogies. Compared with the results of EMD

it shows that LMD decomposes signals into demodulation components as much as possible and gets very suitable for processing multi-components vibration signals.

关键词

Keywords

references

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滚动轴承故障诊断的多小波谱峭度方法.西安交通大学学报,2010,44(3): 77-81.

一种自适应局部线性嵌入与谱聚类融合的故障诊断方法.西安交通大学学报,2010,44(1):77-82.

粒计算及其在机械故障智能诊断中的应用.西安交通大学学报,2009,43(9):37-41.

具有模糊隶属度的模糊支持向量机算法.西安交通大学学报,2009,43(7): 40-43.

自适应移频变尺度随机共振在故障诊断中的应用.西安交通大学学报,2009,43(7):69-73.

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