基于特征融合的牵引电机轴承声学故障诊断

杨岗; 卫昱乾; 李芾

doi:10.13890/j.issn.1000-128X.2023.02.012

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基于特征融合的牵引电机轴承声学故障诊断

电力牵引与控制 | 更新时间：2024-08-02

- 基于特征融合的牵引电机轴承声学故障诊断
- Acoustic fault diagnosis of traction motor bearing based on fusion feature
- 机车电传动 2023年第2期页码：103-112
- 作者机构：
  
  1.西南交通大学机械工程学院，四川成都 610031
  2.西南交通大学唐山研究院，河北唐山;063000
- 作者简介：
  
  杨　岗（1973—），男，博士，讲师，硕士生导师，主要研究方向为交通设备故障诊断与控制工程、人工智能与大数据分析、弓网动力学与主动控制；E-mail: yanggang@swjtu.cn
- 基金信息：
  
  国家重点研发计划项目(2020YFB1200300ZL);四川省科技计划项目(2022YFG0088)
- DOI：10.13890/j.issn.1000-128X.2023.02.012
  中图分类号： U292.91⁺4;U260.331⁺.2
- 纸质出版日期：2023-03-10，
  
  收稿日期：2023-02-13，
- 稿件说明：
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杨岗, 卫昱乾, 李芾. 基于特征融合的牵引电机轴承声学故障诊断[J]. 机车电传动, 2023(2): 103-112.

YANG Gang, WEI Yuqian, LI Fu. Acoustic fault diagnosis of traction motor bearing based on fusion feature[J]. Electric Drive for Locomotives,2023(2): 103-112.
杨岗, 卫昱乾, 李芾. 基于特征融合的牵引电机轴承声学故障诊断[J]. 机车电传动, 2023(2): 103-112. DOI： 10.13890/j.issn.1000-128X.2023.02.012.

YANG Gang, WEI Yuqian, LI Fu. Acoustic fault diagnosis of traction motor bearing based on fusion feature[J]. Electric Drive for Locomotives,2023(2): 103-112. DOI： 10.13890/j.issn.1000-128X.2023.02.012.

摘要

滚动轴承作为高速列车牵引电机的重要部件，其故障情况严重影响列车运行安全。声学轴承故障诊断方式具有无安装侵入性、运维成本低的优点，但也具有信噪比低、故障特征难以提取的缺点，机器学习则具有克服噪声影响的鲁棒性。针对应用机器学习进行声学故障诊断时，少量特征无法全面表征轴承故障的难题，文章提出将格拉姆角场（GAF）与小波时频图进行叠加融合，构成6通道融合特征图用以有效表征轴承的故障。首先，建立牵引电机轴承声学故障试验台获取故障声学信号；其次，建立基于GAF的声学信号融合特征图，然后使用残差网络（ResNET）模型针对融合特征图特征训练并验证故障分类模型，并与以单种特征图作为特征的故障分类方法进行准确率对比。结果表明，基于GAF的融合特征图的声学故障分类模型具有99.89%的准确率，融合特征图能更有效地映射轴承故障。

Abstract

The rolling bearing is an important part of the traction motor of high-speed train

and its failure seriously affects the safety of train operation. The acoustic diagnosis method for bearing fault has the advantages of non-invasive installation and low operation and maintenance cost

but it also has the disadvantages of low signal-to-noise ratio and difficult to extract fault features. Machine learning has the robustness to avoid the influence of noise. Aiming at the problem that a small number of features cannot fully characterize the bearing fault when applying machine learning to acoustic fault diagnosis

this paper proposes to superimpose and fuse the Gramian angular field (GAF) and wavelet time-frequency figure to form a six channel fusion feature map to effectively characterize the bearing fault. Firstly

the traction motor bearing acoustic fault test-bed was established to obtain the fault acoustic signal. Secondly

the acoustic signal fusion feature map based on GAF was established. Then

the residual networks (ResNETs) model was used to train and verify the fault classification model for the features of the fusion feature map

and the accuracy was compared with the fault classification method with a single feature map as the feature. The results show that the acoustic fault classification model based on GAF fusion feature map has an accuracy of 99.89%

so the fusion feature map can more effectively reflect the bearing fault.

关键词

牵引电机轴承声学故障诊断卷积神经网络融合特征图格拉姆角场高速列车

Keywords

traction motor bearingacoustic fault diagnosisconvolutional neural networkfusion feature mapGramian angular fieldhigh-speed train

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