基于VMD-TEO窗和DBiLSTM的早期轴承故障诊断

乔美英; 闫书豪; 兰建义; 王波; 汤夏夏; 杨金显

doi:10.13471/j.cnki.acta.snus.2019.08.20.2019B081

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基于VMD-TEO窗和DBiLSTM的早期轴承故障诊断

研究论文 | 更新时间：2023-11-01

- 基于VMD-TEO窗和DBiLSTM的早期轴承故障诊断
- Early bearing fault diagnosis based on VMD-TEO window function and DBiLSTM
- 中山大学学报(自然科学版) 2020年59卷第5期页码：66-77
- 作者机构：
  
  1.河南理工大学电气工程与自动化学院，河南焦作 454000
  2.煤炭安全生产河南省协同创新中心，河南焦作 454000
  3.河南理工大学能源科学与工程学院，河南焦作 454000
- 作者简介：
  
  乔美英(1976年生)，女；研究方向：主要从事机器学习，故障诊断等；E-mail: qiaomy@hpu.edu.cn
- 基金信息：
  
  国家自然科学基金(41672363;61573129);河南省科技攻关项目(172102310239;172102210289)
- DOI：10.13471/j.cnki.acta.snus.2019.08.20.2019B081
  中图分类号： TH165.3
- 纸质出版日期：2020-09-25，
  
  收稿日期：2019-08-20，
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乔美英,闫书豪,兰建义等.基于VMD-TEO窗和DBiLSTM的早期轴承故障诊断[J].中山大学学报(自然科学版),2020,59(05):66-77.

QIAO Meiying,YAN Shuhao,LAN Jianyi,et al.Early bearing fault diagnosis based on VMD-TEO window function and DBiLSTM[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2020,59(05):66-77.
乔美英,闫书豪,兰建义等.基于VMD-TEO窗和DBiLSTM的早期轴承故障诊断[J].中山大学学报(自然科学版),2020,59(05):66-77. DOI： 10.13471/j.cnki.acta.snus.2019.08.20.2019B081.

QIAO Meiying,YAN Shuhao,LAN Jianyi,et al.Early bearing fault diagnosis based on VMD-TEO window function and DBiLSTM[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2020,59(05):66-77. DOI： 10.13471/j.cnki.acta.snus.2019.08.20.2019B081.

摘要

针对现代滚动轴承早期故障监测因数据量增大所带来的诊断困难问题，提出了基于变分模态分解（VMD）与TEO窗特征提取的深层双向长短记忆神经网络（DBiLSTM）轴承故障诊断方法。首先，利用改进果蝇算法优化的VMD-TEO窗函数，提取轴承振动信号的瞬时能量特征，构造具有时序特性的特征矩阵；其次，利用训练集对DBiLSTM模型进行学习以确定模型参数；最后，用测试集对模型进行验证，输出轴承状态识别结果。试验采用凯西西楚大学轴承故障数据集，结果表明：该方法在处理数据量较大的滚动轴承故障诊断问题时，能有效地对多种故障类型，不同损伤等级的滚动轴承振动信号进行识别。

Abstract

Aiming at the problems caused by the data increase in the early fault monitoring of modern rolling bearings

a fault diagnosis model for deep bidirectional long and short memory neural network (DBiLSTM) based on variational mode decomposition (VMD) and TEO energy window was put forward. Firstly

the instantaneous energy characteristics of bearing vibration signals were extracted by the VMD-TEO window function that optimized by the improved fruit fly algorithm. Meanwhile

the characteristic matrix with time characteristic was constructed. Secondly

DBiLSTM network model was trained by using the training set to determine the model parameters. Finally

the trained model was applied in the test set to generate fault recognition results. The test used Case Western Reserve University bearing fault data set

and the results show that this method can effectively identify vibration signals of rolling bearings with various fault types and different damage levels when dealing with large amounts of data problems.

关键词

变分模态分解TEO能量窗函数深层双向长短记忆神经网络（DBiLSTM）轴承故障诊断

Keywords

variational modal decompositionTEO energy window functionDBiLSTMbearing failure diagnosis

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