An Approach of Intelligent Compound Fault Diagnosis of Rolling Bearing based on MWT and CNN

Han Tao; Yuan Jianhu; Tang Jian; An Lizhou

doi:10.16578/j.issn.1004.2539.2016.12.031

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An Approach of Intelligent Compound Fault Diagnosis of Rolling Bearing based on MWT and CNN

更新时间：2022-10-20

- An Approach of Intelligent Compound Fault Diagnosis of Rolling Bearing based on MWT and CNN
- Vol. 40, Issue 12, Pages: 139-143(2016)
- 作者机构：
  
  1. 解放军理工大学野战工程学院
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2016.12.031
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[1]韩涛,袁建虎,唐建,安立周.基于MWT和CNN的滚动轴承智能复合故障诊断方法[J].机械传动,2016,40(12):139-143.

Han Tao, Yuan Jianhu, Tang Jian, et al. An Approach of Intelligent Compound Fault Diagnosis of Rolling Bearing based on MWT and CNN. [J]. 40(12):139-143(2016)
[1]韩涛,袁建虎,唐建,安立周.基于MWT和CNN的滚动轴承智能复合故障诊断方法[J].机械传动,2016,40(12):139-143. DOI： 10.16578/j.issn.1004.2539.2016.12.031.

Han Tao, Yuan Jianhu, Tang Jian, et al. An Approach of Intelligent Compound Fault Diagnosis of Rolling Bearing based on MWT and CNN. [J]. 40(12):139-143(2016) DOI： 10.16578/j.issn.1004.2539.2016.12.031.

摘要

提出一种基于多小波变换（MWT）和卷积神经网络（CNN）的滚动轴承智能复合故障诊断方法。对滚动轴承的振动信号进行去除后处理的MWT,得到相应的多小波系数分支;用所得多小波系数分支构造特征图,建立CNN分类器组模型,以实现滚动轴承复合故障的智能诊断。基于人工轴承故障数据集进行了实验研究,同时对诊断方法作了优化改进,即对振动信号进行MWT,用所得多小波系数矩阵构造特征图,建立CNN分类器模型,并进行了对比实验研究。结果表明,该方法能有效识别滚动轴承的复合故障,改进的方法能有效提高故障识别率,降低训练成本。

Abstract

An approach to intelligent compound fault diagnosis of rolling bearing using multi- wavelet transform（ MWT） and convolution neural network（ CNN） was proposed. According to this approach,the vibration signals of rolling bearing are analyzed by using MWT of removed post processing,and the corresponding multi- wavelet coefficient branches are obtained. Then,all the multi- wavelet coefficient branches are used to form feature maps,and a multiple CNN classifiers is developed to identify the compound fault of rolling bearing.The tests for the proposed method are accomplished based on artificial bearing fault data sets,and the method is optimized,the vibration signals are analyzed by using MWT,the obtained multi- wavelet coefficient matrix is used to form feature map,and CNN is developed to make a comparative experimental study. The experimental results indicates that this method could effectively identify the compound fault of rolling bearing,and the improved method could effectively improve the fault recognition rate and reduce the training cost.

关键词

滚动轴承智能复合故障诊断多小波变换卷积神经网络

Keywords

Rolling bearingIntelligent compound fault diagnosisMulti-wavelet transform（MWT）Convolution neural network（CNN）

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