ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN

CHEN DaiJun; CHEN LiLi; DONG ShaoJiang

doi:10.16579/j.issn.1001.9669.2023.06.002

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ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN

更新时间：2024-06-07

- ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN
- Journal of Mechanical Strength Vol. 45, Issue 6, Pages: 1280-1285(2023)
- 作者机构：
  
  重庆交通大学机电与车辆工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2023.06.002
  CLC： TH133.33
- Published：15 December 2023，
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CHEN DaiJun, CHEN LiLi, DONG ShaoJiang. ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN. [J]. Journal of Mechanical Strength 45(6):1280-1285(2023)
DOI：

CHEN DaiJun, CHEN LiLi, DONG ShaoJiang. ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN. [J]. Journal of Mechanical Strength 45(6):1280-1285(2023) DOI： 10.16579/j.issn.1001.9669.2023.06.002.

摘要

针对传统故障诊断方法需人工提取特征且识别率低的问题，提出了一种基于变分模态分解(Variational Modal Decomposition

VMD)和连续小波变换(Continuous Wavelet Transform

CWT)并结合卷积神经网络(Convolutional Neural Network

CNN)的VMD-CWT-CNN模型用于滚动轴承故障诊断。首先，通过VMD将轴承振动信号分解为多个具有不同中心频率的模态分量；其次，采用CWT将模态分量进行计算并转为二维时频图；最后，将时频图输入结构裁剪后的EfficientNet卷积神经网络自动提取特征并完成滚动轴承的故障诊断。使用提出的方法对美国凯斯西储大学10类轴承故障数据进行多次实验，平均准确率达到了99.86%

能有效地完成滚动轴承信号的特征提取以及损伤程度的精确诊断。

Abstract

Aiming at problems that traditional fault diagnosis methods need to extract features manually and the recognition rate is low

a VMD-CWT-CNN model based on variational modal decomposition (VMD) and continuous wavelet transform (CWT) combined with convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly

the bearing vibration signal is decomposed into multiple modal components with different center frequencies by VMD. Secondly

the modal components are calculated by CWT and transformed into two-dimensional time-frequency diagram. Finally

the time-frequency diagram is input into the EfficientNet convolution neural network after structure cutting

the features are automatically extracted

and the fault diagnosis of rolling bearing is completed. Using the method proposed

the average accuracy of multiple experiments on 10 types of bearing fault data from Case Western Reserve University is 99. 86%

which can effectively complete the feature extraction of rolling bearing signal and the accurate diagnosis of damage degree.

关键词

变分模态分解连续小波变换时频图卷积神经网络故障诊断

Keywords

VMDCWTTime-frequency diagramCNNFault diagnosis

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