FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN

LI SiQi; JIANG ZhiJian

doi:10.16579/j.issn.1001.9669.2020.05.003

您当前的位置：

首页 >

文章列表页 >

FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN

更新时间：2022-09-22

- FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN
- Journal of Mechanical Strength Vol. 42, Issue 5, Pages: 1033-1038(2020)
- 作者机构：
  
  1. 北京建筑大学电信学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2020.05.003
  CLC：
扫描看全文
LI SiQi, JIANG ZhiJian. FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN. [J]. 42(5):1033-1038(2020)
DOI：

LI SiQi, JIANG ZhiJian. FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN. [J]. 42(5):1033-1038(2020) DOI： 10.16579/j.issn.1001.9669.2020.05.003.

摘要

为了提高滚动轴承故障诊断的准确性,提出了基于集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)与卷积神经网络(Convolution Neural Networks,CNN)的故障诊断方法。首先使用EEMD对信号进行分解,并根据相关系数以及峭度值选取适当的本征模态函数(Intrinsic Mode Function,IMF)进行信号重构。对重构信号进行一系列指标计算后使用卷积神经网络以及多种方法进行故障诊断。结果表明,所使用的方法能够有效地进行故障诊断,准确率可达96.7%,在实际中有着一定的应用意义。

Abstract

In order to improve the rolling bearing fault diagnosis accuracy,this paper presents a fault diagnosis method based on Ensemble Empirical Mode Decomposition（ EEMD） and Convolution Neural Networks（ CNN）. At first,using the EEMD decompose the signal. After that,choose appropriate IMFs according to the correlation coefficent and kurtosis calculating results to reconstruct the signal. After calculating a series of indexes of reconstructed signals,using CNN and various methods to diagnose faults. The results shows that the method used in this paper can effectively carry out fault diagnosis. The accuracy can reach 96. 7%. It has certain application significance to fault diagnosis.

关键词

集合经验模态分解卷积神经网络故障诊断机器学习

Keywords

Ensemble empirical mode decompositionConvolution neural networkFault diagnosisMachine learning

references

Views

979

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

APPLICATION OF GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS

APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT)

A SINGULAR INTERVAL RECONSTRUCTION LOCAL MEAN DECOMPOSITION AND ITS APPLICATION IN BEARING FAULT DIAGNOSIS OF RECIPROCATING COMPRESSOR (MT)

FAULT DIAGNOSIS OF GEARBOX BASED ON ADAPTIVE EEMD AND FAST KURTOGRAM

RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK

Related Author

No data

Related Institution

School of Energy and Power Engineering, University of Shanghai for Science and Technology

Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering

School of Mechanical Engineering, Shenyang University of Technology

BMW Brilliance Automotive Co., Ltd.

School of Mechanical Science and Engineering, Northeast Petroleum University

⁰