A new fault diagnosis method for centrifugal fans based on ensemble empirical mode decomposition sample entropy and LIBSVM

ZHOU Yunlong; ZHANG Gang

English

您当前的位置：

首页 >

文章列表页 >

A new fault diagnosis method for centrifugal fans based on ensemble empirical mode decomposition sample entropy and LIBSVM

Thermal Power Generation Issue 1, (2017)

更新时间：2025-09-28

- A new fault diagnosis method for centrifugal fans based on ensemble empirical mode decomposition sample entropy and LIBSVM
- 作者机构：
  
  东北电力大学能源与动力工程学院,吉林,吉林,132012
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH165.3
- Published Online：24 January 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
ZHOU Yunlong, ZHANG Gang. A new fault diagnosis method for centrifugal fans based on ensemble empirical mode decomposition sample entropy and LIBSVM[J]. Thermal Power Generation, 2017, (1).
DOI：

ZHOU Yunlong, ZHANG Gang. A new fault diagnosis method for centrifugal fans based on ensemble empirical mode decomposition sample entropy and LIBSVM[J]. Thermal Power Generation, 2017, (1). DOI：

摘要

针对离心风机故障信号非平稳、非线性的特征以及应用过程样本少等特点

提出基于集合经验模态分解(EEMD)样本熵和LIBSVM的离心风机故障诊断的新方法。首先采用总体平均经验模态将传感器信号分解成若干个平稳的本征模态函数(IMF)分量；其次通过相关系数准则对IMF分量进行选择去除虚假模式分量

对于筛选出的IMF分量分别计算样本熵并将其作为特征向量；最后将特征向量输入LIBSVM和BP神经网络进行模式识别。实验验证了该方法的可行性、有效性和优越性。

Abstract

关键词

Keywords

references

Views

279

下载量

CSCD

Alert me when the article has been cited

提交

Publicity Resources

A rotor fault diagnosis method based on EEMD cloud model and SVM

Ultra-short-term wind power prediction based on modal decomposition and combined neural network

Prediction model for flue gas wet desulfurization efficiency based on partial least squares support vector machine

Photovoltaic power prediction based on fuzzy support vector machine

GA-SVM-based soft-sensor model for oxygen content in flue gas of utility boilers

Related Author

TIAN Songfeng

XU Jiarui

WANG Meijun

HAN Qiang

张经龙

况逸

高正中

TIE Zhixin

Related Institution

Key Lab of Condition Monitoring and Control for Power Plant Equipment, North China Electric Power University

Focused Photonics (Hangzhou) Inc.

School of Information Science and Technology, Zhejiang Sci-Tech University

Guangxi Key Laboratory of New Energy and Building Energy Saving,Guilin University of Technology

Department of Automation, North China Electric Power University

AI问答

⁰