[1]罗方芳,陶求华.基于级联极限学习机的基站空调在线监测系统[J].集美大学学报(自然版),2018,23(6):475-480.
 LUO Fangfang,TAO Qiuhua.Air Conditioning Online Monitoring System for Base Station Based on Cascaded Extreme Learning Machines[J].Journal of Jimei University,2018,23(6):475-480.
点击复制

基于级联极限学习机的基站空调在线监测系统()
分享到:

《集美大学学报(自然版)》[ISSN:1007-7405/CN:35-1186/N]

卷:
第23卷
期数:
2018年第6期
页码:
475-480
栏目:
数理科学与信息工程
出版日期:
2018-11-28

文章信息/Info

Title:
Air Conditioning Online Monitoring System for Base Station Based on Cascaded Extreme Learning Machines
作者:
罗方芳1陶求华2
(1.集美大学计算机工程学院,福建 厦门 361021;2.集美大学机械与能源工程学院,福建 厦门 361021)
Author(s):
LUO Fangfang1TAO Qiuhua2
(1.Computer Engineering College,Jimei University,Xiamen 361021,China;2.School of Mechanical and Energy Engineering,Jimei University,Xiamen 361021,China)
关键词:
基站空调故障诊断级联极限学习机在线监测
Keywords:
base station air-conditioningfault diagnosiscascaded extreme learning machinesonline monitoring
文献标志码:
A
摘要:
提出一种基于级联极限学习机的基站空调在线监测系统。首先,基于某基站空调公司提供的监测数据集构建多个原子极限学习机分类器,每一个原子极限学习机对应一种故障类别;再将各原子分类器以级联方式组合用于未知样本的故障诊断;最后将级联极限学习机与单独的多类极限学习机算法、SVM算法、BP神经网络算法、C4.5决策树算法进行比较测试。结果表明,级联极限学习机算法提高了小类样本的故障识别率,具有更高的故障诊断精度和较短的训练时间,且诊断时间达到在线实时的要求。
Abstract:
An efficient and real-time fault detection and diagnosis system for on-line base station air conditioner can ensure the stable operation of various equipment in the base station.Due to the fault categories of the base station air conditioner are nonlinear and unbalanced,an on-line monitoring system of cascaded extreme learning machines is proposed for the fault diagnosis of air conditioner in field base station.Firstly,based on the data set provided by a base station’s air conditioning company,a collection of basic binary extreme learning machine classifiers are constructed,in which each classifier corresponds to a fault category.Then,these basic binary classifiers are combined in a serial cascade to be applied to the fault diagnosis of new samples.Finally,the cascaded extreme learning machine algorithm is compared with single multi-class extreme learning machine,SVM algorithm,BP neural network algorithm and C4.5 decision tree algorithm when they are used in the online monitoring system of the field base station air conditioner.Experimental results show that the cascaded extreme learning machine algorithm can improve the recognition rate for minority class,and has the advantages of higher fault diagnosis accuracy and shorter training time than traditional algorithm.Furthermore,the test diagnosis time can meet the on-line and real-time requirement.

相似文献/References:

[1]王荣杰.基于相似度的电力电子电路故障诊断技术[J].集美大学学报(自然版),2010,15(5):372.
[2]王宁,陈景锋.基于油液监测的柴油机磨损故障诊断系统[J].集美大学学报(自然版),2012,17(3):212.
 WANG NingCHEN Jing-feng.Diesel Engine Wear Fault Diagnosis System Based on the Oil Monitoring Technology[J].Journal of Jimei University,2012,17(6):212.
[3]王永坚,陈景锋,杨小明.基于油液分析的船舶尾轴承状态监测与故障诊断[J].集美大学学报(自然版),2014,19(4):285.
 WANG Yong-jian,CHEN Jing-feng,YANG Xiao-ming.Condition Monitoring and Fault Diagnosis for Ship Stern Bearing Based on Lube Oil Monitoring Analysis[J].Journal of Jimei University,2014,19(6):285.
[4]崔博文.基于小波神经网络的逆变器功率开关故障诊断[J].集美大学学报(自然版),2017,22(1):46.
 CUI Bowen.Open-circuit Faults Diagnosis of Power Device inInverter Based on Wavelet and Neural Network[J].Journal of Jimei University,2017,22(6):46.

更新日期/Last Update: 2019-01-18