基于遗传算法和RBF的制冷机组实时FDD

Yonghong Huang, Jihong Zhan
{"title":"基于遗传算法和RBF的制冷机组实时FDD","authors":"Yonghong Huang, Jihong Zhan","doi":"10.1109/IUCE.2009.40","DOIUrl":null,"url":null,"abstract":"Purpose of this research is to describe the application of a Radius Basis Function (RBF) network to the problem of real-time Fault Detection and Diagnosis (FDD) in a vapor compression refrigeration system. First, we analyze the common refrigeration system faults and their dominant symptoms. Next, an FDD strategy for the refrigeration system is proposed which adopts a RBF network to model the causation of symptoms and faults. Gaussian functions are selected as the basis functions of the hidden layer neurons. The parameters of the Gaussian functions and the weights of the network are ob-tained by using a novel network training method which com-bines Genetic Algorithm (GA) and psudo-inverse matrix algorithm.  Finally, a real-time   FDD program in C language based on the proposed strategy is developed. The FDD program is tested with the refrigeration system installed in a laboratory and successfully identifies each of the six faults artificially in-troduced at the laboratory.","PeriodicalId":153560,"journal":{"name":"2009 International Symposium on Intelligent Ubiquitous Computing and Education","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"GA and RBF Based Real-Time FDD for Refrigeration Units\",\"authors\":\"Yonghong Huang, Jihong Zhan\",\"doi\":\"10.1109/IUCE.2009.40\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose of this research is to describe the application of a Radius Basis Function (RBF) network to the problem of real-time Fault Detection and Diagnosis (FDD) in a vapor compression refrigeration system. First, we analyze the common refrigeration system faults and their dominant symptoms. Next, an FDD strategy for the refrigeration system is proposed which adopts a RBF network to model the causation of symptoms and faults. Gaussian functions are selected as the basis functions of the hidden layer neurons. The parameters of the Gaussian functions and the weights of the network are ob-tained by using a novel network training method which com-bines Genetic Algorithm (GA) and psudo-inverse matrix algorithm.  Finally, a real-time   FDD program in C language based on the proposed strategy is developed. The FDD program is tested with the refrigeration system installed in a laboratory and successfully identifies each of the six faults artificially in-troduced at the laboratory.\",\"PeriodicalId\":153560,\"journal\":{\"name\":\"2009 International Symposium on Intelligent Ubiquitous Computing and Education\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Symposium on Intelligent Ubiquitous Computing and Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IUCE.2009.40\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Symposium on Intelligent Ubiquitous Computing and Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IUCE.2009.40","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本研究的目的是描述半径基函数(RBF)网络在蒸汽压缩制冷系统实时故障检测与诊断(FDD)中的应用。首先,分析了制冷系统常见的故障及其主要症状。其次,提出了一种针对制冷系统的FDD策略,该策略采用RBF网络对症状和故障原因进行建模。选取高斯函数作为隐层神经元的基函数。采用遗传算法和伪逆矩阵算法相结合的网络训练方法,获得高斯函数的参数和网络的权值。最后,基于所提出的策略,用C语言编写了一个实时FDD程序。FDD程序与安装在实验室的制冷系统进行了测试,成功地识别了实验室人为引入的六个故障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GA and RBF Based Real-Time FDD for Refrigeration Units
Purpose of this research is to describe the application of a Radius Basis Function (RBF) network to the problem of real-time Fault Detection and Diagnosis (FDD) in a vapor compression refrigeration system. First, we analyze the common refrigeration system faults and their dominant symptoms. Next, an FDD strategy for the refrigeration system is proposed which adopts a RBF network to model the causation of symptoms and faults. Gaussian functions are selected as the basis functions of the hidden layer neurons. The parameters of the Gaussian functions and the weights of the network are ob-tained by using a novel network training method which com-bines Genetic Algorithm (GA) and psudo-inverse matrix algorithm.  Finally, a real-time   FDD program in C language based on the proposed strategy is developed. The FDD program is tested with the refrigeration system installed in a laboratory and successfully identifies each of the six faults artificially in-troduced at the laboratory.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信