Fault Diagnosis for Variable-Air-Volume Systems Using Fuzzy Neural Networks

被引：0
|
作者：
Xie Hui [1 ]
Liu Yan [2 ]
Li Deying [3 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Civil & Environm Engn, Beijing 100083, Peoples R China
[2] Univ Sci & Technol Beijing, Asset Management Ltd, Beijing 100083, Peoples R China
[3] Beijing Inst Civil Engn & Architecture, Sch Environm & Energy Engn, Beijing, Peoples R China
来源：
ICCSSE 2009: PROCEEDINGS OF 2009 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION | 2009年
关键词：
fault diagnosis; VAV air-conditioning system; self-organizing fuzzy neural networks; IDENTIFICATION; MODEL;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper presents a new method for fault diagnosis of variable air volume (VAV) air-conditioning systems. The method determines performance indices using self-organizing fuzzy neural networks (SOFNN). The SOFNN has two outstanding characteristics. Firstly, the learning speed is very fast and fuzzy rules can be generated quickly because no iterative learning is employed. Secondly, by using the pruning technology, significant nodes can be self-adaptive according to their contributions to the system performance. Consequently, the proposed method can achieve high performance with a parsimonious structure. Simulation results indicate that the SOFNN-based fault diagnosis method for VAV systems gives a very good performance in training speed and diagnosis speed and has high diagnosis rate.
引用
收藏
页码：183 / +
页数：3
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