Fault Detection and Diagnosis of an HVAC System Using Artificial Immune Recognition System

被引：0
|
作者：
Chang, Long [1 ]
Wang, Hong [2 ]
Wang, Lingfeng [1 ]
机构：
[1] Univ Toledo, Coll Engn, Dept Elect Engn & Comp Sci, Toledo, OH 43606 USA
[2] Univ Toledo, Coll Engn, Dept Engn Technol, Toledo, OH 43606 USA
来源：
2013 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC) | 2013年
关键词：
Artificial immune systems; HVAC system; fault detection and diagnosis; data classification; ALGORITHM;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Heating, Ventilation, and Air Conditioning (HVAC) is essential to providing a comfortable indoor environment for the occupants in a building, while it also consumes a majority of the building energy. Operating with fault, the HVAC system can be more energy consuming and eventually lead to the degraded comfort experience of occupants. This paper is focused on applying an intelligent classification method, Artificial Immune Recognition System (AIRS), in solving the Fault Detection and Diagnosis (FDD) problem of an HVAC system simulated by Trnsys. The AIRS classifier is run on the WEKA classification tool. Thirteen fault types for a selected zone in a multi-zone building are considered in this study. To achieve more comprehensive results, the simulation is carried out for a whole year.
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页数：5
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