Fault detection and diagnosis system for air-conditioning units using recurrent type neural network

Author

Samarasinghe, Herath K U ; Hashimoto, Shuji

Author_Institution

Dept. of Appl. Phys., Waseda Univ., Tokyo, Japan

Volume

4

fYear

2000

fDate

2000

Firstpage

2637

Abstract

The air-conditioning systems of buildings have been diversified in recent years, and the complexity of the systems has increased. At the same time, stability in the system and low running cost are demanded. To solve these problems, various research projects have been done. The development of the energy load prediction systems and the fault detection and diagnosis systems have received great attention. The authors propose a real time fault diagnosis system for air conditioning units (the heating unit, the cooling unit, the air intake unit, and the air-recycling unit) using a recurrent type neural network

Keywords

air conditioning; load forecasting; power system analysis computing; power system faults; power system reliability; real-time systems; recurrent neural nets; air conditioning units; air intake unit; air-conditioning systems; air-conditioning units; air-recycling unit; buildings; cooling unit; energy load prediction systems; fault detection; heating unit; real time fault diagnosis system; recurrent type neural network; running cost; Air conditioning; Cooling; Costs; Fault detection; Fault diagnosis; Heating; Neural networks; Real time systems; Recurrent neural networks; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2000 IEEE International Conference on

Conference_Location

Nashville, TN

ISSN

1062-922X

Print_ISBN

0-7803-6583-6

Type

conf

DOI

10.1109/ICSMC.2000.884392

Filename

884392