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

Herath K U Samarasinghe; Shuji Hashimoto

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

Herath K U Samarasinghe^*, Shuji Hashimoto

^*この研究の対応する著者

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

The air-conditioning systems of buildings have been diversified in recent years, and the complexity of the system has been increased. At the same time, stability in the system and the low-running cost are demanded. To solve these problems, various researches have been done. The development of the energy load prediction systems and the faults detection and diagnosis systems have received greater attention. In this paper, we 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.

本文言語	English
ホスト出版物のタイトル	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
出版社	IEEE
ページ	2637-2642
ページ数	6
巻	4
出版ステータス	Published - 2000
イベント	2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA 継続期間: 2000 10月 8 → 2000 10月 11

Other

Other	2000 IEEE International Conference on Systems, Man and Cybernetics
City	Nashville, TN, USA
Period	00/10/8 → 00/10/11

ASJC Scopus subject areas

ハードウェアとアーキテクチャ
制御およびシステム工学

他のファイルとリンク

引用スタイル

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abstract = "The air-conditioning systems of buildings have been diversified in recent years, and the complexity of the system has been increased. At the same time, stability in the system and the low-running cost are demanded. To solve these problems, various researches have been done. The development of the energy load prediction systems and the faults detection and diagnosis systems have received greater attention. In this paper, we 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.",

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year = "2000",

language = "English",

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AU - Samarasinghe, Herath K U

AU - Hashimoto, Shuji

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N2 - The air-conditioning systems of buildings have been diversified in recent years, and the complexity of the system has been increased. At the same time, stability in the system and the low-running cost are demanded. To solve these problems, various researches have been done. The development of the energy load prediction systems and the faults detection and diagnosis systems have received greater attention. In this paper, we 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.

AB - The air-conditioning systems of buildings have been diversified in recent years, and the complexity of the system has been increased. At the same time, stability in the system and the low-running cost are demanded. To solve these problems, various researches have been done. The development of the energy load prediction systems and the faults detection and diagnosis systems have received greater attention. In this paper, we 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.

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VL - 4

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EP - 2642

BT - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

PB - IEEE

T2 - 2000 IEEE International Conference on Systems, Man and Cybernetics

Y2 - 8 October 2000 through 11 October 2000

ER -

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

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Other

ASJC Scopus subject areas

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