Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network

Sulaiman, Noor Asyikin and Sabal Menanti, Nur Amalina and Abd Razak, Norazlina and Zainudin, Muhammad Noorazlan Shah and Norhidayah, Mohamad Yatim and Md Yusop, Azdiana and Abdullah, Md Pauzi (2023) Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network. Przeglad Elektrotechniczny, 9. pp. 113-117. ISSN 0033-2097

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Abstract

In this project, a fault detection and diagnosis (FDD) system was developed using Long Short-Term Memory Recurrent Neural Network (LSTM RNN), to detect and classify six common faults in a centralised chilled water air conditioning system. Datasets from a lab-scale centralised chilled water air conditioning system were used in the developed model. Results showed that the classifier model demonstrated a classification accuracy of over 99.3% for all six classes.

Item Type:	Article
Uncontrolled Keywords:	Chilled water system, Fault detection and diagnosis, LTSM-RNN
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	19 Jun 2024 16:15
Last Modified:	19 Jun 2024 16:15
URI:	http://eprints.utem.edu.my/id/eprint/27119
Statistic Details:	View Download Statistic

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