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

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

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 condi...

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Bibliographic Details
Main Authors: Sulaiman, Noor Asyikin, Sabal Menanti, Nur Amalina, Md. Yusop, Azdiana, Zainudin, Muhammad Noorazlan Shah, Mohamad Yatim, Norhidayah, Abd. Razak, Norazlina, Abdullah, Md. Pauzi
Format: Article
Published: Wydawnictwo SIGMA-NOT 2023
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/106632/
http://dx.doi.org/10.15199/48.2023.09.21
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Institution: Universiti Teknologi Malaysia
Description
Summary: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.

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