Sulaiman, Noor Asyikin and Sabal Menanti, Nur Amalina and Md. Yusop, Azdiana and Zainudin, Muhammad Noorazlan Shah and Mohamad Yatim, Norhidayah and Abd. Razak, Norazlina and Abdullah, Md. Pauzi (2023) Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network. Przeglad Elektrotechniczny, 2023 (9). pp. 113-117. ISSN 0033-2097
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Official URL: http://dx.doi.org/10.15199/48.2023.09.21
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 |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Electrical Engineering |
| ID Code: | 106632 |
| Deposited By: | Yanti Mohd Shah |
| Deposited On: | 09 Jul 2024 08:08 |
| Last Modified: | 09 Jul 2024 08:08 |
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