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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| Online Access: | http://eprints.utem.edu.my/id/eprint/27119/2/0104616102023.PDF http://eprints.utem.edu.my/id/eprint/27119/ http://www.pe.org.pl/articles/2023/9/21.pdf |
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my.utem.eprints.271192024-06-19T16:15:43Z http://eprints.utem.edu.my/id/eprint/27119/ Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network Sulaiman, Noor Asyikin Sabal Menanti, Nur Amalina Abd Razak, Norazlina Zainudin, Muhammad Noorazlan Shah Norhidayah, Mohamad Yatim Md Yusop, Azdiana Abdullah, Md Pauzi 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. Wydawnictwo SIGMA-NOT 2023 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27119/2/0104616102023.PDF 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 http://www.pe.org.pl/articles/2023/9/21.pdf 10.15199/48.2023.09.21 |
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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. |
| format |
Article |
| author |
Sulaiman, Noor Asyikin Sabal Menanti, Nur Amalina Abd Razak, Norazlina Zainudin, Muhammad Noorazlan Shah Norhidayah, Mohamad Yatim Md Yusop, Azdiana Abdullah, Md Pauzi |
| spellingShingle |
Sulaiman, Noor Asyikin Sabal Menanti, Nur Amalina Abd Razak, Norazlina Zainudin, Muhammad Noorazlan Shah Norhidayah, Mohamad Yatim Md Yusop, Azdiana Abdullah, Md Pauzi Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network |
| author_facet |
Sulaiman, Noor Asyikin Sabal Menanti, Nur Amalina Abd Razak, Norazlina Zainudin, Muhammad Noorazlan Shah Norhidayah, Mohamad Yatim Md Yusop, Azdiana Abdullah, Md Pauzi |
| author_sort |
Sulaiman, Noor Asyikin |
| title |
Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network |
| title_short |
Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network |
| title_full |
Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network |
| title_fullStr |
Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network |
| title_full_unstemmed |
Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network |
| title_sort |
fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network |
| publisher |
Wydawnictwo SIGMA-NOT |
| publishDate |
2023 |
| url |
http://eprints.utem.edu.my/id/eprint/27119/2/0104616102023.PDF http://eprints.utem.edu.my/id/eprint/27119/ http://www.pe.org.pl/articles/2023/9/21.pdf |
| _version_ |
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