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 (Wydawnictwo SIGMA-NOT, 2023)
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.