Data-driven fault detection and diagnosis for centralised chilled water air conditioning system

• Main Authors: Sulaiman, Noor Asyikin, Chuink, Kai Wern, Zainudin, Muhammad Noorazlan Shah, Md. Yusop, Azdiana, Sulaiman, Siti Fatimah, Abdullah, Md. Pauzi
• Format: Article
• Published: Wydawnictwo SIGMA-NOT 2022
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/103892/; http://dx.doi.org/10.15199/48.2022.01.47
• Institution: Universiti Teknologi Malaysia

The air conditioning system is complex and consumes the most energy in the building. Due to its complexity, it is difficult to identify faults in the system immediately. In this project, fault detection and diagnosis system using decision tree classifier model was developed to detect and diagnose faults in a chilled water air conditioning system. The developed model successfully classified normal condition and five common faults for more than 99% accuracy and precision. A graphical user interface of the system was also developed to ease the users.