Fault detection for air conditioning system using machine learning

• Main Authors: Sulaiman, Noor Asyikin, Abdullah, Md. Pauzi, Abdullah, Hayati, Zainudin, Muhammad Noorazlan Shah, Md. Yusop, Azdiana
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2020
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/91178/1/MdPauziAbdullah2020_FaultDetectionforAirConditioningSystem.pdf; http://eprints.utm.my/id/eprint/91178/; http://dx.doi.org/10.11591/ijai.v9.i1.pp109-116
• Institution: Universiti Teknologi Malaysia

Air conditioning system is a complex system and consumes the most energy in a building. Any fault in the system operation such as cooling tower fan faulty, compressor failure, and damper stuck, etc. could lead to energy wastage and reduction in the system’s coefficient of performance (COP). Due to the complexity of the air conditioning system, detecting those faults is hard as it requires exhaustive inspections. This paper consists of two parts; i) to investigate the impact of different faults related to the air conditioning system on COP and ii) to analyse the performances of machine learning algorithms to classify those faults. Three supervised learning classifier models were developed, which were deep learning, support vector machine (SVM) and multi-layer perceptron (MLP). The performances of each classifier were investigated in terms of six different classes of faults. Results showed that different faults give different negative impacts on the COP. Also, the three supervised learning classifier models able to classify all faults for more than 94%, and MLP produced the highest accuracy and precision among all.