Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

Sulaiman, Noor Asyikin and Md Yusop, Azdiana and Zainudin, Muhammad Noorazlan Shah and Sulaiman, Siti Fatimah and Abdullah, Md Pauzi and Abdullah, Hayati (2022) Parameter selection in data-driven fault detection and diagnosis of the air conditioning system. Indonesian Journal Of Electrical Engineering And Computer Science, 25 (1). pp. 59-67. ISSN 2502-4752

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Abstract

Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful enough to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge of data driven FDD is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralised chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data- driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data- driven FDD: the rooms' temperature, TTCx, supplied chilled water temperature, TCHWS, supplied chilled water flow rate, VCHWS, and supplied cooled water temperature, TCWS. Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6% - 3.4% differed fromthe original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy.

Item Type:	Article
Uncontrolled Keywords:	Air conditioning system, Data-driven FDD, Parameter selection
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	06 Mar 2023 11:44
Last Modified:	06 Mar 2023 11:44
URI:	http://eprints.utem.edu.my/id/eprint/26263
Statistic Details:	View Download Statistic

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