Parameter selection in data-driven fault detection and diagnosis of the air conditioning system
Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled cen...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Institute of Advanced Engineering and Science
2022
|
Subjects: | |
Online Access: | http://eprints.utm.my/104457/1/MdPauziAbdullah2022_ParameterSelectioninDataDrivenFault.pdf http://eprints.utm.my/104457/ http://dx.doi.org/10.11591/ijeecs.v25.i1.pp59-67 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.104457 |
---|---|
record_format |
eprints |
spelling |
my.utm.1044572024-02-08T08:02:57Z http://eprints.utm.my/104457/ Parameter selection in data-driven fault detection and diagnosis of the air conditioning system Sulaiman, Noor Asyikin Abdullah, Md. Pauzi Abdullah, Hayati Zainudin, Muhammad Noorazlan Shah Md. Yusop, Azdiana Sulaiman, Siti Fatimah TK Electrical engineering. Electronics Nuclear engineering Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralized 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 from the 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. Institute of Advanced Engineering and Science 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/104457/1/MdPauziAbdullah2022_ParameterSelectioninDataDrivenFault.pdf Sulaiman, Noor Asyikin and Abdullah, Md. Pauzi and Abdullah, Hayati and Zainudin, Muhammad Noorazlan Shah and Md. Yusop, Azdiana and Sulaiman, Siti Fatimah (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 http://dx.doi.org/10.11591/ijeecs.v25.i1.pp59-67 DOI : 10.11591/ijeecs.v25.i1.pp59-67 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
language |
English |
topic |
TK Electrical engineering. Electronics Nuclear engineering |
spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Sulaiman, Noor Asyikin Abdullah, Md. Pauzi Abdullah, Hayati Zainudin, Muhammad Noorazlan Shah Md. Yusop, Azdiana Sulaiman, Siti Fatimah Parameter selection in data-driven fault detection and diagnosis of the air conditioning system |
description |
Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralized 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 from the 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. |
format |
Article |
author |
Sulaiman, Noor Asyikin Abdullah, Md. Pauzi Abdullah, Hayati Zainudin, Muhammad Noorazlan Shah Md. Yusop, Azdiana Sulaiman, Siti Fatimah |
author_facet |
Sulaiman, Noor Asyikin Abdullah, Md. Pauzi Abdullah, Hayati Zainudin, Muhammad Noorazlan Shah Md. Yusop, Azdiana Sulaiman, Siti Fatimah |
author_sort |
Sulaiman, Noor Asyikin |
title |
Parameter selection in data-driven fault detection and diagnosis of the air conditioning system |
title_short |
Parameter selection in data-driven fault detection and diagnosis of the air conditioning system |
title_full |
Parameter selection in data-driven fault detection and diagnosis of the air conditioning system |
title_fullStr |
Parameter selection in data-driven fault detection and diagnosis of the air conditioning system |
title_full_unstemmed |
Parameter selection in data-driven fault detection and diagnosis of the air conditioning system |
title_sort |
parameter selection in data-driven fault detection and diagnosis of the air conditioning system |
publisher |
Institute of Advanced Engineering and Science |
publishDate |
2022 |
url |
http://eprints.utm.my/104457/1/MdPauziAbdullah2022_ParameterSelectioninDataDrivenFault.pdf http://eprints.utm.my/104457/ http://dx.doi.org/10.11591/ijeecs.v25.i1.pp59-67 |
_version_ |
1792147746488057856 |
score |
13.160551 |