Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis

Tun, W; Wong, JK-W; Ling, S-H

Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis

Tun, W Wong, JK-W Ling, S-H

Permalink

Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sensors, 2021, 21, (24), pp. 8163
Issue Date:: 2021-12-07

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (771.21 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Tun, W
dc.contributor.author	Wong, JK-W
dc.contributor.author	Ling, S-H
dc.date.accessioned	2022-01-06T22:25:42Z
dc.date.available	2022-01-06T22:25:42Z
dc.date.issued	2021-12-07
dc.identifier.citation	Sensors, 2021, 21, (24), pp. 8163
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/152779
dc.description.abstract	<jats:p>The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Sensors
dc.relation.isbasedon	10.3390/s21248163
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.title	Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2022-01-06T22:25:41Z
pubs.issue	24
pubs.publication-status	Published online
pubs.volume	21
utslib.citation.issue	24

Abstract:

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/152779