Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

Fault detection for air conditioning system using machine learning

IAES International Journal of Artificial Intelligence · Vol. 9 No. 1 (2020) · hlm. 109–116

DOI
10.11591/ijai.v9.i1.pp109-116
ISSN
2252-8938, 2089-4872
Terbit
2020-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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, 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.

Kata kunci

Jaringan kolaborasi para penulis

28 penulis · 90 tautan kepenulisan bersama · 26 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Universiti Teknikal Malaysia Melaka
  • University of Technology Malaysia
  • MILA University
  • Universiti Teknologi MARA
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Mohammad Yusri HassanUniversity of Technology Malaysia2
Faridah HussinUniversity of Technology Malaysia2
Wira Hidayat Mohd SaadUniversiti Teknikal Malaysia Melaka2
Mohd Safirin KarisUniversiti Teknikal Malaysia Melaka2
Hyreil Anuar KasdirinUniversiti Teknikal Malaysia Melaka2
Dalila Mat SaidUniversity of Technology Malaysia2
Nur Farahin Asa EsaUniversity of Technology Malaysia1
Mohd Fua’ad RahmatUniversity of Technology Malaysia1
Ahmad Athif Mohd FaudziUniversity of Technology Malaysia1
Khairuddin OsmanUniversiti Teknikal Malaysia Melaka1
Amar Faiz Zainal AbidinUniversiti Teknikal Malaysia Melaka1
Sharatul Izah SamsudinUniversiti Teknikal Malaysia Melaka1
Siti Fatimah SulaimanUniversiti Teknikal Malaysia Melaka1
Abdul Hafiz RazaliUniversity of Technology Malaysia1
Nasarudin AhmadUniversity of Technology Malaysia1
Zaris Izzati Mohd YassinMILA University1
NN Nik Abd MalikUniversity of Technology Malaysia1
Nursabilillah Mohd AliAfiliasi tidak diketahui1
Mohd Shahrieel Mohd ArasUniversiti Teknikal Malaysia Melaka1
Norafizah Binti AbasUniversiti Teknikal Malaysia Melaka1
Norafizah AbasUniversiti Teknikal Malaysia Melaka1
Nursabilillah Mohd AliUniversiti Teknikal Malaysia Melaka1
Zarina RazlanUniversiti Teknologi MARA1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Fault detection for air conditioning system using machine learning</dc:title>
	<dc:creator>Sulaiman, Noor Asyikin</dc:creator>
	<dc:creator>Abdullah, Md Pauzi</dc:creator>
	<dc:creator>Abdullah, Hayati</dc:creator>
	<dc:creator>Zainudin, Muhammad Noorazlan Shah</dc:creator>
	<dc:creator>Md Yusop, Azdiana</dc:creator>
	<dc:subject xml:lang="en-US">Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">Air Conditioning System; Fault detection; Machine learning</dc:subject>
	<dc:description xml:lang="en-US">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, 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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2020-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20422</dc:identifier>
	<dc:identifier>10.11591/ijai.v9.i1.pp109-116</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 9, No 1: March 2020; 109-116</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v9.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20422/12920</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>