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Rotating blade faults classification of a rotor-disk-blade system using artificial neural network

International Journal of Power Electronics and Drive Systems · Vol. 12 No. 3 (2021) · hlm. 1900–1911

DOI
10.11591/ijpeds.v12.i3.pp1900-1911
ISSN
2722-256X, 2088-8694
Terbit
2021-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.

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  • University of Technology Malaysia
  • Robert Gordon University
  • Jubail Industrial College
  • University of Kuala Lumpur
  • Jubail University College
  • Glasgow Caledonian University
  • Universidad Técnica Federico Santa María
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Rekan penulisOrganisasiArtikel bersama
Nurul Aini BaniUniversity of Technology Malaysia8
Firdaus Muhammad‐SukkiRobert Gordon University8
Mohd Nabil MuhtazaruddinUniversity of Technology Malaysia6
Siti Hawa Abu-BakarUniversity of Kuala Lumpur5
Jorge Alfredo Ardila ReyUniversidad Técnica Federico Santa María4
Nazmi SellamiRobert Gordon University3
Jasrul Jamani JamianUniversity of Technology Malaysia2
Z Abdul-MalekUniversity of Technology Malaysia2
Kamilia KamardinUniversity of Technology Malaysia2
Hamza AhmadTun Hussein Onn University of Malaysia2
Abu Bakar MunirUniversity of Malaya2
Hassan Zuhair Al GarniJubail Industrial College2
Surajudeen AdewusiJubail University College2
Daria FreierGlasgow Caledonian University1
Roberto Ramírez-ÍñiguezGlasgow Caledonian University1
Tapas Kumar MallickUniversity of Exeter1
Siti Hajar Mohd YasinUniversiti Teknologi MARA1
Siti Sarah Abu-BakarKing's College London1
Zulkurnain Abdul‐MalekUniversity of Technology Malaysia1
Siti Armiza Mohd ArisUniversity of Technology Malaysia1
Siti Zura A. JalilUniversity of Technology Malaysia1
Hazilah Mad KaidiUniversity of Technology Malaysia1
Sharipah Alwiah Syed Abd RahamanUniversity of Technology Malaysia1
Ahmad Syahir AyubAfiliasi tidak diketahui1
Jonas TaverneRobert Gordon University1
Draco IyiRobert Gordon University1
Ibrahim Abubakar Mas’udAfiliasi tidak diketahui1
Ridoan KarimMonash University Malaysia1
Mu’azu Mohammed AbdullahiUniversity of Hafr Al-Batin1
Ahmad Shakir Mohd SaudiUniversity of Kuala Lumpur1
Asan Vernyuy WirbaJubail Industrial College1
Mohd Alif Saifuddin JamalludinAfiliasi tidak diketahui1
Fadzliana RamleeUniversity of Glasgow1
Ahmad Syahrir AyubUniversity of Kuala Lumpur1
Mantas DarameičikasRobert Gordon University1

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://ijpeds.iaescore.com/index.php/IJPEDS/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Rotating blade faults classification of a rotor-disk-blade system using artificial neural network</dc:title>
	<dc:creator>Abubakar Mas’ud, Abdullahi</dc:creator>
	<dc:creator>Jamal, Ahmad</dc:creator>
	<dc:creator>Adewusi, Surajuddeen</dc:creator>
	<dc:creator>Sundaram, Arunachalam</dc:creator>
	<dc:subject xml:lang="en-US">Artificial neural networks; Blade faults; Blade system; Python; Rotating machinery</dc:subject>
	<dc:description xml:lang="en-US">In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.</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>2021-09-01</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://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21430</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v12.i3.pp1900-1911</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 12, No 3: September 2021; 1900-1911</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v12.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21430/13540</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Abdullahi Abubakar Mas’ud, Ahmad Jamal, Surajuddeen Adewusi, Arunachalam Sundaram</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>