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A deep convolutional neural network for vibration-based health-monitoring of rotating machinery

The gearbox is a critical component in the mechanical system, requiring vigilant monitoring to prevent adverse consequences on safety and quality due to malfunction. Therefore, early fault diagnosis of the gearbox before the fatal breakdown of the entire mechanical system is of imperative importan...

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Main Authors: Ong Pauline, Ong Pauline, Tan Yean Keong, Tan Yean Keong, Lai Kee Huong, Lai Kee Huong, Sia Chee Kiong, Sia Chee Kiong
Format: Article
Language:English
Published: Elsevier 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/9430/1/J15990_2064fc6358db37f4a127145f5fa98b10.pdf
http://eprints.uthm.edu.my/9430/
https://doi.org/10.1016/j.dajour.2023.100219
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spelling my.uthm.eprints.94302023-07-30T07:12:45Z http://eprints.uthm.edu.my/9430/ A deep convolutional neural network for vibration-based health-monitoring of rotating machinery Ong Pauline, Ong Pauline Tan Yean Keong, Tan Yean Keong Lai Kee Huong, Lai Kee Huong Sia Chee Kiong, Sia Chee Kiong T Technology (General) The gearbox is a critical component in the mechanical system, requiring vigilant monitoring to prevent adverse consequences on safety and quality due to malfunction. Therefore, early fault diagnosis of the gearbox before the fatal breakdown of the entire mechanical system is of imperative importance. This study proposes a onedimensional deep convolutional neural network (1D-DCNN) to learn features directly from the vibrational signals and identify the gear fault under different health conditions. The performance is compared with the decision tree, random forest, and support vector machine to validate the superiority of the 1D-DCNN. Experimental results showed that the proposed scheme outperforms other comparative methods, with a diagnostic accuracy of 97.11 %, thus confirming its effectiveness. Elsevier 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9430/1/J15990_2064fc6358db37f4a127145f5fa98b10.pdf Ong Pauline, Ong Pauline and Tan Yean Keong, Tan Yean Keong and Lai Kee Huong, Lai Kee Huong and Sia Chee Kiong, Sia Chee Kiong (2023) A deep convolutional neural network for vibration-based health-monitoring of rotating machinery. Decision Analytics Journal, 7. pp. 1-9. https://doi.org/10.1016/j.dajour.2023.100219
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Ong Pauline, Ong Pauline
Tan Yean Keong, Tan Yean Keong
Lai Kee Huong, Lai Kee Huong
Sia Chee Kiong, Sia Chee Kiong
A deep convolutional neural network for vibration-based health-monitoring of rotating machinery
description The gearbox is a critical component in the mechanical system, requiring vigilant monitoring to prevent adverse consequences on safety and quality due to malfunction. Therefore, early fault diagnosis of the gearbox before the fatal breakdown of the entire mechanical system is of imperative importance. This study proposes a onedimensional deep convolutional neural network (1D-DCNN) to learn features directly from the vibrational signals and identify the gear fault under different health conditions. The performance is compared with the decision tree, random forest, and support vector machine to validate the superiority of the 1D-DCNN. Experimental results showed that the proposed scheme outperforms other comparative methods, with a diagnostic accuracy of 97.11 %, thus confirming its effectiveness.
format Article
author Ong Pauline, Ong Pauline
Tan Yean Keong, Tan Yean Keong
Lai Kee Huong, Lai Kee Huong
Sia Chee Kiong, Sia Chee Kiong
author_facet Ong Pauline, Ong Pauline
Tan Yean Keong, Tan Yean Keong
Lai Kee Huong, Lai Kee Huong
Sia Chee Kiong, Sia Chee Kiong
author_sort Ong Pauline, Ong Pauline
title A deep convolutional neural network for vibration-based health-monitoring of rotating machinery
title_short A deep convolutional neural network for vibration-based health-monitoring of rotating machinery
title_full A deep convolutional neural network for vibration-based health-monitoring of rotating machinery
title_fullStr A deep convolutional neural network for vibration-based health-monitoring of rotating machinery
title_full_unstemmed A deep convolutional neural network for vibration-based health-monitoring of rotating machinery
title_sort deep convolutional neural network for vibration-based health-monitoring of rotating machinery
publisher Elsevier
publishDate 2023
url http://eprints.uthm.edu.my/9430/1/J15990_2064fc6358db37f4a127145f5fa98b10.pdf
http://eprints.uthm.edu.my/9430/
https://doi.org/10.1016/j.dajour.2023.100219
_version_ 1773545898845929472
score 13.211869

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