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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| Format: | Article |
| Language: | English |
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Elsevier
2023
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| Online Access: | http://eprints.uthm.edu.my/9902/ http://eprints.uthm.edu.my/9902/1/J15990_2064fc6358db37f4a127145f5fa98b10.pdf |
| _version_ | 1848889804842860544 |
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| 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 |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T20:32:00Z |
| format | Article |
| id | uthm-9902 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:32:00Z |
| publishDate | 2023 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-99022023-09-13T07:28:47Z http://eprints.uthm.edu.my/9902/ 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/9902/1/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. 1-8. https://doi.org/10.1016/j.dajour.2023.100219 |
| 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 |
| title | 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_short | 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 |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/9902/ http://eprints.uthm.edu.my/9902/ http://eprints.uthm.edu.my/9902/1/J15990_2064fc6358db37f4a127145f5fa98b10.pdf |