A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features
Gearbox failures can lead to substantial damage, significant financial losses due to maintenance downtimes, and, in some instances, fatalities. This study introduces an intelligent gear fault diagnosis system employing a convolutional neural network (CNN), utilizing vibration and thermal features ex...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Elsevier
2024
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/10963/1/J17474_6913d9be8e815071bc4a9ed648d52d56.pdf http://eprints.uthm.edu.my/10963/ https://doi.org/10.1016/j.dajour.2024.100399 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833419385137528832 |
|---|---|
| author | Pauline Ong, Pauline Ong Anelka John Koshy, Anelka John Koshy Kee Huong Lai, Kee Huong Lai Chee Kiong Sia, Chee Kiong Sia Maznan Ismon, Maznan Ismon |
| author_facet | Pauline Ong, Pauline Ong Anelka John Koshy, Anelka John Koshy Kee Huong Lai, Kee Huong Lai Chee Kiong Sia, Chee Kiong Sia Maznan Ismon, Maznan Ismon |
| author_sort | Pauline Ong, Pauline Ong |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Gearbox failures can lead to substantial damage, significant financial losses due to maintenance downtimes, and, in some instances, fatalities. This study introduces an intelligent gear fault diagnosis system employing a convolutional neural network (CNN), utilizing vibration and thermal features extracted from healthy, chipped, and broken tooth gear health categories. The performance of the CNN is compared with conventional machine learning models, including Naïve Bayes (NB), random forest (RF), and support vector machine (SVM) classifiers. Experimental investigations highlight CNN’s remarkable performance. With vibration features, the
CNN achieved 96.78% accuracy, surpassing SVM (84.89%), NB (81.56%), and RF (85.11%). The CNN attained 100% accuracy when utilizing thermal features, while SVM, NB, and RF achieved 91.11%, 88.89%, and 96.51% accuracies, respectively. |
| format | Article |
| id | my.uthm.eprints-10963 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| publisher | Elsevier |
| record_format | eprints |
| spelling | my.uthm.eprints-109632024-05-15T07:17:55Z http://eprints.uthm.edu.my/10963/ A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features Pauline Ong, Pauline Ong Anelka John Koshy, Anelka John Koshy Kee Huong Lai, Kee Huong Lai Chee Kiong Sia, Chee Kiong Sia Maznan Ismon, Maznan Ismon TJ Mechanical engineering and machinery Gearbox failures can lead to substantial damage, significant financial losses due to maintenance downtimes, and, in some instances, fatalities. This study introduces an intelligent gear fault diagnosis system employing a convolutional neural network (CNN), utilizing vibration and thermal features extracted from healthy, chipped, and broken tooth gear health categories. The performance of the CNN is compared with conventional machine learning models, including Naïve Bayes (NB), random forest (RF), and support vector machine (SVM) classifiers. Experimental investigations highlight CNN’s remarkable performance. With vibration features, the CNN achieved 96.78% accuracy, surpassing SVM (84.89%), NB (81.56%), and RF (85.11%). The CNN attained 100% accuracy when utilizing thermal features, while SVM, NB, and RF achieved 91.11%, 88.89%, and 96.51% accuracies, respectively. Elsevier 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/10963/1/J17474_6913d9be8e815071bc4a9ed648d52d56.pdf Pauline Ong, Pauline Ong and Anelka John Koshy, Anelka John Koshy and Kee Huong Lai, Kee Huong Lai and Chee Kiong Sia, Chee Kiong Sia and Maznan Ismon, Maznan Ismon (2024) A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features. Decision Analytics Journal, 10. pp. 1-9. https://doi.org/10.1016/j.dajour.2024.100399 |
| spellingShingle | TJ Mechanical engineering and machinery Pauline Ong, Pauline Ong Anelka John Koshy, Anelka John Koshy Kee Huong Lai, Kee Huong Lai Chee Kiong Sia, Chee Kiong Sia Maznan Ismon, Maznan Ismon A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features |
| title | A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features |
| title_full | A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features |
| title_fullStr | A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features |
| title_full_unstemmed | A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features |
| title_short | A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features |
| title_sort | deep learning approach for health monitoring in rotating machineries using vibrations and thermal features |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.uthm.edu.my/10963/1/J17474_6913d9be8e815071bc4a9ed648d52d56.pdf http://eprints.uthm.edu.my/10963/ https://doi.org/10.1016/j.dajour.2024.100399 |
| url_provider | http://eprints.uthm.edu.my/ |