Noise Reduction in CWRU Data Using DAE and Classification with ViT
With the Fourth Industrial Revolution unfolding worldwide, technologies including the Internet of Things, sensors, and artificial intelligence are undergoing rapid development. These technological advancements have played a significant role in the dramatic growth of the predictive maintenance market...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/11771 |
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| author | Jun-gyo Jang Soon-sup Lee Se-yun Hwang Jae-chul Lee |
| author_facet | Jun-gyo Jang Soon-sup Lee Se-yun Hwang Jae-chul Lee |
| author_sort | Jun-gyo Jang |
| collection | DOAJ |
| description | With the Fourth Industrial Revolution unfolding worldwide, technologies including the Internet of Things, sensors, and artificial intelligence are undergoing rapid development. These technological advancements have played a significant role in the dramatic growth of the predictive maintenance market for mechanical equipment, prompting active research on noise removal techniques and classification algorithms for the accurate determination of the causes of equipment failure. In this study, time series data were preprocessed using the denoising autoencoder technique, a deep learning-based noise removal method, to improve the accuracy of failure classification from mechanical equipment data. To convert the preprocessed time series data into frequency components, the short-time Fourier transform technique was employed. The fault types of mechanical equipment were classified using the vision transformer (ViT) technique, a deep learning technique that has been actively used in recent image analysis research. Additionally, the classification performance of the ViT-based technique for vibration time series data was comparatively validated against existing classification algorithms. The accuracy of failure classification was the highest when the data, preprocessed using a Denoising Autoencoder (DAE), were classified by a Vision Transformer (ViT). |
| format | Article |
| id | doaj-art-8dda185af9544b7595da1ab7c4ea18be |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-8dda185af9544b7595da1ab7c4ea18be2025-08-20T02:57:05ZengMDPI AGApplied Sciences2076-34172024-12-0114241177110.3390/app142411771Noise Reduction in CWRU Data Using DAE and Classification with ViTJun-gyo Jang0Soon-sup Lee1Se-yun Hwang2Jae-chul Lee3ADIALAB, 702-1, 57 Centum-dong-ro, Haeundae-gu, Busan 48059, Republic of KoreaDepartment of Naval Architecture and Ocean Engineering, College of Ocean Sciences, Gyeongsang National University, 11-dong, 2 Tongyeonghaean-ro, Tongyeong-si 53064, Gyeongsangnam-do, Republic of KoreaDepartment of Naval Architecture and Ocean Engineering, College of Ocean Sciences, Gyeongsang National University, 11-dong, 2 Tongyeonghaean-ro, Tongyeong-si 53064, Gyeongsangnam-do, Republic of KoreaDepartment of Naval Architecture and Ocean Engineering, College of Ocean Sciences, Gyeongsang National University, 11-dong, 2 Tongyeonghaean-ro, Tongyeong-si 53064, Gyeongsangnam-do, Republic of KoreaWith the Fourth Industrial Revolution unfolding worldwide, technologies including the Internet of Things, sensors, and artificial intelligence are undergoing rapid development. These technological advancements have played a significant role in the dramatic growth of the predictive maintenance market for mechanical equipment, prompting active research on noise removal techniques and classification algorithms for the accurate determination of the causes of equipment failure. In this study, time series data were preprocessed using the denoising autoencoder technique, a deep learning-based noise removal method, to improve the accuracy of failure classification from mechanical equipment data. To convert the preprocessed time series data into frequency components, the short-time Fourier transform technique was employed. The fault types of mechanical equipment were classified using the vision transformer (ViT) technique, a deep learning technique that has been actively used in recent image analysis research. Additionally, the classification performance of the ViT-based technique for vibration time series data was comparatively validated against existing classification algorithms. The accuracy of failure classification was the highest when the data, preprocessed using a Denoising Autoencoder (DAE), were classified by a Vision Transformer (ViT).https://www.mdpi.com/2076-3417/14/24/11771failure diagnosisvision transformerdenoising auto encodervibration data |
| spellingShingle | Jun-gyo Jang Soon-sup Lee Se-yun Hwang Jae-chul Lee Noise Reduction in CWRU Data Using DAE and Classification with ViT Applied Sciences failure diagnosis vision transformer denoising auto encoder vibration data |
| title | Noise Reduction in CWRU Data Using DAE and Classification with ViT |
| title_full | Noise Reduction in CWRU Data Using DAE and Classification with ViT |
| title_fullStr | Noise Reduction in CWRU Data Using DAE and Classification with ViT |
| title_full_unstemmed | Noise Reduction in CWRU Data Using DAE and Classification with ViT |
| title_short | Noise Reduction in CWRU Data Using DAE and Classification with ViT |
| title_sort | noise reduction in cwru data using dae and classification with vit |
| topic | failure diagnosis vision transformer denoising auto encoder vibration data |
| url | https://www.mdpi.com/2076-3417/14/24/11771 |
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