Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning
To realize high-precision and high-efficiency machine fault diagnosis, a novel deep learning framework that combines transfer learning and transposed convolution is proposed. Compared with existing methods, this method has faster training speed, fewer training samples per time, and higher accuracy....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8863388 |
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| _version_ | 1832550829878411264 |
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| author | Jianye Zhou Xinyu Yang Lin Zhang Siyu Shao Gangying Bian |
| author_facet | Jianye Zhou Xinyu Yang Lin Zhang Siyu Shao Gangying Bian |
| author_sort | Jianye Zhou |
| collection | DOAJ |
| description | To realize high-precision and high-efficiency machine fault diagnosis, a novel deep learning framework that combines transfer learning and transposed convolution is proposed. Compared with existing methods, this method has faster training speed, fewer training samples per time, and higher accuracy. First, the raw data collected by multiple sensors are combined into a graph and normalized to facilitate model training. Next, the transposed convolution is utilized to expand the image resolution, and then the images are treated as the input of the transfer learning model for training and fine-tuning. The proposed method adopts 512 time series to conduct experiments on two main mechanical datasets of bearings and gears in the variable-speed gearbox, which verifies the effectiveness and versatility of the method. We have obtained advanced results on both datasets of the gearbox dataset. The dataset shows that the test accuracy is 99.99%, achieving a significant improvement from 98.07% to 99.99%. |
| format | Article |
| id | doaj-art-4c8d4868d0fe4da49a711e0dae204afb |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-4c8d4868d0fe4da49a711e0dae204afb2025-02-03T06:05:41ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/88633888863388Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer LearningJianye Zhou0Xinyu Yang1Lin Zhang2Siyu Shao3Gangying Bian4Graduate School of Air Force Engineering University, Xi’an 710051, ChinaAir and Missile Defence College of Air Force Engineering University, Xi’an 710051, ChinaAir and Missile Defence College of Air Force Engineering University, Xi’an 710051, ChinaAir and Missile Defence College of Air Force Engineering University, Xi’an 710051, ChinaAir and Missile Defence College of Air Force Engineering University, Xi’an 710051, ChinaTo realize high-precision and high-efficiency machine fault diagnosis, a novel deep learning framework that combines transfer learning and transposed convolution is proposed. Compared with existing methods, this method has faster training speed, fewer training samples per time, and higher accuracy. First, the raw data collected by multiple sensors are combined into a graph and normalized to facilitate model training. Next, the transposed convolution is utilized to expand the image resolution, and then the images are treated as the input of the transfer learning model for training and fine-tuning. The proposed method adopts 512 time series to conduct experiments on two main mechanical datasets of bearings and gears in the variable-speed gearbox, which verifies the effectiveness and versatility of the method. We have obtained advanced results on both datasets of the gearbox dataset. The dataset shows that the test accuracy is 99.99%, achieving a significant improvement from 98.07% to 99.99%.http://dx.doi.org/10.1155/2020/8863388 |
| spellingShingle | Jianye Zhou Xinyu Yang Lin Zhang Siyu Shao Gangying Bian Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning Shock and Vibration |
| title | Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning |
| title_full | Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning |
| title_fullStr | Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning |
| title_full_unstemmed | Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning |
| title_short | Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning |
| title_sort | multisignal vgg19 network with transposed convolution for rotating machinery fault diagnosis based on deep transfer learning |
| url | http://dx.doi.org/10.1155/2020/8863388 |
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