A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals
In real engineering scenarios, it is difficult to collect adequate cases with faulty conditions to train an intelligent diagnosis system. To alleviate the problem of limited fault data, this paper proposes a fault diagnosis method combining a generative adversarial network (GAN) and stacked denoisin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/17/5765 |
| _version_ | 1797556800584679424 |
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| author | Qiang Fu Huawei Wang |
| author_facet | Qiang Fu Huawei Wang |
| author_sort | Qiang Fu |
| collection | DOAJ |
| description | In real engineering scenarios, it is difficult to collect adequate cases with faulty conditions to train an intelligent diagnosis system. To alleviate the problem of limited fault data, this paper proposes a fault diagnosis method combining a generative adversarial network (GAN) and stacked denoising auto-encoder (SDAE). The GAN approach augments the limited real measured data, especially in faulty conditions. The generated data are then transformed into the SDAE fault diagnosis model. The GAN-SDAE approach improves the accuracy of the fault diagnosis from the vibration signals, especially when the measured samples are few. The usefulness of this method is assessed through two condition-monitoring cases: one is a classic bearing example and the other is a more general gear failure. The results demonstrate that diagnosis accuracy for both cases is above 90% for various working conditions, and the GAN-SDAE system is stable. |
| first_indexed | 2024-03-10T17:08:06Z |
| format | Article |
| id | doaj.art-a92ee94e96b5443c8bb823abeae5f494 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T17:08:06Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a92ee94e96b5443c8bb823abeae5f4942023-11-20T10:45:40ZengMDPI AGApplied Sciences2076-34172020-08-011017576510.3390/app10175765A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration SignalsQiang Fu0Huawei Wang1College of Civil Aviation Nanjing, University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Civil Aviation Nanjing, University of Aeronautics and Astronautics, Nanjing 211106, ChinaIn real engineering scenarios, it is difficult to collect adequate cases with faulty conditions to train an intelligent diagnosis system. To alleviate the problem of limited fault data, this paper proposes a fault diagnosis method combining a generative adversarial network (GAN) and stacked denoising auto-encoder (SDAE). The GAN approach augments the limited real measured data, especially in faulty conditions. The generated data are then transformed into the SDAE fault diagnosis model. The GAN-SDAE approach improves the accuracy of the fault diagnosis from the vibration signals, especially when the measured samples are few. The usefulness of this method is assessed through two condition-monitoring cases: one is a classic bearing example and the other is a more general gear failure. The results demonstrate that diagnosis accuracy for both cases is above 90% for various working conditions, and the GAN-SDAE system is stable.https://www.mdpi.com/2076-3417/10/17/5765vibration signalsfault diagnosisgenerative adversarial networksstacked denoising auto-encoderdata augmentation |
| spellingShingle | Qiang Fu Huawei Wang A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals Applied Sciences vibration signals fault diagnosis generative adversarial networks stacked denoising auto-encoder data augmentation |
| title | A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals |
| title_full | A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals |
| title_fullStr | A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals |
| title_full_unstemmed | A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals |
| title_short | A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals |
| title_sort | novel deep learning system with data augmentation for machine fault diagnosis from vibration signals |
| topic | vibration signals fault diagnosis generative adversarial networks stacked denoising auto-encoder data augmentation |
| url | https://www.mdpi.com/2076-3417/10/17/5765 |
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