Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working Conditions
Planetary gearbox is a critical component for many mechanical systems. It is essential to monitor the planetary gearbox health and performance in order to maintain the whole machine works well. The methodology of mechanical fault diagnosis is increasingly intelligent with the extensive application o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/6/1685 |
| _version_ | 1798038142646747136 |
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| author | Weigang Wen Yihao Bai Weidong Cheng |
| author_facet | Weigang Wen Yihao Bai Weidong Cheng |
| author_sort | Weigang Wen |
| collection | DOAJ |
| description | Planetary gearbox is a critical component for many mechanical systems. It is essential to monitor the planetary gearbox health and performance in order to maintain the whole machine works well. The methodology of mechanical fault diagnosis is increasingly intelligent with the extensive application of deep learning. However, the cross-domain issue caused by varying working conditions becomes an enormous encumbrance to fault diagnosis based on deep learning. In this paper, in order to fully excavate potentialities of deep neural network architectures, a novel generative adversarial learning method was introduced for a completely new fault diagnosis based on a deep convolution neural network. In addition, the intelligent fault diagnostic scheme for planetary gearbox under varying speed conditions was developed. After that, some experiments on measured vibration signals of planetary gearbox were conducted to verify the validity and efficiency of the fault diagnostic scheme. The results showed that the proposed method enhanced the capability of the intelligent diagnosis for planetary gear faults under varying speed conditions. |
| first_indexed | 2024-04-11T21:36:12Z |
| format | Article |
| id | doaj.art-d415269743294ef0b7e76a24405ca6ef |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:36:12Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d415269743294ef0b7e76a24405ca6ef2022-12-22T04:01:45ZengMDPI AGSensors1424-82202020-03-01206168510.3390/s20061685s20061685Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working ConditionsWeigang Wen0Yihao Bai1Weidong Cheng2School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaPlanetary gearbox is a critical component for many mechanical systems. It is essential to monitor the planetary gearbox health and performance in order to maintain the whole machine works well. The methodology of mechanical fault diagnosis is increasingly intelligent with the extensive application of deep learning. However, the cross-domain issue caused by varying working conditions becomes an enormous encumbrance to fault diagnosis based on deep learning. In this paper, in order to fully excavate potentialities of deep neural network architectures, a novel generative adversarial learning method was introduced for a completely new fault diagnosis based on a deep convolution neural network. In addition, the intelligent fault diagnostic scheme for planetary gearbox under varying speed conditions was developed. After that, some experiments on measured vibration signals of planetary gearbox were conducted to verify the validity and efficiency of the fault diagnostic scheme. The results showed that the proposed method enhanced the capability of the intelligent diagnosis for planetary gear faults under varying speed conditions.https://www.mdpi.com/1424-8220/20/6/1685planetary gearboxcross-domainintelligent fault diagnosisgenerative adversarial learningvarying working conditions |
| spellingShingle | Weigang Wen Yihao Bai Weidong Cheng Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working Conditions Sensors planetary gearbox cross-domain intelligent fault diagnosis generative adversarial learning varying working conditions |
| title | Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working Conditions |
| title_full | Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working Conditions |
| title_fullStr | Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working Conditions |
| title_full_unstemmed | Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working Conditions |
| title_short | Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working Conditions |
| title_sort | generative adversarial learning enhanced fault diagnosis for planetary gearbox under varying working conditions |
| topic | planetary gearbox cross-domain intelligent fault diagnosis generative adversarial learning varying working conditions |
| url | https://www.mdpi.com/1424-8220/20/6/1685 |
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