Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG
The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different op...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/11/1618 |
| _version_ | 1827646633176727552 |
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| author | Dongyue Huo Yuyun Kang Baiyang Wang Guifang Feng Jiawei Zhang Hongrui Zhang |
| author_facet | Dongyue Huo Yuyun Kang Baiyang Wang Guifang Feng Jiawei Zhang Hongrui Zhang |
| author_sort | Dongyue Huo |
| collection | DOAJ |
| description | The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets. |
| first_indexed | 2024-03-09T19:05:17Z |
| format | Article |
| id | doaj.art-4288f71334a64f439176338be8c6e9e3 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T19:05:17Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-4288f71334a64f439176338be8c6e9e32023-11-24T04:37:11ZengMDPI AGEntropy1099-43002022-11-012411161810.3390/e24111618Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGGDongyue Huo0Yuyun Kang1Baiyang Wang2Guifang Feng3Jiawei Zhang4Hongrui Zhang5School of Information Science and Engineering, Linyi University, Linyi 276000, ChinaSchool of Logistics, Linyi University, Linyi 276000, ChinaSchool of Information Science and Engineering, Linyi University, Linyi 276000, ChinaSchool of Life Science, Linyi University, Linyi 276000, ChinaLinyi Trade Logistics Science and Technology Industry Research Institute, Linyi 276000, ChinaSchool of Mechanical and Vehicle Engineering, Linyi University, Linyi 276000, ChinaThe gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.https://www.mdpi.com/1099-4300/24/11/1618gear fault diagnosismulti-sensor information fusionVGG |
| spellingShingle | Dongyue Huo Yuyun Kang Baiyang Wang Guifang Feng Jiawei Zhang Hongrui Zhang Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG Entropy gear fault diagnosis multi-sensor information fusion VGG |
| title | Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG |
| title_full | Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG |
| title_fullStr | Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG |
| title_full_unstemmed | Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG |
| title_short | Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG |
| title_sort | gear fault diagnosis method based on multi sensor information fusion and vgg |
| topic | gear fault diagnosis multi-sensor information fusion VGG |
| url | https://www.mdpi.com/1099-4300/24/11/1618 |
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