Fault diagnosis of mechanical seals using graph neural networks with multi-sensor data fusion
Mechanical seals are critical components in the mechanical industry, and their operational status directly impacts the performance of pumps, compressors, and other machinery. Therefore, conducting research on the fault diagnosis of mechanical seals is essential. To enhance the accuracy of the assess...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-02-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132251319141 |
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| _version_ | 1850025802255564800 |
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| author | Xiaoran Zhu Jiahao Wang Binhui Wang Hao Wang Ren Sheng Baozun Zhai |
| author_facet | Xiaoran Zhu Jiahao Wang Binhui Wang Hao Wang Ren Sheng Baozun Zhai |
| author_sort | Xiaoran Zhu |
| collection | DOAJ |
| description | Mechanical seals are critical components in the mechanical industry, and their operational status directly impacts the performance of pumps, compressors, and other machinery. Therefore, conducting research on the fault diagnosis of mechanical seals is essential. To enhance the accuracy of the assessment model, we propose an integrated approach that leverages the fusion of multiple graph neural networks (GNNs). Firstly, recognizing the diversity among different sensors, we utilize multi-channel data to comprehensively represent the operational state of the mechanical seal. These channels include various types of sensors such as acoustic emission and force sensors. Secondly, we employ multiple methods to transform the original multi-channel data into graph data, thereby continuously increasing the diversity of the datasets used for training. Finally, after training GNNs, we output the data of these networks through data fusion to obtain evaluation results. The effectiveness of our assessment approach is demonstrated using mechanical seal test data. |
| format | Article |
| id | doaj-art-243198a702da4e90bf9005db0bad8359 |
| institution | DOAJ |
| issn | 1687-8140 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj-art-243198a702da4e90bf9005db0bad83592025-08-20T03:00:44ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402025-02-011710.1177/16878132251319141Fault diagnosis of mechanical seals using graph neural networks with multi-sensor data fusionXiaoran Zhu0Jiahao Wang1Binhui Wang2Hao Wang3Ren Sheng4Baozun Zhai5School of Mechanical Engineering, Yellow River Conservancy Technical Institute, Kaifeng, Henan, ChinaSchool of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan, ChinaSchool of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan, ChinaSchool of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan, ChinaSchool of Mechanical Engineering, Yellow River Conservancy Technical Institute, Kaifeng, Henan, ChinaSchool of Mechanical Engineering, Yellow River Conservancy Technical Institute, Kaifeng, Henan, ChinaMechanical seals are critical components in the mechanical industry, and their operational status directly impacts the performance of pumps, compressors, and other machinery. Therefore, conducting research on the fault diagnosis of mechanical seals is essential. To enhance the accuracy of the assessment model, we propose an integrated approach that leverages the fusion of multiple graph neural networks (GNNs). Firstly, recognizing the diversity among different sensors, we utilize multi-channel data to comprehensively represent the operational state of the mechanical seal. These channels include various types of sensors such as acoustic emission and force sensors. Secondly, we employ multiple methods to transform the original multi-channel data into graph data, thereby continuously increasing the diversity of the datasets used for training. Finally, after training GNNs, we output the data of these networks through data fusion to obtain evaluation results. The effectiveness of our assessment approach is demonstrated using mechanical seal test data.https://doi.org/10.1177/16878132251319141 |
| spellingShingle | Xiaoran Zhu Jiahao Wang Binhui Wang Hao Wang Ren Sheng Baozun Zhai Fault diagnosis of mechanical seals using graph neural networks with multi-sensor data fusion Advances in Mechanical Engineering |
| title | Fault diagnosis of mechanical seals using graph neural networks with multi-sensor data fusion |
| title_full | Fault diagnosis of mechanical seals using graph neural networks with multi-sensor data fusion |
| title_fullStr | Fault diagnosis of mechanical seals using graph neural networks with multi-sensor data fusion |
| title_full_unstemmed | Fault diagnosis of mechanical seals using graph neural networks with multi-sensor data fusion |
| title_short | Fault diagnosis of mechanical seals using graph neural networks with multi-sensor data fusion |
| title_sort | fault diagnosis of mechanical seals using graph neural networks with multi sensor data fusion |
| url | https://doi.org/10.1177/16878132251319141 |
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