An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery
As the most basic component of rotating machinery, rolling bearing frequently works in harsh environments and complex working conditions, and its health status affects seriously the working efficiency. The health statuses of rolling bearing can not only reduce equipment maintenance costs but also co...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/4648311 |
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| _version_ | 1849306753870594048 |
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| author | Zhen Li Saleem Riaz Muhammad Waqas Munira Batool |
| author_facet | Zhen Li Saleem Riaz Muhammad Waqas Munira Batool |
| author_sort | Zhen Li |
| collection | DOAJ |
| description | As the most basic component of rotating machinery, rolling bearing frequently works in harsh environments and complex working conditions, and its health status affects seriously the working efficiency. The health statuses of rolling bearing can not only reduce equipment maintenance costs but also contribute to reducing major accidents. Based on this, an adaptive diagnosis method that combines deep gated recurrent unit (DGRU) with wavelet packet decomposition (WPD) and extreme learning machine (ELM) is proposed for rolling bearing. Firstly, WPD is utilized to eliminate the noise of data. Secondly, DGRU is designed to extract the representative features of denoised data. Finally, ELM is utilized to output the diagnosis results. Massive results prove that the superiority and robustness of our approach outperform existing popular methods. Additionally, the proposed method can also achieve powerful antinoise ability. |
| format | Article |
| id | doaj-art-23a9e77a9d8b489e8b06daae77ed7fbc |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-23a9e77a9d8b489e8b06daae77ed7fbc2025-08-20T03:54:58ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/4648311An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating MachineryZhen Li0Saleem Riaz1Muhammad Waqas2Munira Batool3Department of Automotive EngineeringSool of AutomationSchool of Electrical EngineeringDepartment of Electrical EngineeringAs the most basic component of rotating machinery, rolling bearing frequently works in harsh environments and complex working conditions, and its health status affects seriously the working efficiency. The health statuses of rolling bearing can not only reduce equipment maintenance costs but also contribute to reducing major accidents. Based on this, an adaptive diagnosis method that combines deep gated recurrent unit (DGRU) with wavelet packet decomposition (WPD) and extreme learning machine (ELM) is proposed for rolling bearing. Firstly, WPD is utilized to eliminate the noise of data. Secondly, DGRU is designed to extract the representative features of denoised data. Finally, ELM is utilized to output the diagnosis results. Massive results prove that the superiority and robustness of our approach outperform existing popular methods. Additionally, the proposed method can also achieve powerful antinoise ability.http://dx.doi.org/10.1155/2022/4648311 |
| spellingShingle | Zhen Li Saleem Riaz Muhammad Waqas Munira Batool An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery Shock and Vibration |
| title | An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery |
| title_full | An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery |
| title_fullStr | An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery |
| title_full_unstemmed | An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery |
| title_short | An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery |
| title_sort | enhanced gated recurrent unit based adaptive fault diagnosis of rotating machinery |
| url | http://dx.doi.org/10.1155/2022/4648311 |
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