Improved condition monitoring technique for wind turbine gearbox and shaft stress detection
© The Institution of Engineering and Technology 2017. Condition monitoring has been widely used to detect mechanical and electrical faults of wind turbine in order to avoid any potential catastrophic failures, reduce operational and maintenance cost, and enhance the reliability and availability of t...
| Main Authors: | , , |
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| Format: | Journal Article |
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Institution of Engineering and Technology
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/55873 |
| _version_ | 1848759729168318464 |
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| author | Salem, A. Abu-Siada, Ahmed Islam, S. |
| author_facet | Salem, A. Abu-Siada, Ahmed Islam, S. |
| author_sort | Salem, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © The Institution of Engineering and Technology 2017. Condition monitoring has been widely used to detect mechanical and electrical faults of wind turbine in order to avoid any potential catastrophic failures, reduce operational and maintenance cost, and enhance the reliability and availability of the equipment. Although several papers about wind turbine condition monitoring can be found in the literatures, adopting a reliable and cost-effective technique for wind turbines that usually experience severe mechanical stress due to the harsh weather conditions they are exposed to is still challenging. Although statistical studies show that gearbox failure rate is low, the resulted downtime and the replacement or repairing cost is substantial. This study introduces an improved technique to monitor the condition of the wind turbine gearbox based on gearbox vibration and shaft torque signatures analyses. In this context, a test rig that emulates real wind turbine operation has been developed to analyse the behaviour of wind turbine under various mechanical fault levels. Shaft torque and mechanical vibration signals are detected using high-resolution sensors and are analysed using two signal processing techniques: wavelet and order analyses to examine the impact of investigated fault levels on the wind turbine drive train. Both signal processing techniques are compared based on their sensitivity to detect incipient fault levels. |
| first_indexed | 2025-11-14T10:04:30Z |
| format | Journal Article |
| id | curtin-20.500.11937-55873 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:04:30Z |
| publishDate | 2017 |
| publisher | Institution of Engineering and Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-558732017-09-13T16:10:28Z Improved condition monitoring technique for wind turbine gearbox and shaft stress detection Salem, A. Abu-Siada, Ahmed Islam, S. © The Institution of Engineering and Technology 2017. Condition monitoring has been widely used to detect mechanical and electrical faults of wind turbine in order to avoid any potential catastrophic failures, reduce operational and maintenance cost, and enhance the reliability and availability of the equipment. Although several papers about wind turbine condition monitoring can be found in the literatures, adopting a reliable and cost-effective technique for wind turbines that usually experience severe mechanical stress due to the harsh weather conditions they are exposed to is still challenging. Although statistical studies show that gearbox failure rate is low, the resulted downtime and the replacement or repairing cost is substantial. This study introduces an improved technique to monitor the condition of the wind turbine gearbox based on gearbox vibration and shaft torque signatures analyses. In this context, a test rig that emulates real wind turbine operation has been developed to analyse the behaviour of wind turbine under various mechanical fault levels. Shaft torque and mechanical vibration signals are detected using high-resolution sensors and are analysed using two signal processing techniques: wavelet and order analyses to examine the impact of investigated fault levels on the wind turbine drive train. Both signal processing techniques are compared based on their sensitivity to detect incipient fault levels. 2017 Journal Article http://hdl.handle.net/20.500.11937/55873 10.1049/iet-smt.2016.0338 Institution of Engineering and Technology restricted |
| spellingShingle | Salem, A. Abu-Siada, Ahmed Islam, S. Improved condition monitoring technique for wind turbine gearbox and shaft stress detection |
| title | Improved condition monitoring technique for wind turbine gearbox and shaft stress detection |
| title_full | Improved condition monitoring technique for wind turbine gearbox and shaft stress detection |
| title_fullStr | Improved condition monitoring technique for wind turbine gearbox and shaft stress detection |
| title_full_unstemmed | Improved condition monitoring technique for wind turbine gearbox and shaft stress detection |
| title_short | Improved condition monitoring technique for wind turbine gearbox and shaft stress detection |
| title_sort | improved condition monitoring technique for wind turbine gearbox and shaft stress detection |
| url | http://hdl.handle.net/20.500.11937/55873 |