Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine
In the monitoring process of wind turbines the utmost attention should be given to gearboxes. This conclusion is derived from numerous summary papers. They reveal that, on the one hand, gearboxes are one of the most fault susceptible elements in the drive-train and, on the other, the most expensive...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/4086324 |
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| _version_ | 1832550283025055744 |
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| author | Marcin Strączkiewicz Tomasz Barszcz |
| author_facet | Marcin Strączkiewicz Tomasz Barszcz |
| author_sort | Marcin Strączkiewicz |
| collection | DOAJ |
| description | In the monitoring process of wind turbines the utmost attention should be given to gearboxes. This conclusion is derived from numerous summary papers. They reveal that, on the one hand, gearboxes are one of the most fault susceptible elements in the drive-train and, on the other, the most expensive to replace. Although state-of-the-art CMS can usually provide advanced signal processing tools for extraction of diagnostic information, there are still many installations, where the diagnosis is based simply on the averaged wideband features like root-mean-square (RMS) or peak-peak (PP). Furthermore, for machinery working in highly changing operational conditions, like wind turbines, those estimators are strongly fluctuating, and this fluctuation is not linearly correlated to operation parameters. Thus, the sudden increase of a particular feature does not necessarily have to indicate the development of fault. To overcome this obstacle, it is proposed to detect a fault development with Artificial Neural Network (ANN) and further observation of linear regression parameters calculated on the estimation error between healthy and unknown condition. The proposed reasoning is presented on the real life example of ring gear fault in wind turbine’s planetary gearbox. |
| format | Article |
| id | doaj-art-7499463082c34afd8a5a717f4657406c |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-7499463082c34afd8a5a717f4657406c2025-02-03T06:07:15ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/40863244086324Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind TurbineMarcin Strączkiewicz0Tomasz Barszcz1Department of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30 Mickiewicza Avenue, 30-059 Krakow, PolandDepartment of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30 Mickiewicza Avenue, 30-059 Krakow, PolandIn the monitoring process of wind turbines the utmost attention should be given to gearboxes. This conclusion is derived from numerous summary papers. They reveal that, on the one hand, gearboxes are one of the most fault susceptible elements in the drive-train and, on the other, the most expensive to replace. Although state-of-the-art CMS can usually provide advanced signal processing tools for extraction of diagnostic information, there are still many installations, where the diagnosis is based simply on the averaged wideband features like root-mean-square (RMS) or peak-peak (PP). Furthermore, for machinery working in highly changing operational conditions, like wind turbines, those estimators are strongly fluctuating, and this fluctuation is not linearly correlated to operation parameters. Thus, the sudden increase of a particular feature does not necessarily have to indicate the development of fault. To overcome this obstacle, it is proposed to detect a fault development with Artificial Neural Network (ANN) and further observation of linear regression parameters calculated on the estimation error between healthy and unknown condition. The proposed reasoning is presented on the real life example of ring gear fault in wind turbine’s planetary gearbox.http://dx.doi.org/10.1155/2016/4086324 |
| spellingShingle | Marcin Strączkiewicz Tomasz Barszcz Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine Shock and Vibration |
| title | Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine |
| title_full | Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine |
| title_fullStr | Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine |
| title_full_unstemmed | Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine |
| title_short | Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine |
| title_sort | application of artificial neural network for damage detection in planetary gearbox of wind turbine |
| url | http://dx.doi.org/10.1155/2016/4086324 |
| work_keys_str_mv | AT marcinstraczkiewicz applicationofartificialneuralnetworkfordamagedetectioninplanetarygearboxofwindturbine AT tomaszbarszcz applicationofartificialneuralnetworkfordamagedetectioninplanetarygearboxofwindturbine |