A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things
The wind turbine drive system is one of the key components in converting wind energy into electrical energy. The life prediction of drive system is very important for the maintenance of wind turbine. With increasing capacity, the wind turbine system has become more complicated. Consequently, for the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/9048508 |
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| author | Hang Zhou Shi-Jun Yi Ya-Fei Liu Yong-Quan Hu Yong Xiang |
| author_facet | Hang Zhou Shi-Jun Yi Ya-Fei Liu Yong-Quan Hu Yong Xiang |
| author_sort | Hang Zhou |
| collection | DOAJ |
| description | The wind turbine drive system is one of the key components in converting wind energy into electrical energy. The life prediction of drive system is very important for the maintenance of wind turbine. With increasing capacity, the wind turbine system has become more complicated. Consequently, for the life prediction of drive system, it is necessary to consider the problems of multi-information fusion of big data, quantification of time-varying dynamic loads, and analysis of multiple-damage coupling. In order to solve the above challenges, the fatigue life analysis and evaluation method considering the interaction of coupled multiple damages are proposed in this study. The hierarchical Bayesian theory with fault physics technology is introduced to deal with the uncertainty of wind turbine drive system. Then, a time-varying performance analysis model is established based on the multiple-damage coupling competition failure mechanism. Moreover, the Internet of Things (IoT) technology is introduced and combined with the proposed model. Through the data collection by IoT, the time-stress curve of drive system can be obtained. A case study about the remaining fatigue life estimation of drive system is utilized to illustrate the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-19b69757fca9404fbd2701da6fbb8e73 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-19b69757fca9404fbd2701da6fbb8e732025-02-03T01:01:53ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422020-01-01202010.1155/2020/90485089048508A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of ThingsHang Zhou0Shi-Jun Yi1Ya-Fei Liu2Yong-Quan Hu3Yong Xiang4School of Computer Engineering, Chengdu Technological University, Chengdu 611731, ChinaSchool of Computer Engineering, Chengdu Technological University, Chengdu 611731, ChinaSchool of Computer Engineering, Chengdu Technological University, Chengdu 611731, ChinaSchool of Computer Engineering, Chengdu Technological University, Chengdu 611731, ChinaSchool of Computer Engineering, Chengdu Technological University, Chengdu 611731, ChinaThe wind turbine drive system is one of the key components in converting wind energy into electrical energy. The life prediction of drive system is very important for the maintenance of wind turbine. With increasing capacity, the wind turbine system has become more complicated. Consequently, for the life prediction of drive system, it is necessary to consider the problems of multi-information fusion of big data, quantification of time-varying dynamic loads, and analysis of multiple-damage coupling. In order to solve the above challenges, the fatigue life analysis and evaluation method considering the interaction of coupled multiple damages are proposed in this study. The hierarchical Bayesian theory with fault physics technology is introduced to deal with the uncertainty of wind turbine drive system. Then, a time-varying performance analysis model is established based on the multiple-damage coupling competition failure mechanism. Moreover, the Internet of Things (IoT) technology is introduced and combined with the proposed model. Through the data collection by IoT, the time-stress curve of drive system can be obtained. A case study about the remaining fatigue life estimation of drive system is utilized to illustrate the effectiveness of the proposed method.http://dx.doi.org/10.1155/2020/9048508 |
| spellingShingle | Hang Zhou Shi-Jun Yi Ya-Fei Liu Yong-Quan Hu Yong Xiang A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things Advances in Materials Science and Engineering |
| title | A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things |
| title_full | A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things |
| title_fullStr | A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things |
| title_full_unstemmed | A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things |
| title_short | A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things |
| title_sort | fatigue life prediction method for the drive system of wind turbine using internet of things |
| url | http://dx.doi.org/10.1155/2020/9048508 |
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