An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors
Abstract Bolt looseness may occur on wind turbine (WT) blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-04-01
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| Series: | Wind Energy |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/we.2892 |
| _version_ | 1827317588811579392 |
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| author | Feng Gao Chenkai Qian Lin Xu Juncheng Liu Hong Zhang |
| author_facet | Feng Gao Chenkai Qian Lin Xu Juncheng Liu Hong Zhang |
| author_sort | Feng Gao |
| collection | DOAJ |
| description | Abstract Bolt looseness may occur on wind turbine (WT) blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this paper proposes a method to monitor the looseness of blade root bolts using the sensors installed on the WT blade. An experimental platform was first built by installing acceleration and strain sensors for monitoring bolt looseness. Through the physical experiment of blade root bolts' looseness, the response data of blade sensors is then obtained under different bolt looseness numbers and degrees. Afterwards, the sensor signal of the blade root bolts is analyzed in time domain, frequency domain, and time‐frequency domain, and the sensitivity features of various signals are extracted. So the eigenvalue category as the input of the state discrimination model was determined. The LightGBM (light gradient boosting machine) classification algorithm was applied to identify different bolt looseness states for the multi‐domain features. The impact of different combinations of sensor categories and quantities as the data source on the identification results is discussed, and a reference for the selection of sensors is provided. The proposed method can discriminate four bolt states at an accuracy of around 99.8% using 5‐fold cross‐validation. |
| first_indexed | 2024-04-24T23:41:25Z |
| format | Article |
| id | doaj.art-91175b61ed6a4a798efd84596ad3f1e2 |
| institution | Directory Open Access Journal |
| issn | 1095-4244 1099-1824 |
| language | English |
| last_indexed | 2024-04-24T23:41:25Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Wind Energy |
| spelling | doaj.art-91175b61ed6a4a798efd84596ad3f1e22024-03-15T12:14:31ZengWileyWind Energy1095-42441099-18242024-04-0127436338110.1002/we.2892An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensorsFeng Gao0Chenkai Qian1Lin Xu2Juncheng Liu3Hong Zhang4North China Electric Power University Beijing ChinaNorth China Electric Power University Beijing ChinaWindey Energy Technology Group Co., Ltd. Hangzhou ChinaNorth China Electric Power University Beijing ChinaNorth China Electric Power University Beijing ChinaAbstract Bolt looseness may occur on wind turbine (WT) blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this paper proposes a method to monitor the looseness of blade root bolts using the sensors installed on the WT blade. An experimental platform was first built by installing acceleration and strain sensors for monitoring bolt looseness. Through the physical experiment of blade root bolts' looseness, the response data of blade sensors is then obtained under different bolt looseness numbers and degrees. Afterwards, the sensor signal of the blade root bolts is analyzed in time domain, frequency domain, and time‐frequency domain, and the sensitivity features of various signals are extracted. So the eigenvalue category as the input of the state discrimination model was determined. The LightGBM (light gradient boosting machine) classification algorithm was applied to identify different bolt looseness states for the multi‐domain features. The impact of different combinations of sensor categories and quantities as the data source on the identification results is discussed, and a reference for the selection of sensors is provided. The proposed method can discriminate four bolt states at an accuracy of around 99.8% using 5‐fold cross‐validation.https://doi.org/10.1002/we.2892blade sensorsbolt loosenessLightGBMmulti‐domain feature fusionwind turbine blade |
| spellingShingle | Feng Gao Chenkai Qian Lin Xu Juncheng Liu Hong Zhang An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors Wind Energy blade sensors bolt looseness LightGBM multi‐domain feature fusion wind turbine blade |
| title | An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors |
| title_full | An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors |
| title_fullStr | An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors |
| title_full_unstemmed | An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors |
| title_short | An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors |
| title_sort | experimental study on the identification of the root bolts state of wind turbine blades using blade sensors |
| topic | blade sensors bolt looseness LightGBM multi‐domain feature fusion wind turbine blade |
| url | https://doi.org/10.1002/we.2892 |
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