Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion
To meet the demands for real-time and accurate fault warning of wind turbine gear transmission systems, this study proposes an innovative intelligent warning method based on the integration of digital twin and multi-source data fusion. A digital twin system architecture is developed, comprising a hi...
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MDPI AG
2025-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8655 |
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| author | Tiantian Xu Xuedong Zhang Wenlei Sun |
| author_facet | Tiantian Xu Xuedong Zhang Wenlei Sun |
| author_sort | Tiantian Xu |
| collection | DOAJ |
| description | To meet the demands for real-time and accurate fault warning of wind turbine gear transmission systems, this study proposes an innovative intelligent warning method based on the integration of digital twin and multi-source data fusion. A digital twin system architecture is developed, comprising a high-precision geometric model and a dynamic mechanism model, enabling real-time interaction and data fusion between the physical transmission system and its virtual model. At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. Experimental validation shows that this model outperforms traditional methods in prediction accuracy. Specifically, it achieves average improvements of 0.3945, 0.546 and 0.061 in Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R<sup>2</sup>) metrics, respectively. Building on the above findings, a monitoring and early warning platform for the wind turbine transmission system was developed, integrating digital twin visualization with intelligent prediction functions. This platform enables a fully intelligent process from data acquisition and status evaluation to fault warning, providing an innovative solution for the predictive maintenance of wind turbines. |
| format | Article |
| id | doaj-art-34cf7892104842de910a8ce23efb6fcb |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-34cf7892104842de910a8ce23efb6fcb2025-08-20T04:00:53ZengMDPI AGApplied Sciences2076-34172025-08-011515865510.3390/app15158655Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data FusionTiantian Xu0Xuedong Zhang1Wenlei Sun2School of Mechanical Engineering, Xinjiang University, Urumqi 830046, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830046, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830046, ChinaTo meet the demands for real-time and accurate fault warning of wind turbine gear transmission systems, this study proposes an innovative intelligent warning method based on the integration of digital twin and multi-source data fusion. A digital twin system architecture is developed, comprising a high-precision geometric model and a dynamic mechanism model, enabling real-time interaction and data fusion between the physical transmission system and its virtual model. At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. Experimental validation shows that this model outperforms traditional methods in prediction accuracy. Specifically, it achieves average improvements of 0.3945, 0.546 and 0.061 in Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R<sup>2</sup>) metrics, respectively. Building on the above findings, a monitoring and early warning platform for the wind turbine transmission system was developed, integrating digital twin visualization with intelligent prediction functions. This platform enables a fully intelligent process from data acquisition and status evaluation to fault warning, providing an innovative solution for the predictive maintenance of wind turbines.https://www.mdpi.com/2076-3417/15/15/8655digital twinmulti-source data fusionwind turbine gear transmission systemfault early warning model |
| spellingShingle | Tiantian Xu Xuedong Zhang Wenlei Sun Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion Applied Sciences digital twin multi-source data fusion wind turbine gear transmission system fault early warning model |
| title | Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion |
| title_full | Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion |
| title_fullStr | Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion |
| title_full_unstemmed | Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion |
| title_short | Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion |
| title_sort | intelligent fault warning method for wind turbine gear transmission system driven by digital twin and multi source data fusion |
| topic | digital twin multi-source data fusion wind turbine gear transmission system fault early warning model |
| url | https://www.mdpi.com/2076-3417/15/15/8655 |
| work_keys_str_mv | AT tiantianxu intelligentfaultwarningmethodforwindturbinegeartransmissionsystemdrivenbydigitaltwinandmultisourcedatafusion AT xuedongzhang intelligentfaultwarningmethodforwindturbinegeartransmissionsystemdrivenbydigitaltwinandmultisourcedatafusion AT wenleisun intelligentfaultwarningmethodforwindturbinegeartransmissionsystemdrivenbydigitaltwinandmultisourcedatafusion |