A Long-Time Series Forecast Method for Wind Turbine Blade Strain with Incremental Bi-LSTM Learning
This article presents a novel incremental forecast method to address the challenges in long-time strain status prediction for a wind turbine blade (WTB) under wind loading. Taking strain as the key indicator of structural health, a mathematical model is established to characterize the long-time seri...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/3898 |
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| author | Bingkai Wang Wenlei Sun Hongwei Wang |
| author_facet | Bingkai Wang Wenlei Sun Hongwei Wang |
| author_sort | Bingkai Wang |
| collection | DOAJ |
| description | This article presents a novel incremental forecast method to address the challenges in long-time strain status prediction for a wind turbine blade (WTB) under wind loading. Taking strain as the key indicator of structural health, a mathematical model is established to characterize the long-time series forecast forecasting process. Based on the Bi-directional Long Short-Term Memory (Bi-LSTM) framework, the proposed method incorporates incremental learning via an error-supervised feedback mechanism, enabling the dynamic self-updating of the model parameters. The experience replay and elastic weight consolidation are integrated to further enhance the prediction accuracy. Ultimately, the experimental results demonstrate that the proposed incremental forecast method achieves a 24% and 4.6% improvement in accuracy over the Bi-LSTM and Transformer, respectively. This research not only provides an effective solution for long-time prediction of WTB health but also offers a novel technical framework and theoretical foundation for long-time series forecasting. |
| format | Article |
| id | doaj-art-648e0149e51e428cbeb39b79491c21b3 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-648e0149e51e428cbeb39b79491c21b32025-08-20T03:28:59ZengMDPI AGSensors1424-82202025-06-012513389810.3390/s25133898A Long-Time Series Forecast Method for Wind Turbine Blade Strain with Incremental Bi-LSTM LearningBingkai Wang0Wenlei Sun1Hongwei Wang2School of Mechanical Engineering, Xinjiang University, Urumqi 830047, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830047, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830047, ChinaThis article presents a novel incremental forecast method to address the challenges in long-time strain status prediction for a wind turbine blade (WTB) under wind loading. Taking strain as the key indicator of structural health, a mathematical model is established to characterize the long-time series forecast forecasting process. Based on the Bi-directional Long Short-Term Memory (Bi-LSTM) framework, the proposed method incorporates incremental learning via an error-supervised feedback mechanism, enabling the dynamic self-updating of the model parameters. The experience replay and elastic weight consolidation are integrated to further enhance the prediction accuracy. Ultimately, the experimental results demonstrate that the proposed incremental forecast method achieves a 24% and 4.6% improvement in accuracy over the Bi-LSTM and Transformer, respectively. This research not only provides an effective solution for long-time prediction of WTB health but also offers a novel technical framework and theoretical foundation for long-time series forecasting.https://www.mdpi.com/1424-8220/25/13/3898Bi-LSTMincremental learninglong-time series forecaststrainwind turbine blade |
| spellingShingle | Bingkai Wang Wenlei Sun Hongwei Wang A Long-Time Series Forecast Method for Wind Turbine Blade Strain with Incremental Bi-LSTM Learning Sensors Bi-LSTM incremental learning long-time series forecast strain wind turbine blade |
| title | A Long-Time Series Forecast Method for Wind Turbine Blade Strain with Incremental Bi-LSTM Learning |
| title_full | A Long-Time Series Forecast Method for Wind Turbine Blade Strain with Incremental Bi-LSTM Learning |
| title_fullStr | A Long-Time Series Forecast Method for Wind Turbine Blade Strain with Incremental Bi-LSTM Learning |
| title_full_unstemmed | A Long-Time Series Forecast Method for Wind Turbine Blade Strain with Incremental Bi-LSTM Learning |
| title_short | A Long-Time Series Forecast Method for Wind Turbine Blade Strain with Incremental Bi-LSTM Learning |
| title_sort | long time series forecast method for wind turbine blade strain with incremental bi lstm learning |
| topic | Bi-LSTM incremental learning long-time series forecast strain wind turbine blade |
| url | https://www.mdpi.com/1424-8220/25/13/3898 |
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