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Structural Engineering and Mechanics Volume 92, Number 2, October25 2024 , pages 121-131 DOI: https://doi.org/10.12989/sem.2024.92.2.121 |
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 open accessFault detection in blade pitch systems of floating wind turbines utilizing transformer architecture |
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Seongpil Cho, Sang-Woo Kim and Hyo-Jin Kim
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| Abstract | ||
| This paper proposes a fault detection method for blade pitch systems of floating wind turbines using transformerbased deep-learning models. Transformers leverage self-attention mechanisms, efficiently process time-series data, and capture long-term dependencies more effectively than traditional recurrent neural networks (RNNs). The model was trained using normal operational data to detect anomalies through high reconstruction losses when encountering abnormal data. In this study, various fault conditions in a blade pitch system, including environmental load cases, were simulated using a detailed model of a spar-type floating wind turbine, the data collected from these simulations were used to train and test the transformer models. The model demonstrated superior fault-detection capabilities with high accuracy, precision, recall, and F1 scores. The results show that the proposed method successfully identifies faults and achieves high-performance metrics, outperforming existing traditional multi-layer perceptron (MLP) models and long short-term memory-autoencoder (LSTM-AE) models. This study highlights the potential of transformer models for real-time fault detection in wind turbines, contributing to more advanced condition-monitoring systems with minimal human intervention. | ||
| Key Words | ||
| blade pitch system; fault detection; floating wind turbine; prognostics and health management; sequential data; transformer | ||
| Address | ||
| Seongpil Cho, Sang-Woo Kim: Department of Aeronautical and Astronautical Engineering, Korea Aerospace University, 76 Gonghangdaehak-ro, Deokyang-gu, Goyang, Gyeonggi 10540, Republic of Korea Hyo-Jin Kim: Department of Korean Medical Science, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea | ||