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Multisensory collaborative damage diagnosis of a 10 MW floating offshore wind turbine tendons using multi-scale convolutional neural network with attention mechanism

Author

Listed:
  • Xu, Zifei
  • Bashir, Musa
  • Yang, Yang
  • Wang, Xinyu
  • Wang, Jin
  • Ekere, Nduka
  • Li, Chun

Abstract

An effective damage diagnosis and prognostic management method can considerably reduce operation and maintenance costs of floating wind turbines. In this research, an intelligent damage diagnosis framework, named “MS-ACNN”, has been developed using a multi-scale deep convolution neural network model fused with an attention mechanism. The framework is used to detect, localize, and quantify existing and potential damages on multibody floating wind turbine tendons. The MS-ACNN framework is fitted with two multi-scale extractors, designed to capture multi-scale information from raw wind turbine response signals measured using multi-sensor. The attention mechanism uses weight ratios of extracted damage feature to enhance the MS-ACNN's capability in offering a better generalization in damage diagnosis. The framework's performance is examined under normal and noisy environments and with a diagnosis accuracy of 80%, which is higher than those obtained using most generic industrial grade diagnostic tools (MS–CNN–I, MSCNN-II, CNN, CNN-LSTM and CNN-BiLSTM) by at least 10%. The framework is also fitted with a Majority Weighted Voting rule to reduce false alarms and ensure optimum performance of the multi-sensor during collaborative diagnosis. Further examination shows that the inclusion of a voting rule increases the diagnostic performance's F1 index from 90% for single sensor and 84% for multi-sensor results to 94%.

Suggested Citation

  • Xu, Zifei & Bashir, Musa & Yang, Yang & Wang, Xinyu & Wang, Jin & Ekere, Nduka & Li, Chun, 2022. "Multisensory collaborative damage diagnosis of a 10 MW floating offshore wind turbine tendons using multi-scale convolutional neural network with attention mechanism," Renewable Energy, Elsevier, vol. 199(C), pages 21-34.
    • Handle: RePEc:eee:renene:v:199:y:2022:i:c:p:21-34
    DOI: 10.1016/j.renene.2022.08.093
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    References listed on IDEAS

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    1. Choe, Do-Eun & Kim, Hyoung-Chul & Kim, Moo-Hyun, 2021. "Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades," Renewable Energy, Elsevier, vol. 174(C), pages 218-235.
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    Cited by:

    1. Yan, Jie & Nuertayi, Akejiang & Yan, Yamin & Liu, Shan & Liu, Yongqian, 2023. "Hybrid physical and data driven modeling for dynamic operation characteristic simulation of wind turbine," Renewable Energy, Elsevier, vol. 215(C).
    2. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.

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