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Deep Learning-Based Imputation of Multi-Source Missing Data for Offshore Wind Turbines
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Dong LI1, Yizhen LIAO1, Yuan SANG1, Zeyu LI2, Bo YAO3, Hongbing CHEN2
Industrial Construction | 2026, 56(5) : 176 - 186
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Industrial Construction | 2026, 56(5): 176-186
Deep Learning-Based Imputation of Multi-Source Missing Data for Offshore Wind Turbines
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Dong LI1, Yizhen LIAO1, Yuan SANG1, Zeyu LI2, Bo YAO3, Hongbing CHEN2
Affiliations
  • 1College of Civil Engineering, Fuzhou University, Fuzhou350108, China
  • 2Future City College / Institute of Urbanization and Urban Safety, University of Science and Technology Beijing, Beijing100083, China
  • 3China MCC5 Group Co.,Ltd., Chengdu476300, China
Published: 2026-05-20 doi: 10.3724/j.gyjzG26031308
Outline
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To address the problem of missing multi-source monitoring data of offshore wind turbines caused by sensor failures or communication interruptions under harsh operating conditions, this paper proposes a novel imputation model based on a multi-head gated residual network. This method achieves collaborative fusion of supervisory control and data acquisition (SCADA) data and structural vibration monitoring data through feature concatenation, employs a gated residual network to extract deep nonlinear coupling features, and uses a multi-head parallel output architecture for the independent reconstruction of these two heterogeneous data types. During the training stage, a dynamic masking mechanism combined with a hybrid loss function is adopted to enhance the model’s adaptability to complex aerodynamic operating conditions. Validated with field data from a 10 MW offshore wind turbine, the proposed model achieved a high coefficient of determination under training conditions, enabling accurate reconstruction of missing multi-source data. In generalization tests for non-training periods, although the coefficient of determination of the model’s predictions fluctuated slightly, the model still effectively captured the overall trends of monitoring signals. Notably, the degradation in generalization performance for vibration data was less pronounced than that for SCADA data, demonstrating its greater stability. The proposed method can significantly improve the completeness and reliability of multi-source monitoring data for wind turbines and holds considerable potential for engineering applications.

offshore wind turbines  /  data imputation  /  gated residual network  /  multi-source data fusion
Dong LI, Yizhen LIAO, Yuan SANG, Zeyu LI, Bo YAO, Hongbing CHEN. Deep Learning-Based Imputation of Multi-Source Missing Data for Offshore Wind Turbines[J]. Industrial Construction, 2026 , 56 (5) : 176 -186 . DOI: 10.3724/j.gyjzG26031308
Year 2026 volume 56 Issue 5
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doi: 10.3724/j.gyjzG26031308
  • Receive Date:2026-03-13
  • Online Date:2026-06-25
  • Published:2026-05-20
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  • Received:2026-03-13
Affiliations
    1College of Civil Engineering, Fuzhou University, Fuzhou350108, China
    2Future City College / Institute of Urbanization and Urban Safety, University of Science and Technology Beijing, Beijing100083, China
    3China MCC5 Group Co.,Ltd., Chengdu476300, China
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小菇属 Mycena 11 5.26
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