Deep Learning-Based Imputation of Multi-Source Missing Data for Offshore Wind Turbines

LI Dong; LIAO Yizhen; SANG Yuan; LI Zeyu; YAO Bo; CHEN Hongbing

doi:10.3724/j.gyjzG26031308

Volume 56 Issue 5

May 2026

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2026 > 56(5): 176-186

LI Dong, LIAO Yizhen, SANG Yuan, LI Zeyu, YAO Bo, CHEN Hongbing. 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

Citation:

LI Dong, LIAO Yizhen, SANG Yuan, LI Zeyu, YAO Bo, CHEN Hongbing. 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

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Deep Learning-Based Imputation of Multi-Source Missing Data for Offshore Wind Turbines

doi: 10.3724/j.gyjzG26031308

1. College of Civil Engineering, Fuzhou University, Fuzhou 350108, China;
2. Future City College/Institute of Urbanization and Urban Safety, University of Science and Technology Beijing, Beijing 100083, China;
3. China MCC5 Group Co., Ltd., Chengdu 476300, China

Received Date: 2026-03-13
Available Online: 2026-06-06
Publish Date: 2026-05-20

Abstract

Abstract

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

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References(22)

References

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