What question did this study set out to answer?

To develop an integrated condition-monitoring and predictive-maintenance framework that improves fault detection accuracy, lifetime estimation, and repair scheduling for offshore wind turbines.

August 16, 2026Advanced Electromagnetics0 citationsOpen Access

Optimization and Improvement of Operation and Maintenance Efficiency of Condition Monitoring Technology for Offshore Wind Turbines

YOY. L. Ouyang WLW. Liang

Key Points

To develop an integrated condition-monitoring and predictive-maintenance framework that improves fault detection accuracy, lifetime estimation, and repair scheduling for offshore wind turbines.
Designed a multi-source sensing setup capturing vibration, temperature, strain, and operational parameters using variance-weighted fusion and denoising algorithms.
Constructed a hybrid CNN–LSTM neural network paired with a digital-twin model to quantify degradation patterns and estimate remaining useful life.
Integrated health assessment metrics with operational weather windows and maintenance resource constraints for field validation in an offshore wind farm.
Achieved a 96.2% fault identification accuracy in operational offshore field testing.
Predicted the remaining useful life of the turbine main bearing as it degraded from 180 days down to 16 days prior to failure.

Abstract

This study proposes an integrated condition-monitoring and predictive-maintenance framework for offshore wind turbines operating in harsh marine environments. To address the challenges of signal degradation, environmental interference, and limited fault-warning capability, a multi-source sensing architecture is developed based on risk-driven sensor deployment, edge-side signal fusion, and intelligent health assessment. Vibration, temperature, strain, and operational signals are adaptively processed through variance-weighted fusion and denoising strategies to improve data reliability. A CNN– LSTM hybrid model is employed for fault feature extraction and temporal degradation analysis, while a digital-twin-driven health assessment framework is used to quantify health indices and remaining useful life. Maintenance scheduling is further optimized by integrating equipment health conditions, resource constraints, and operational windows. Field validation in an offshore wind farm demonstrates that the proposed diagnostic model achieves a fault identification accuracy of 96.2%, while the predicted remaining useful life of the main bearing decreases from 180 days to 16 days before failure. The proposed framework establishes a closed-loop process linking signal acquisition, intelligent diagnosis, lifetime prediction, and maintenance decision-making, providing an effective engineering solution for reliable condition monitoring and intelligent operation of offshore energy systems.

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