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

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.