Self-Supervised Condition Monitoring for Wind Turbine Gearboxes Based on Adaptive Feature Selection and Contrastive Residual Graph Neural Network

Frequent failures in wind turbines underscore the critical need for accurate and efficient online monitoring and early warning systems to detect abnormal conditions. Given the complexity of monitoring numerous components individually, subsystem-level monitoring emerges as a practical and effective alternative. Among all subsystems, the gearbox is particularly critical due to its high failure rate and prolonged downtime. However, achieving both efficiency and accuracy in gearbox condition monitoring remains a significant challenge. To tackle this issue, we present a novel adaptive condition monitoring method specifically for wind turbine gearbox. The approach begins with adaptive feature selection based on correlation analysis, through which a quantitative indicator is defined. With the utilization the selected features, graph-based data representations are constructed, and a self-supervised contrastive residual graph neural network is developed for effective data mining. For online monitoring, a health index is derived using distance metrics in a multidimensional feature space, and statistical process control is employed to determine failure thresholds. This framework enables real-time condition tracking and early warning of potential faults. Validation using SCADA data and maintenance records from two wind farms demonstrates that the proposed method can issue early warnings of abnormalities 30 to 40 h in advance, with anomaly detection accuracy and F1 score both exceeding 90%. This highlights its effectiveness, practicality, and strong potential for real-world deployment in wind turbine monitoring applications.