Optimizing Wind Turbine Reliability through a Machine Learning Predictive Maintenance Framework

The reliability and efficiency of wind turbines are crucial for maximizing energy production and ensuring the economic viability of wind farms. This study introduces a machine learning-based predictive maintenance pipeline designed to monitor and forecast critical faults within wind turbine systems. By leveraging Supervisory Control and Data Acquisition (SCADA) systems and comprehensive event log datasets, the pipeline incorporates advanced feature engineering techniques, along with Mutual Information and Lasso Regression, to identify and select the most relevant operational parameters. Four sophisticated classifiers LightGBM, XGBoost, Random Forest, and Multilayer Perceptron (MLP) are employed and optimized using Genetic Algorithms (GAs), ensuring high predictive accuracy and robustness. The models address five different fault types, including generator overheating, gearbox oil issues, and fan failures. Compared to a majority-class baseline, the optimized models achieved macro-averaged accuracy exceeding 97 percent and $\mathbf{F 1}$-scores ranging from $\mathbf{8 5 \%}$ to $\mathbf{9 7 \%}$ across the five fault types, demonstrating their capability to capture complex, nonlinear relationships inherent in SCADA data. The multi-class, time-aware classification framework categorizes faults into distinct time intervals ($0-24$ hours, 24-48 hours, 2-7 days, 1-4 weeks, and over 1 month), providing actionable time-to-failure predictions. By accurately predicting faults, the system facilitates proactive maintenance interventions, optimizing maintenance scheduling and resource allocation, and extending the lifespan of critical turbine components. This shift from reactive to proactive maintenance not only maximizes energy production but also enhances asset reliability and operational cost efficiency.