ReNewind: A Practical Machine Learning Pipeline for Wind Turbine Fault Prediction

This article introduces ReNewind—a complete predictive maintenance system for wind turbines. By comparing seven classification models (such as XGBoost, Random Forest, etc.) and three class imbalance handling strategies, it ultimately achieves an 89% fault recall rate, providing a reproducible technical solution for equipment operation and maintenance in the renewable energy industry.

Maintenance costs account for a significant proportion of operational expenses in wind power. The traditional reactive maintenance model often leads to unplanned downtime, causing huge economic losses. Predictive maintenance uses machine learning to identify potential faults in advance, which can reduce maintenance costs by more than 30%, improve equipment availability and power generation efficiency. The core challenge is extreme class imbalance: fault samples account for less than 1% of normal operation data, and conventional classification models tend to fall into the trap of "predicting all as normal", missing real faults.

ReNewind builds an end-to-end machine learning pipeline covering data preprocessing, feature engineering, model training, imbalance handling, and performance evaluation. Its modular design facilitates deployment and optimization across wind farms. The core optimization metric is recall rate (prioritizing fault capture). Seven mainstream classification algorithms are compared: Logistic Regression (baseline, interpretable), Random Forest (non-linear interaction), XGBoost (excellent for structured data), SVM (optimal hyperplane in high dimensions), KNN (local pattern recognition), Naive Bayes (efficient), and MLP (complex non-linear mapping). All models undergo hyperparameter tuning and fair comparison.

To address the problem of scarce fault samples, three strategies are compared: 1. Random Undersampling: Reduces majority class samples to balance data, fast training but may lose information from normal samples; works best with XGBoost. 2. SMOTE Oversampling: Synthesizes minority class samples in feature space, retains majority class information but easily generates noise. 3. Class Weight Adjustment: Assigns high weights to minority classes in the loss function; simple to implement but requires empirical tuning.

The optimal solution is XGBoost combined with random undersampling, achieving an 89% fault recall rate on the test set. Key findings: Tree models (XGBoost, Random Forest) are significantly better than linear models; undersampling outperforms SMOTE in this scenario (possibly due to redundant normal samples in wind power); evaluation metrics for imbalanced data such as F1-score and AUC-PR must be used—pursuing accuracy alone will lead to model failure.

The engineering implementation includes automated data pipelines (real-time access to sensor data from SCADA systems), model version management (tracking performance and A/B testing), interpretable outputs (feature importance analysis), and dynamic threshold adjustment (balancing recall and precision). Application value: Provides a reproducible template for wind power operation and maintenance, which can be migrated to fault prediction for rotating equipment such as aero-engines and industrial pumps. Future directions: Introduce time-series modeling (LSTM, Transformer) to capture degradation trends, integrate multi-source heterogeneous data, and explore federated learning to achieve knowledge sharing across wind farms.