Machine Learning-Driven Predictive Maintenance for Wind Turbines

Wind turbines play a vital role in sustainable energy generation, but their continuous operation makes them vulnerable to failures and costly downtime. Effective maintenance strategies are essential to ensure reliability and maximize energy production. Traditional preventive and corrective maintenance approaches are inefficient, often resulting in unnecessary repairs, delayed fault detection, and increased operational costs. To address these limitations, this paper introduces a Self-Supervised Learning Maintenance Framework (SSL-MF), which leverages unlabeled sensor data and contrastive learning techniques for early fault detection and accurate Remaining Useful Life (RUL) prediction. The proposed method enables proactive decision-making by learning robust feature representations without extensive labeled datasets, thus reducing reliance on expensive manual annotations. Experimental evaluation demonstrates that SSL-MF significantly improves anomaly detection accuracy, extends fault prediction horizons, and optimizes maintenance scheduling. This advancement enhances wind turbine reliability, reduces downtime, and lowers overall maintenance costs.