Purpose The purpose of this study is to develop a scalable and privacy-preserving predictive maintenance framework for wind farms by addressing the challenges of heterogeneous multi-component degradation. By combining stochastic physics-based models with machine learning techniques under a federated learning (FL) paradigm, the framework aims to provide accurate estimation of remaining useful life (RUL) for critical components while preserving data confidentiality. Furthermore, a multi-objective optimization layer leverages prognostic outputs to minimize maintenance costs and maximize turbine availability, offering an intelligent solution for enhancing reliability and efficiency in modern wind energy infrastructures. Design/methodology/approach This study proposes an integrated framework for predictive maintenance in wind farms, addressing multi-component degradation through the combination of FL and hybrid degradation models. Under variable wind and load conditions, critical wind turbine components such as gearboxes, bearings and generators exhibit heterogeneous and nonlinear degradation behaviors that challenge conventional maintenance approaches. The proposed framework integrates stochastic physics-based degradation modeling with data-driven learning techniques to enable accurate and interpretable estimation of the RUL at the component level. FL is employed to facilitate decentralized model training while preserving data privacy, allowing turbine-level models to collaboratively improve prognostic performance without the need for centralized data aggregation. Simulation-based evaluation demonstrates that the proposed adaptive framework achieves a reduction in cumulative wear of approximately 35% compared with static maintenance strategies, while increasing the Weibull characteristic life by nearly 49%. In addition, the framework reduces total maintenance cost by about 22%, improves estimation accuracy by lowering the Kalman filter mean absolute error by approximately 44% and increases system availability by more than 140% relative to static policies. These results confirm that the proposed approach provides a scalable, privacy-preserving and cost-efficient solution for predictive maintenance and reliability enhancement in distributed wind farm environments. Findings The study demonstrates that integrating hybrid degradation models with FL significantly improves the accuracy of RUL estimation for wind turbine components under heterogeneous operating conditions. Simulation results reveal that the proposed framework reduces wear progression, lowers maintenance costs and enhances overall system reliability compared to traditional centralized or periodic strategies. The federated approach ensures data privacy and reduces communication overhead, while the optimization layer effectively balances cost efficiency and turbine availability. These findings highlight the framework’s potential as a scalable, intelligent and privacy-preserving solution for predictive maintenance in modern wind farms. Research limitations/implications This study is based on simulation experiments and may not fully capture the complexities of real-world wind farm operations, such as unmodeled environmental influences or rare failure modes. The FL framework requires consistent data quality and communication infrastructure across turbines, which may limit applicability in regions with poor connectivity. Future work should involve large-scale field validation, incorporation of more diverse degradation mechanisms and integration with real-time supervisory control systems. Despite these limitations, the framework offers a strong foundation for advancing intelligent, distributed and privacy-preserving predictive maintenance in modern wind energy infrastructures. Practical implications The proposed framework provides wind farm operators with a scalable tool to optimize maintenance scheduling while reducing downtime and operational costs. By accurately estimating the RUL of critical components such as gearboxes, bearings and generators, operators can shift from fixed-interval maintenance to condition-based strategies, extending component lifespan and improving asset reliability. The FL approach preserves data privacy, enabling collaboration across multiple sites without centralizing sensitive operational data. This enhances trust and data security while supporting widespread adoption, making the framework highly applicable for industrial deployment in modern wind energy infrastructures. Social implications The adoption of intelligent predictive maintenance in wind farms contributes to greater energy security and sustainability by ensuring higher system reliability and reduced downtime. Improved efficiency in wind energy production supports the global transition toward clean energy, lowering dependence on fossil fuels and reducing greenhouse gas emissions. By preserving data privacy through FL, the framework also fosters trust and collaboration between operators, regulators and stakeholders. Ultimately, this approach strengthens public confidence in renewable energy technologies, promotes green job creation and accelerates the achievement of climate and sustainability goals at both regional and global levels. Originality/value This study introduces a novel predictive maintenance framework that integrates hybrid degradation models with FL to address the challenges of heterogeneous, multi-component wear in wind farms. Unlike traditional centralized or periodic strategies, the framework enables accurate and interpretable RUL predictions while preserving data privacy and reducing communication costs. The addition of a multi-objective optimization layer ensures cost-efficient and availability-driven maintenance scheduling. This combination of decentralized learning, physics-informed modeling and optimization provides a scalable, intelligent and privacy-preserving solution, offering significant value for advancing maintenance practices in modern renewable energy infrastructures.
Cheikh et al. (Thu,) studied this question.