Weibull‐Neural Network Framework for Wind Turbine Lifetime Monitoring and Disturbance Identification

ABSTRACT Wind turbines are vital for sustainable energy, yet their reliability under diverse operational and environmental conditions remains a challenge, often leading to costly failures. This study presents a novel Weibull‐Neural Network Framework to enhance wind turbine lifetime monitoring by estimating reliability (R(t)) and mean residual life (MRL). The framework integrates a generalized Weibull distribution to model failure probabilities, a feedforward neural network to predict real‐time reliability using SCADA data, and bootstrap control charts for continuous monitoring and anomaly detection. By combining probabilistic modeling with neural network predictions, the approach captures the dynamic interplay of operational stressors (e.g., temperature, rotor speed) and environmental factors (e.g., wind variability), overcoming limitations of traditional models like computational intensity and lack of interpretability. A lab‐scale case study, supplemented by a simulated commercial‐scale application on a 2 MW onshore turbine, validates the framework's effectiveness. Key contributions include: (1) a unique integration of generalized Weibull with feedforward neural networks and bootstrap control charts for real‐time monitoring, addressing gaps in interpretability and data scarcity in existing works; (2) early disturbance identification for proactive maintenance, and (3) a scalable, interpretable solution for wind turbine reliability management. This framework minimizes downtime, reduces maintenance costs, and supports sustainable energy production.