Automated Wind Turbine Blade Defect Detection and Prognosis via Multi-modal Data Fusion and HyperScore Evaluation

**Abstract:** This paper introduces a novel framework for early detection and prognosis of defects in wind turbine blades, utilizing a multi-modal data ingestion and normalization layer coupled with a semantic understanding engine and a rigorous evaluation pipeline. Leveraging techniques from automated theorem proving, code verification, and high-dimensional data analysis, the system moves beyond simple visual inspection to achieve near-perfect accuracy in defect identification and prediction of remaining useful life (RUL), with quantifiable improvements in operational efficiency and reduced maintenance costs. This framework's novelty lies in its holistic approach to data integration and its score-fusion methodology employing Shapley-AHP weighting and Bayesian calibration to prioritize reliability and impact.