Associations of Autonomous Inspection Analytics and Resource Recovery with Defect Prioritization in Wind Blade Maintenance

Abstract

The rapid global expansion of wind energy has led to an unprecedented increase in the number of wind turbine blades in operation. These complex composite structures are subjected to harsh environmental and operational conditions, resulting in progressive structural degradation that threatens both energy production and structural integrity. Traditional manual inspection methods are labor-intensive, hazardous, and lack the quantitative precision required for optimal predictive maintenance. This paper presents a comprehensive framework that integrates autonomous unmanned aerial vehicle (UAV) inspection analytics with a circular-economy-driven resource recovery and maintenance scheduling model. Using multi-sensor aerial data, including high-resolution visual and infrared thermographic imaging, we develop a deep-learning-based segmentation and classification pipeline to extract precise defect characteristics. A multi-criteria Blade Defect Severity Index (BDSI) is formulated to prioritize maintenance actions based on structural risk, location-specific stress profiles, and defect progression rates. Furthermore, this prioritization is coupled with a decision-making model for resource recovery, aligning maintenance interventions with structural repair, local refurbishment, or material recycling pathways. Experimental evaluation on a large-scale industrial dataset demonstrates that our proposed framework significantly reduces inspection-to-repair cycle times, minimizes unplanned downtime, and maximizes the preservation and recovery of valuable composite materials.