Deep Learning for Indoor Pedestal Fan Blade Inspection: Utilizing Low-Cost Autonomous Drones in an Educational Setting

Abstract

This paper introduces a drone-based surrogate project aimed at serving as a preliminary educational platform for undergraduate students in the Electrical and Computer Engineering (ECE) fields. Utilizing small Unmanned Aerial Vehicles (sUAVs), this project serves as a surrogate for the inspection of wind turbines using scaled-down pedestal fans to replace actual turbines. This approach significantly reduces the costs, risks, and logistical complexities, enabling feasible and safe on-campus experiments. Through this project, students engage in hands-on applications of Python programming, computer vision, and machine learning algorithms to detect and classify simulated defects in pedestal fan blade (PFB) images. The primary educational objectives are to equip students with foundational skills in autonomous systems and data analysis, critical for their progression to larger scale projects involving professional drones and actual wind turbines in wind farm settings. This surrogate setup not only provides practical experience in a controlled learning environment, but also prepares students for real-world challenges in renewable energy technologies, emphasizing the transition from theoretical knowledge to practical skills.