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

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...

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Bibliographic Details
Published in:Drones (Basel) 2024-07, Vol.8 (7), p.298
Main Authors: Rodriguez, Angel A., Davis, Mason, Zander, Joshua, Nazario Dejesus, Edwin, Shekaramiz, Mohammad, Memari, Majid, Masoum, Mohammad A. S.
Format: Article
Language:English
Subjects:
Air-turbines
Algorithms
autonomous drone
Blades
Classification
College students
Computer engineering
Computer vision
Critical thinking
Data analysis
Deep learning
Drone aircraft
Drones
Educational technology
Energy technology
Engineering education
Environmental monitoring
Inspection
Inspections
Machine learning
Machine vision
Miniature aircraft
object detection
path planning
Project feasibility
Python
Skills
Students
Tello EDU drone
Turbines
Undergraduate study
Unmanned aerial vehicles
Wind farms
Wind power
Wind turbines
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Summary: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.
ISSN:2504-446X
2504-446X
DOI:10.3390/drones8070298