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Design of a Multi-Robot System for Wind Turbine Maintenance

  • The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.

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Metadaten
Author:Josef Franko, Shengzhi Du, Stephan Kallweit, Enno Sebastian Duelberg, Heiko Engemann
DOI:https://doi.org/10.3390/en13102552
ISSN:1996-1073
Parent Title (English):Energies
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Year of Completion:2020
Date of the Publication (Server):2020/07/17
Volume:13
Issue:10
First Page:Article 2552
Link:https://doi.org/10.3390/en13102552
Zugriffsart:weltweit
Institutes:FH Aachen / Fachbereich Maschinenbau und Mechatronik
FH Aachen / MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik
collections:Verlag / MDPI
Open Access / Gold
Licence (German):License LogoCreative Commons - Namensnennung