Fachbereich Maschinenbau und Mechatronik
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Entwicklung eines Bemessungsmodells für punktförmige Verbindungen textibewehrter Betonbauteile
(2008)
In Folge mehrjähriger statistischer Untersuchungen an der FH Aachen ist unter anderem ein Eingangstest entstanden, der als Diagnosetool für einen erfolgreichen Studieneinstieg verwendet wird. Es hat sich herausgestellt, dass ein Testergebnis von weniger als 25 (von maximal 56 erreichbaren) Punkten die Chance auf einen erfolgreichen Studieneinstieg deutlich verringert. Ungefähr die Hälfte aller Erstsemester hat weniger als 25 Punkte im Eingangstest. Weniger als 20 % dieser Gruppe bestehen innerhalb eines Jahres die Klausur Mathematik 1. Die investierte Zeit von zwei Semestern ist mit Blick auf den Wissenszuwachs und damit letztendlich den Studienerfolg nicht effizient genutzt. Deshalb haben wir im WS 2013/14 einen semesterbegleitenden Anpassungskurs für diese Gruppe installiert. Ziel eines solchen Kurses ist es, die Student/innen innerhalb eines Jahres in die Lage zu versetzen, nach zwei Semestern problemlos den Vorlesungen in Mathematik zu folgen. Dieser Artikel beschreibt das Konzept dieses Anpassungskurses und zeigt erste Ergebnisse und Probleme des Pilotdurchgangs auf.
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.
For smaller railway operators or those with a diverse fleet, it can be difficult to collect sufficient data to improve maintenance programs. At the same time, new rules such as entity in charge of maintenance – ECM – regulations impose an additional workload by requiring a dedicated maintenance management system and specific reports. The RailCrowd platform sets out to facilitate compliance with ECM and similar regulations while at the same time pooling anonymised fleet data across operators to form virtual fleets, providing greater data insights.