Lowin, MaximilianKellner, DomenicKohl, TobiasMihale-Wilson, Cristina2021-12-142021-12-142021978-3-88579-708-1https://dl.gi.de/handle/20.500.12116/37769Optimizing the maintenance of large-scale infrastructure can be a significant cost driver for small and medium-sized enterprises (SMEs). This paper presents a feasible approach to combine data from real-world physical structures collected through an automated maintenance process with cloud-based AI services to generate a meaningful virtual representation of such structures. We use photovoltaic systems as an exemplary physical structure and thermal imaging, collected through scheduled drone monitoring. With help of these unstructured data sources, we demonstrate our approach's applicability. Our solution artifact provides a lightweight AI application that is adoptable for other problem spaces, enabling an easier knowledge transfer from research to SMEs. By combining Cloud Computing with Machine Learning, the artifact identifies present and emerging damages of physical objects. It provides a virtual representation of the object's state and empowers a meaningful visualization.enDigital TwinMachine LearningPredictive MaintenancePhotovoltaic SystemInternet of ThingsProcess AutomationNeural NetworksVisualization10.18420/informatik2021-0991617-5468