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Auflistung nach Autor:in "Kellner, Domenic"

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  • Textdokument
    From Physical to Virtual: Leveraging Drone Imagery to Automate Photovoltaic System Maintenance
    (INFORMATIK 2021, 2021) Lowin, Maximilian; Kellner, Domenic; Kohl, Tobias; Mihale-Wilson, Cristina
    Optimizing 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.
  • Textdokument
    Towards Designing a User-centric Decision Support System for Predictive Maintenance in SMEs
    (INFORMATIK 2021, 2021) Kellner, Domenic; Lowin, Maximilian; von Zahn, Moritz; Chen, Johannes
    In manufacturing, small and medium-sized enterprises (SMEs) face global competition. In the field of predictive maintenance (PdM), artificial intelligence (AI) helps to prevent machine failures and has the potential to significantly reduce costs and increase process efficiency. Even though PdM has several benefits, it also entails considerable challenges for SMEs, especially when it comes to user interactions. In this short paper, we harness the design science methodology and discuss several problems regarding user interactions with predictive maintenance applications. We incorporate two different literature streams, namely, predictive maintenance and decision support systems. Finally, we present necessary design requirements, principles, features, and propose a research design to further develop and evaluate a user-centric PdM decision support system. Thereby, we contribute to making AI tangible in SMEs.