Publikationsserver der Technischen Hochschule Augsburg
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A Spatial Perspective on Green Technology Adoption in China: Insights from Patent Licensing Data
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Kombinierter Einfluss von psychologischen und biomechanischen Faktoren auf die muskulären Belastungen beim Fußballspielen
No full textOn Data-Preprocessing for Effective Predictive Maintenance on Multi-Purpose Machines
No full textMaintenance of complex machinery is time and resource intensive. Therefore, decreasing maintenance cycles by employing Predictive Maintenance (PdM) is sought after by many manufacturers of machines and can be a valuable selling point. However, currently PdM is a hard to solve problem getting increasingly harder with the complexity of the maintained system. One challenge is to adequately prepare data for model training and analysis. In this paper, we propose the use of expert knowledge–based preprocessing techniques to extend the standard data science–workflow. We define complex multi-purpose machinery as an pplication domain and test our proposed techniques on real-world data generated by numerous achines deployed in the wild. We find that our techniques enable and enhance model training
