Abstract
In this contribution, we give an insight in our experiences in the technical and organizational realization of industrial analytics. We address challenges in implementing industrial analytics in real-world applications and discuss aspects to consider when designing a machine learning solution for production. We focus on technical and organizational aspects to make industrial analytics work for real-world applications in factory automation. As an example, we consider a machine learning use case in the area of industry compressors. We discuss the importance of scalability and reusability of data analytics pipelines and present a container-based system architecture.
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Koester, M. (2019). Making Industrial Analytics work for Factory Automation Applications. In: Beyerer, J., Kühnert, C., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 9. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58485-9_13
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DOI: https://doi.org/10.1007/978-3-662-58485-9_13
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Publisher Name: Springer Vieweg, Berlin, Heidelberg
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Online ISBN: 978-3-662-58485-9
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