Latest revision as of 16:17, 21 January 2021

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.

Document type: Part of book or chapter of book

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The different versions of the original document can be found in:

• https://link.springer.com/content/pdf/10.1007%2F978-3-662-58485-9_13.pdf under the license https://creativecommons.org/licenses/by

• http://link.springer.com/content/pdf/10.1007/978-3-662-58485-9_13,

http://dx.doi.org/10.1007/978-3-662-58485-9_13 under the license cc-by

• https://link.springer.com/chapter/10.1007/978-3-662-58485-9_13,

https://www.scipedia.com/public/Koester_2018a,

https://dblp.uni-trier.de/db/conf/ml4cps/ml4cps2018.html#Koester18,

https://academic.microsoft.com/#/detail/2905317108 under the license http://creativecommons.org/licenses/by/4.0