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Application of Machine Learning to Identify State Drivers

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Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Part of the book series: Springer Theses ((Springer Theses))

Abstract

In this section, the application of ML is investigated in further detail. First ML is briefly introduced in more detail with respect to the manufacturing domain. Based on this brief general elaboration, SVM algorithms are selected as a suitable ML technique to match the detailed requirements of the stated research problem. In the final subsection, the application of SVM is discussed towards its objective of identification of state drivers in manufacturing programmes. Within this last subsection, the application and evaluation approach of the SVM application are presented and the derived hypotheses are detailed based on the decision to use the SVM algorithm to conclude the section.

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Notes

  1. 1.

    The content of this section has been partly published in accordance with (Universität Bremen 2007) in (Wuest et al. 2012).

  2. 2.

    The content of this section has been partly published in accordance with (Universität Bremen 2007) in (Wuest et al. 2013).

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Authors and Affiliations

  1. Department of ICT Applications for Production, BIBA—Bremer Institut für Produktion und Logistik GmbH, Bremen, Germany

    Thorsten Wuest

  2. Department of Production Engineering, University of Bremen, Bremen, Germany

    Thorsten Wuest

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  1. Thorsten Wuest
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Wuest, T. (2015). Application of Machine Learning to Identify State Drivers. In: Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-17611-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-17611-6_5

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