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Modeling and Analyzing Performance of a Production Unit Using Dynamic Bayesian Networks

  • Conference paper
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Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017 (ISPEM 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 637))

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Abstract

The aim of this paper is to formulate a quantitative integrated model of how quality and productivity performances of a production system are interrelated. Indeed, productivity and quality, some of most important objectives of a production system have been studied separately since decades whereas studies are demonstrating a close interaction between them nowadays. Such an integrated model will be beneficial to engineers during design and/or operation stages of the system because it can be used to set up or to assess overall performance measures such as: total production rate, effective production rate, machines availability, inspection policies performance, etc. Dynamic Bayesian network will be used as the underlying mathematical tool to describe the dynamics of the state of the system as they are well suited for the representation of stochastic processes (machine failures, quality failures, etc.).

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Author information

Authors and Affiliations

  1. Laboratoire Génie de Production, Université Fédérale Toulouse - Midi Pyrénées, 47 Avenue d’Azeirex, 65016, Tarbes, France

    Ayeley Tchangani & François Pérès

Authors
  1. Ayeley Tchangani
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  2. François Pérès
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Corresponding author

Correspondence to Ayeley Tchangani .

Editor information

Editors and Affiliations

  1. Faculty of Mechanical Engineering, Centre for Advanced Manufacturing Technologies, Fraunhofer Project Center, Wrocław University of Technology, Wrocław, Poland

    Anna Burduk

  2. Lublin University of Technology, Lublin, Poland

    Dariusz Mazurkiewicz

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Tchangani, A., Pérès, F. (2018). Modeling and Analyzing Performance of a Production Unit Using Dynamic Bayesian Networks. In: Burduk, A., Mazurkiewicz, D. (eds) Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017. ISPEM 2017. Advances in Intelligent Systems and Computing, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-64465-3_28

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  • DOI: https://doi.org/10.1007/978-3-319-64465-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64464-6

  • Online ISBN: 978-3-319-64465-3

  • eBook Packages: EngineeringEngineering (R0)

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