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Pattern Recognition for Predictive Analysis in Automotive Industry

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Cybernetics and Mathematics Applications in Intelligent Systems (CSOC 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 574))

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Abstract

Predictive maintenance (PdM) techniques are designed to help identify the condition of devices in order to predict when maintenance should be performed. The ultimate goal of PdM is to perform maintenance at a scheduled point in time when the maintenance activity is most cost-effective and before the equipment loses performance within a threshold. Currently, reducing service costs and losses due to downtime is one of the ways to increase your profits and success in the market. We tried to identify problem messages and failures from the manufacturing data example set from car body work. Two different data sets were joined and we designed a process to identify message and failure alerts preceding errors.

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References

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  8. Simoncicova, V., Hrcka, L., Tadanai, O., Tanuska, P., Vazan, P.: Data pre-processing from production processes for analysis in automotive, industry [Internet] (2016). http://www.ceciis.foi.hr/app/public/conferences/1/ceciis2016/papers/DKB-3.pdf

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Author information

Authors and Affiliations

  1. Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Slovak University of Technology, Trnava, Slovak Republic

    Veronika Simoncicova, Lukas Hrcka, Lukas Spendla, Pavol Tanuska & Pavel Vazan

Authors
  1. Veronika Simoncicova
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  2. Lukas Hrcka
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  3. Lukas Spendla
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  4. Pavol Tanuska
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  5. Pavel Vazan
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Corresponding author

Correspondence to Lukas Hrcka .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín Faculty of Applied Informatics, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Roman Senkerik

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zuzana Kominkova Oplatkova

  4. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

  5. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

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Simoncicova, V., Hrcka, L., Spendla, L., Tanuska, P., Vazan, P. (2017). Pattern Recognition for Predictive Analysis in Automotive Industry. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Cybernetics and Mathematics Applications in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 574. Springer, Cham. https://doi.org/10.1007/978-3-319-57264-2_32

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  • DOI: https://doi.org/10.1007/978-3-319-57264-2_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57263-5

  • Online ISBN: 978-3-319-57264-2

  • eBook Packages: EngineeringEngineering (R0)

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