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Comparison of Machine Learning Models for Predictive Maintenance Applications

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Advances in System-Integrated Intelligence (SYSINT 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 546))

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Abstract

In the field of industry 4.0, one of the sectors in which research is particularly active is the area of Predictive Maintenance(PdM), the purpose of which is to improve the industrial production process. This type of maintenance aims to predict a possible failure event, reduce non-production times and increase the quality of the processing result. The objective of this paper is to select the best Machine Learning models for a PdM application. In particular, such a model should allow making a prediction based on a real dataset, obtained by monitoring a turning process, with the aim of making the classification of the chip shape. The criteria used to choose the best model are accuracy and prediction speed (to reduce the inference time). Indeed it is crucial to spot any potential machine fault in the shortest time possible, in order to intervene before the machine fails. Hence, our goal is to choose the ML models with a lesser inference time while still maintaining high accuracy.

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Author information

Authors and Affiliations

  1. University Mediterranea of Reggio Calabria, 89124, Reggio Calabria, Italy

    Alessia Lazzaro

  2. University of Naples Federico II, 80125, Napoli, Italy

    Doriana Marilena D’Addona

  3. Center for Digital Safety Security, Austrian Institute of Technology GMbH, 1210, Vienna, Austria

    Massimo Merenda

Authors
  1. Alessia Lazzaro
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  2. Doriana Marilena D’Addona
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  3. Massimo Merenda
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Corresponding author

Correspondence to Alessia Lazzaro .

Editor information

Editors and Affiliations

  1. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Maurizio Valle

  2. Fraunhofer Institute for Manufacturing Technology and Advanced Materials, Bremen, Germany

    Dirk Lehmhus

  3. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Christian Gianoglio

  4. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Edoardo Ragusa

  5. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Lucia Seminara

  6. Computer Science and Mathematics, University of Bremen, Bremen, Germany

    Stefan Bosse

  7. Department of Electrical and Electronics Engineering (EEE), Lebanese International University (LIU), Beirut, Lebanon

    Ali Ibrahim

  8. BIBA, University of Bremen, Bremen, Germany

    Klaus-Dieter Thoben

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Lazzaro, A., D’Addona, D.M., Merenda, M. (2023). Comparison of Machine Learning Models for Predictive Maintenance Applications. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_62

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  • DOI: https://doi.org/10.1007/978-3-031-16281-7_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16280-0

  • Online ISBN: 978-3-031-16281-7

  • eBook Packages: EngineeringEngineering (R0)

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