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Challenges of Stream Learning for Predictive Maintenance in the Railway Sector

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IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2020, IoT Streams 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1325))

Abstract

Smart trains nowadays are equipped with sensors that generate an abundance of data during operation. Such data may, directly or indirectly, reflect the health state of the trains. Thus, it is of interest to analyze these data in a timely manner, preferably on-the-fly as they are being generated, to make maintenance operations more proactive and efficient. This paper provides a brief overview of predictive maintenance and stream learning, with the primary goal of leveraging stream learning in order to enhance maintenance operations in the railway sector. We justify the applicability and promising benefits of stream learning via the example of a real-world railway dataset of the train doors.

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Notes

  1. 1.

    Heavy calculations can be tolerated if we have powerful machines. This weakness concerns the limitation of the technology rather than the limitation of the algorithm.

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Author information

Authors and Affiliations

  1. IKOS Consulting, 92300, Levallois-Perret, France

    Minh Huong Le Nguyen, Fabien Turgis & Pierre-Emmanuel Fayemi

  2. Telecom Paris, 91120, Palaiseau, France

    Minh Huong Le Nguyen & Albert Bifet

Authors
  1. Minh Huong Le Nguyen
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  2. Fabien Turgis
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  3. Pierre-Emmanuel Fayemi
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  4. Albert Bifet
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Corresponding authors

Correspondence to Minh Huong Le Nguyen , Fabien Turgis , Pierre-Emmanuel Fayemi or Albert Bifet .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Joao Gama

  2. Halmstad University, Halmstad, Sweden

    Sepideh Pashami

  3. Waikato University, Hamilton, New Zealand

    Albert Bifet

  4. University of Lille, Lille, France

    Moamar Sayed-Mouchawe

  5. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  6. Graz University of Technology, Graz, Austria

    Franz Pernkopf

  7. University of Duisburg-Essen, Essen, Germany

    Gregor Schiele

  8. XILINX Research, Dublin, Ireland

    Michaela Blott

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Le Nguyen, M.H., Turgis, F., Fayemi, PE., Bifet, A. (2020). Challenges of Stream Learning for Predictive Maintenance in the Railway Sector. In: Gama, J., et al. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. ITEM IoT Streams 2020 2020. Communications in Computer and Information Science, vol 1325. Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-66770-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66769-6

  • Online ISBN: 978-3-030-66770-2

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