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Communication Dans Un Congrès Année : 2020

Challenges of Stream Learning for Predictive Maintenance in the Railway Sector

Résumé

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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Dates et versions

hal-04468448 , version 1 (20-02-2024)

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Citer

Minh-Huong Le Nguyen, Fabien Turgis, Pierre-Emmanuel Fayemi, Albert Bifet. Challenges of Stream Learning for Predictive Maintenance in the Railway Sector. IoT Streams 2020 for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning - Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers, Sep 2020, Ghent, Belgium. pp.14--29, ⟨10.1007/978-3-030-66770-2\_2⟩. ⟨hal-04468448⟩
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