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• W3118404269 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." @default.
• W3118404269 created "2021-01-18" @default.
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• W3118404269 date "2020-01-01" @default.
• W3118404269 modified "2023-10-03" @default.
• W3118404269 title "Challenges of Stream Learning for Predictive Maintenance in the Railway Sector" @default.
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• W3118404269 doi "https://doi.org/10.1007/978-3-030-66770-2_2" @default.
• W3118404269 hasPublicationYear "2020" @default.
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