Challenges of Stream Learning for Predictive Maintenance in the Railway Sector. Le Nguyen, M. H., Turgis, F., Fayemi, P., & Bifet, A. In Gama, J., Pashami, S., Bifet, A., Sayed-Mouchawe, M., Fröning, H., Pernkopf, F., Schiele, G., & Blott, M., editors, IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, of Communications in Computer and Information Science, pages 14–29, Cham, 2020. Springer International Publishing. doi abstract bibtex 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.
@inproceedings{le_nguyen_challenges_2020,
address = {Cham},
series = {Communications in {Computer} and {Information} {Science}},
title = {Challenges of {Stream} {Learning} for {Predictive} {Maintenance} in the {Railway} {Sector}},
isbn = {978-3-030-66770-2},
doi = {10.1007/978-3-030-66770-2_2},
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.},
language = {en},
booktitle = {{IoT} {Streams} for {Data}-{Driven} {Predictive} {Maintenance} and {IoT}, {Edge}, and {Mobile} for {Embedded} {Machine} {Learning}},
publisher = {Springer International Publishing},
author = {Le Nguyen, Minh Huong and Turgis, Fabien and Fayemi, Pierre-Emmanuel and Bifet, Albert},
editor = {Gama, Joao and Pashami, Sepideh and Bifet, Albert and Sayed-Mouchawe, Moamar and Fröning, Holger and Pernkopf, Franz and Schiele, Gregor and Blott, Michaela},
year = {2020},
keywords = {Predictive maintenance, Railway, Stream learning},
pages = {14--29},
}
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