A Survey on Data-Driven Predictive Maintenance for the Railway Industry. Davari, N., Veloso, B., Costa, G. d. A., Pereira, P. M., Ribeiro, R. P., & Gama, J. Sensors, 21(17):5739, January, 2021. Number: 17 Publisher: Multidisciplinary Digital Publishing Institute![A Survey on Data-Driven Predictive Maintenance for the Railway Industry [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events—anomaly detection in time-series—can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.
@article{davari_survey_2021,
title = {A {Survey} on {Data}-{Driven} {Predictive} {Maintenance} for the {Railway} {Industry}},
volume = {21},
copyright = {http://creativecommons.org/licenses/by/3.0/},
issn = {1424-8220},
url = {https://www.mdpi.com/1424-8220/21/17/5739},
doi = {10.3390/s21175739},
abstract = {In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events—anomaly detection in time-series—can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.},
language = {en},
number = {17},
urldate = {2023-10-26},
journal = {Sensors},
author = {Davari, Narjes and Veloso, Bruno and Costa, Gustavo de Assis and Pereira, Pedro Mota and Ribeiro, Rita P. and Gama, João},
month = jan,
year = {2021},
note = {Number: 17
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {artificial intelligence, condition-based maintenance, deep learning, machine learning, predictive maintenance, railway industry},
pages = {5739},
}
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