The MetroPT dataset for predictive maintenance. Veloso, B., Ribeiro, R. P., Gama, J., & Pereira, P. M. Scientific Data, 9(1):764, December, 2022. ![The MetroPT dataset for predictive maintenance [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The paper describes the MetroPT data set, an outcome of a Predictive Maintenance project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 to develop machine learning methods for online anomaly detection and failure prediction. Several analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) provide a framework that can be easily used and help the development of new machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.
@article{veloso_metropt_2022,
title = {The {MetroPT} dataset for predictive maintenance},
volume = {9},
copyright = {2022 The Author(s)},
issn = {2052-4463},
url = {https://www.nature.com/articles/s41597-022-01877-3},
doi = {10.1038/s41597-022-01877-3},
abstract = {The paper describes the MetroPT data set, an outcome of a Predictive Maintenance project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 to develop machine learning methods for online anomaly detection and failure prediction. Several analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) provide a framework that can be easily used and help the development of new machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.},
language = {en},
number = {1},
urldate = {2023-10-09},
journal = {Scientific Data},
author = {Veloso, Bruno and Ribeiro, Rita P. and Gama, João and Pereira, Pedro Mota},
month = dec,
year = {2022},
keywords = {Computer science, Scientific data},
pages = {764},
}
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