TITLE: A Benchmark dataset for predictive maintenance 

ABSTRACT: The paper describes the MetroPT data set, an outcome of a eXplainable
Predictive Maintenance (XPM) project with an urban metro public transportation
service in Porto, Portugal. The data was collected in 2022 that aimed to
evaluate machine learning methods for online anomaly detection and failure
prediction. By capturing several analogic sensor signals (pressure,
temperature, current consumption), digital signals (control signals, discrete
signals), and GPS information (latitude, longitude, and speed), we provide a
dataset that can be easily used to evaluate online machine learning methods.
This dataset contains some interesting characteristics and can be a good
benchmark for predictive maintenance models.
The paper describes the MetroPT data set, an outcome of a eXplainable
Predictive Maintenance (XPM) project with an urban metro public transportation
service in Porto, Portugal. The data was collected in 2022 that aimed to
evaluate machine learning methods for online anomaly detection and failure
prediction. By capturing several analogic sensor signals (pressure,
temperature, current consumption), digital signals (control signals, discrete
signals), and GPS information (latitude, longitude, and speed), we provide a
dataset that can be easily used to evaluate online machine learning methods.
This dataset contains some interesting characteristics and can be a good
benchmark for predictive maintenance models.
A Benchmark dataset for predictive maintenance
http://arxiv.org/abs/2207.05466v3