A Benchmark dataset for predictive maintenance
Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro M. Pereira
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 between 2020 and 2022 that aimed to develop
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 framework that can
be easily used and developed for the new machine learning methods. We believe
this dataset contains some interesting characteristics and can be a good
benchmark for predictive maintenance models.