A Benchmark dataset for predictive maintenance

The paper describes the Railway 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.