A Benchmark dataset for predictive maintenance
This provides a new benchmark dataset for researchers and practitioners working on predictive maintenance in public transportation systems, though it is incremental as it focuses on data collection rather than novel methods.
The paper introduces the MetroPT dataset, collected in 2022 from a metro system in Porto, Portugal, to evaluate machine learning methods for online anomaly detection and failure prediction in predictive maintenance, providing sensor and GPS data for benchmarking.
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.