| Autor: |
Veloso, Bruno, Ribeiro, Rita P., Gama, João, Pereira, Pedro Mota |
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| Zdroj: |
Scientific Data; 12/13/2022, Vol. 9 Issue 1, p1-8, 8p |
| Abstrakt: |
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. Measurement(s) pressure of air • Temperature • ampere Technology Type(s) Pressure Sensor Device • Temperature Sensor Device • Electrical Current Sample Characteristic - Environment trains Sample Characteristic - Location Portuguese Republic [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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