The MetroPT dataset for predictive maintenance

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Veloso, B (Author) FEP; Rita Ribeiro (Author) FCUP; João Gama (Author) FEP; Pereira, PM (Author) Other

Journal

Title: Scientific Data

Vol.  9, No. 764 (2022)

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-00X-KPN

DOI: 10.1038/s41597-022-01877-3

Abstract (EN): 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. © 2022, The Author(s).

Language: English

Type (Professor's evaluation): Scientific