The MetroPT dataset for predictive maintenance

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Veloso, B (Autor) FEP; Rita Ribeiro (Autor) FCUP; João Gama (Autor) FEP; Pereira, PM (Autor) Outra

Revista

Título: Scientific Data

Vol.  9, Nº 764 (2022)

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: 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).

Idioma: Inglês

Tipo (Avaliação Docente): Científica