A Survey on Data-Driven Predictive Maintenance for the Railway Industry

• Tipo: Outra Publicação em Revista Científica Internacional
• Data: 2021

Autores

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

Revista

Título: Sensors

Vol. 21 Nº 17

Página Final: 5739

ISSN: 1424-3210

Editora: MDPI

Indexação

ISI Web of Knowledge - 65 Citações

Outras Informações

ID Authenticus: P-00V-B8R

DOI: 10.3390/s21175739

Abstract (EN): In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events-anomaly detection in time-series-can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 22