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

• Type: Another Publication in an International Scientific Journal
• Date: 2021

Authors

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

Journal

Title: Sensors

Vol. 21 No. 17

Final page: 5739

ISSN: 1424-3210

Publisher: MDPI

Indexing

ISI Web of Knowledge - 61 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 22