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

Sensors

Gustavo de Assis Costa¹  João Gama²  Narjes Davari²  Rita P. Ribeiro²  Bruno Veloso²  Pedro Mota Pereira³ 

[1] Federal Institute of Goiás, Campus Jataí, Unity Flamboyant, Jataí 75801-326, Brazil;Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal;Metro of Porto, 4350-158 Porto, Portugal;

关键词: condition-based maintenance;    predictive maintenance;    machine learning;    deep learning;    artificial intelligence;    railway industry;   

DOI  :  10.3390/s21175739

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown