A Survey on Data-Driven Predictive Maintenance for the Railway Industry
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-seri...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/17/5739 |
| _version_ | 1829453939553075200 |
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| author | Narjes Davari Bruno Veloso Gustavo de Assis Costa Pedro Mota Pereira Rita P. Ribeiro João Gama |
| author_facet | Narjes Davari Bruno Veloso Gustavo de Assis Costa Pedro Mota Pereira Rita P. Ribeiro João Gama |
| author_sort | Narjes Davari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:04:14Z |
| format | Article |
| id | doaj.art-b4288f37b74a42bf8458a13ecdd415eb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:04:14Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b4288f37b74a42bf8458a13ecdd415eb2023-11-22T11:11:37ZengMDPI AGSensors1424-82202021-08-012117573910.3390/s21175739A Survey on Data-Driven Predictive Maintenance for the Railway IndustryNarjes Davari0Bruno Veloso1Gustavo de Assis Costa2Pedro Mota Pereira3Rita P. Ribeiro4João Gama5Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalInstitute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalFederal Institute of Goiás, Campus Jataí, Unity Flamboyant, Jataí 75801-326, BrazilMetro of Porto, 4350-158 Porto, PortugalInstitute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalInstitute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalIn 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.https://www.mdpi.com/1424-8220/21/17/5739condition-based maintenancepredictive maintenancemachine learningdeep learningartificial intelligencerailway industry |
| spellingShingle | Narjes Davari Bruno Veloso Gustavo de Assis Costa Pedro Mota Pereira Rita P. Ribeiro João Gama A Survey on Data-Driven Predictive Maintenance for the Railway Industry Sensors condition-based maintenance predictive maintenance machine learning deep learning artificial intelligence railway industry |
| title | A Survey on Data-Driven Predictive Maintenance for the Railway Industry |
| title_full | A Survey on Data-Driven Predictive Maintenance for the Railway Industry |
| title_fullStr | A Survey on Data-Driven Predictive Maintenance for the Railway Industry |
| title_full_unstemmed | A Survey on Data-Driven Predictive Maintenance for the Railway Industry |
| title_short | A Survey on Data-Driven Predictive Maintenance for the Railway Industry |
| title_sort | survey on data driven predictive maintenance for the railway industry |
| topic | condition-based maintenance predictive maintenance machine learning deep learning artificial intelligence railway industry |
| url | https://www.mdpi.com/1424-8220/21/17/5739 |
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