Systematic review railway infrastructure monitoring: From classic techniques to predictive maintenance
The efficiency and availability of modern railway infrastructure plays an increasingly strategic role in the sustainability, development and prosperity of communities and nations. Recent Artificial Intelligence (AI) algorithms, which enable the use of digital tools such as Data-Driven models that ca...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-01-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132241285631 |
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| Summary: | The efficiency and availability of modern railway infrastructure plays an increasingly strategic role in the sustainability, development and prosperity of communities and nations. Recent Artificial Intelligence (AI) algorithms, which enable the use of digital tools such as Data-Driven models that can automatically adapt system operation, make decisions and suggest strategies based on collected data, form the basis of modern Predictive Maintenance (PdM). PdM is considered a key opportunity for accurate Structural Health Monitoring (SHM), especially for railway infrastructure, where the transition from traditional preventive or periodic maintenance to PdM will reduce intervention times and costs. Furthermore, by directly correlating actual infrastructure conditions with measured information, SHM can utilise a limited number of sensors installed on critical components such as insulated rail joints. This review starts by clearly describing the different components that make up the railway infrastructure, the monitoring systems currently in use and the technical performance parameters that indicate their health status and goes on to examine the issues related to the SHM and related modern digital tools. All these topics are summarised to provide an effective theoretical and practical knowledge of SHM for railway infrastructure, to better understand the current transformation of the sector and to predict future developments. |
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| ISSN: | 1687-8140 |