Smart sensing rail: Solutions, applications and challenges for railway condition monitoring

Smart railway systems (SRS) are getting emerging attention and have driven the development of a series of smart substructures, including smart rail pads, smart sleepers, smart bolts, etc. However, the concept of smart sensing rail (SSR) is rarely defined or systematically reviewed in existing literature. This paper aims to clarify the concept of SSR and provide an overview of its solutions, applications, and challenges. Within the smart railway substructure family, SSR is defined here as rails integrated with advanced sensing technologies that enable information interaction with external systems. The integrated techniques are characterized by permanent or semi-permanent installation, long-distance monitoring capabilities and suitability for long-term operation with low maintenance requirements. Several sensing technologies that meet the definition of SSR are introduced. These technologies include optical fiber sensing, guided wave techniques, RFID systems, and clamped sensor applications. Their sensing principles, installation forms, and application scenarios in railway condition monitoring are summarized and discussed. The review further discusses future development directions for integration with SSR, including energy harvesting, additive manufacturing, IoT, digital twins, and artificial intelligence, and highlights the main challenges that still limit wider deployment in long-term durability, large-scale data management, multi-source data fusion, and the transition from sensing to reliable diagnosis and maintenance decision-making. In summary, SSR provides a promising pathway toward more intelligent, condition-aware, and predictive railway infrastructure, but its broader implementation will depend on further advances in system integration, robustness, and data-to-decision capability.

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Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit
Published
2026-09-09
DOI
https://doi.org/10.1177/09544097261486927
Primary Topic
Railway Engineering and Dynamics
Type
article
Field-Weighted Citation Impact
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Smart sensing rail: Solutions, applications and challenges for railway condition monitoring

Guoqing Jing, Xiaodong Han, Ruizhe Zhang, Liangliang Cheng et al.
Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit
Railway Engineering and Dynamics
article

Smart sensing rail: Solutions, applications and challenges for railway condition monitoring

Guoqing Jing, Xiaodong Han, Ruizhe Zhang, Liangliang Cheng, Shaoguang Li
article en

Abstract

Smart railway systems (SRS) are getting emerging attention and have driven the development of a series of smart substructures, including smart rail pads, smart sleepers, smart bolts, etc. However, the concept of smart sensing rail (SSR) is rarely defined or systematically reviewed in existing literature. This paper aims to clarify the concept of SSR and provide an overview of its solutions, applications, and challenges. Within the smart railway substructure family, SSR is defined here as rails integrated with advanced sensing technologies that enable information interaction with external systems. The integrated techniques are characterized by permanent or semi-permanent installation, long-distance monitoring capabilities and suitability for long-term operation with low maintenance requirements. Several sensing technologies that meet the definition of SSR are introduced. These technologies include optical fiber sensing, guided wave techniques, RFID systems, and clamped sensor applications. Their sensing principles, installation forms, and application scenarios in railway condition monitoring are summarized and discussed. The review further discusses future development directions for integration with SSR, including energy harvesting, additive manufacturing, IoT, digital twins, and artificial intelligence, and highlights the main challenges that still limit wider deployment in long-term durability, large-scale data management, multi-source data fusion, and the transition from sensing to reliable diagnosis and maintenance decision-making. In summary, SSR provides a promising pathway toward more intelligent, condition-aware, and predictive railway infrastructure, but its broader implementation will depend on further advances in system integration, robustness, and data-to-decision capability.

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit
University of Groningen (NL), Beijing Jiaotong University (CN), Network Rail (GB), RMIT University (AU)
Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Railway Engineering and Dynamics
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