Design of sensorized rail pads for real-time monitoring and predictive maintenance of railway infrastructure

Embedding sensors in rail pads allows for direct monitoring of train–track interaction, which is essential for preventive maintenance and sustainable management of railway infrastructure. Nonetheless, given the critical role that rail pads play in enhancing railway track performance and durability, it is crucial to define the optimal configuration of the sensorized pads (InterActive Pads) that ensures both mechanical reliability and functional accuracy. Also, before its widespread application, it is mandatory to provide calibration and modelling to allow for preventive maintenance, improving sustainable management. Thus, this research optimizes the design of rail pads with embedded piezoelectric sensors while validating its performance and developing calibration models to enable the implementation of preventive measures for railroad tracks. Laboratory tests identified the optimal sensor position at the rail pad extremity, featuring a half-embedded design with a gap beneath to ensure mechanical resistance and durability. Large-scale testing further facilitated the development of a calibration model that enhances diagnostic accuracy and supports proactive and sustainable maintenance strategies. The findings demonstrate a strong correlation between sensor signals and train-induced forces, allowing predictions of long-term track performance. This predictive capability enables more effective maintenance, reducing costs and improving safety. By providing a sustainable solution for railway management, this research lays the groundwork for future implementation on real tracks, offering a robust framework for proactive, data-driven maintenance strategies.

Publication Details

Journal
Institutional Repository of the University of Granada (University of Granada)
Published
2025-02-19
DOI
https://doi.org/10.3390/infrastructures
Citations
8
Primary Topic
Plant Gene Expression Analysis
Type
article
Field-Weighted Citation Impact
2.42
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article

Design of sensorized rail pads for real-time monitoring and predictive maintenance of railway infrastructure

8 citations
Institutional Repository of the University of Granada (University of Granada)
Plant Gene Expression Analysis
2.42
article

Design of sensorized rail pads for real-time monitoring and predictive maintenance of railway infrastructure

article en
8 citations

Abstract

Embedding sensors in rail pads allows for direct monitoring of train–track interaction, which is essential for preventive maintenance and sustainable management of railway infrastructure. Nonetheless, given the critical role that rail pads play in enhancing railway track performance and durability, it is crucial to define the optimal configuration of the sensorized pads (InterActive Pads) that ensures both mechanical reliability and functional accuracy. Also, before its widespread application, it is mandatory to provide calibration and modelling to allow for preventive maintenance, improving sustainable management. Thus, this research optimizes the design of rail pads with embedded piezoelectric sensors while validating its performance and developing calibration models to enable the implementation of preventive measures for railroad tracks. Laboratory tests identified the optimal sensor position at the rail pad extremity, featuring a half-embedded design with a gap beneath to ensure mechanical resistance and durability. Large-scale testing further facilitated the development of a calibration model that enhances diagnostic accuracy and supports proactive and sustainable maintenance strategies. The findings demonstrate a strong correlation between sensor signals and train-induced forces, allowing predictions of long-term track performance. This predictive capability enables more effective maintenance, reducing costs and improving safety. By providing a sustainable solution for railway management, this research lays the groundwork for future implementation on real tracks, offering a robust framework for proactive, data-driven maintenance strategies.

Institutional Repository of the University of Granada (University of Granada)
Industry, innovation and infrastructure
Openalex Percentile: Top 10%
Plant Gene Expression Analysis
2.42
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