Comparative analysis into corrective and predictive maintenance based on continuous monitoring through embedded sensors

Abstract

Railway managers face increasing pressure to reduce downtime, emissions, and operational costs. In response, the development of new technologies to facilitate the study of predictive maintenance and to evaluate its impact on the economy and society is becoming increasingly essential, thus, the present study is carried out. This study introduces a three-stage approach for enabling predictive maintenance using a sensor-integrated intelligent rail pad. In the first stage, the monitoring system’s mechanical performance and long-term durability were validated through standardized impact attenuation and fatigue tests, which is essential to guarantee the efficiency of the predictive system. The second stage involved creating high-precision models from sensor signals estimating axle loads and predicting ballast settlement. In the third stage, a comprehensive life cycle assessment (LCA) and life cycle cost analysis (LCCA) were performed to compare the benefits of predictive versus corrective maintenance strategies. The experimental tests confirmed compliance of the intelligent rail pads with regulatory standards and demonstrated the system’s suitability for continuous monitoring. Over a 50-year period, results indicate that predictive maintenance can reduce settlement by up to 14%, decrease ballast renewals by 13%, and halve energy use, emissions, and maintenance costs. These findings suggest that the intelligent rail pad has the potential to serve as a scalable solution for enhancing the efficiency, sustainability, and economic performance of railway infrastructure.

Keywords

Eco-innovative pad / Corrective maintenance / Predictive maintenance / LCA and LCCA

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Funding

Ministerio de Ciencia e Innovación(PDC2022-133966-I00)

RIGHTS & PERMISSIONS

The Author(s)