IoT-driven track health monitoring for sustainable railway operations
Last modified: 2024-04-05
Abstract
The modernization of railway infrastructure maintenance practices demands innovative approaches to ensure safety, optimal performance, and optimized operational costs through the integration of novel technologies. The Internet of Things (IoT) delivers for this domain the innovative cloud- and edge-powered data collection, aggregation and processing which enables the cost effective and performant data-driven maintenance strategy.
The traditional approach to track maintenance relies on periodic inspections done with Track Recording Vehicles (TRV). Data collected by TRV measurements offers the most reliable information regarding the track geometry simply because the measurement is done under dynamic load. However, most of the rail network maintenance organizations are facing issues with the limited availability of the TRV. This impacts the condition monitoring schedule and proper maintenance of the track, often effecting speed restrictions to be applied.
This paper presents a comprehensive study on the continuous monitoring of railway track conditions using an IoT measurement device that assures non-invasive track monitoring with the possibility to be installed on passenger trains which are transiting the railway network much more frequently than a TRV. Our IoT measurement device captures and transmits critical information related to track geometry (alignment, longitudinal level, cross level, and twist of the track) by analysing the specific motions of the bogies of passenger trains. The collected data is saved and processed in cloud and can be accessed through a centralized monitoring dashboard, providing railway operators with valuable insights to the health of their track infrastructure.
Furthermore, this paper explores the possibility of defining degradation models of the track by analysing historical data patterns and predicting potential maintenance needs. The implementation of such predictive maintenance strategies not only improves safety but also optimizes operational costs by reducing the likelihood of unexpected track failures.
The traditional approach to track maintenance relies on periodic inspections done with Track Recording Vehicles (TRV). Data collected by TRV measurements offers the most reliable information regarding the track geometry simply because the measurement is done under dynamic load. However, most of the rail network maintenance organizations are facing issues with the limited availability of the TRV. This impacts the condition monitoring schedule and proper maintenance of the track, often effecting speed restrictions to be applied.
This paper presents a comprehensive study on the continuous monitoring of railway track conditions using an IoT measurement device that assures non-invasive track monitoring with the possibility to be installed on passenger trains which are transiting the railway network much more frequently than a TRV. Our IoT measurement device captures and transmits critical information related to track geometry (alignment, longitudinal level, cross level, and twist of the track) by analysing the specific motions of the bogies of passenger trains. The collected data is saved and processed in cloud and can be accessed through a centralized monitoring dashboard, providing railway operators with valuable insights to the health of their track infrastructure.
Furthermore, this paper explores the possibility of defining degradation models of the track by analysing historical data patterns and predicting potential maintenance needs. The implementation of such predictive maintenance strategies not only improves safety but also optimizes operational costs by reducing the likelihood of unexpected track failures.
Keywords
Smart infrastructure, IoT measurement device for railway track, track geometry, continuous monitoring of railway track, predictive maintenance