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Real-Time Monitoring for Condition Assessment and Long-term Behaviour of Metro Lines

ELIZABETH PISTONE, HANNO TÖLL, GÜNTHER ACHS

Abstract


This paper discusses two different series of results of a continuous monitoring system placed on the metro line in Vienna, Austria. The first one is represented by real-time results gathered over a time span of three years aimed at assessing the long-term behavior of massive train wheels. The second one is represented by results of impulse-tests performed in all major stations of the aforementioned metro line aimed at assessing the condition of the operating track systems as well as at selecting the best system installed. The conventional resilient wheels currently used on Viennese metro trains are planned to be substituted with massive wheels to increase performance, reduce costs and increase safety in regard to noise and vibration. Since the beginning of the project, specific trains were equipped with massive wheels and let circulate. Nine measuring systems were installed within the metro network in the form of monitoring stations to continuously record data. Radio-frequency identification (RFID) tags installed on the trains enable the identification of the aforementioned special trains when crossing the monitoring station. Selected indicators are permanently measured, statistically post-processed and transmitted in real time to our headquarter. On the basis of approximately 2,000 daily recorded trains, statistical signal processing has been performed thanks to this integrated structural health monitoring system, thus providing information on train condition and on the impact of massive wheels. In parallel to this type of investigation, impulse tests under specific conditions were performed in all the nine train stations with the scope of assessing the conditions of track systems, of comparing them and of discriminating among them in order to indicate the highest performance based on real-time data for the installed track systems. The integrated continuous monitoring system as well as the analysis of the big data collected well fit into the context of real-time diagnostics and remote sensing of infrastructures.


DOI
10.12783/shm2019/32150

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