Towards an energy-based indicator of track quality in turnouts

Seyed Mohammad Asadzadeh*, Roberto Galeazzi, Pegah Barkhordari

*Corresponding author for this work

Research output: Contribution to journalConference articleResearchpeer-review

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This paper investigates track vibration energy as a potential novel indicator of turnout's track quality. Exploiting measurements of train-induced track vertical accelerations at different sections of a turnout, the track vibration energy is estimated and its variation over time analysed through the creation of statistical empirical distributions. A clear increase in vibration energy can be observed over a period of two years. An analysis of the turnout track geometry through a standard indicator adopted by the railway industry is then performed, and an increase in longitudinal level over the same investigation period clearly indicates track degradation due to cumulative loading. Last, a correlation analysis is performed between the estimated vibration energy and the indicator of track quality based on geometry data. Such analysis shows a significant correlation between the two indexes, thereby addressing the possibility of developing a novel condition monitoring tool for track quality based on track vibration energy. The whole investigation is based on full-scale measurements of track vertical acceleration and track geometry performed over a period of two years in a turnout of the Danish railway infrastructure.

Original languageEnglish
Book seriesIFAC-PapersOnLine
Issue number2
Pages (from-to)8482-8487
Publication statusPublished - 2020
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020


Conference21st IFAC World Congress 2020

Bibliographical note

Funding Information:
The authors gratefully acknowledge the collaboration with Banedanmark during the INTELLISWITCH project (2015-2019), which provided data from the Danish railway infrastructure. The authors are also grateful to the Danish Meteorological Institute for providing meteorological data.

Publisher Copyright:
Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license


  • Condition monitoring
  • Data fusion
  • Statistical methods for FDI
  • Time series modeling
  • Track quality estimation


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