Abstract
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 language | English |
---|---|
Book series | IFAC-PapersOnLine |
Volume | 53 |
Issue number | 2 |
Pages (from-to) | 8482-8487 |
ISSN | 2405-8963 |
DOIs | |
Publication status | Published - 2020 |
Event | 21st IFAC World Congress 2020 - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020 |
Conference
Conference | 21st IFAC World Congress 2020 |
---|---|
Country/Territory | Germany |
City | Berlin |
Period | 12/07/2020 → 17/07/2020 |
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
Keywords
- Condition monitoring
- Data fusion
- Statistical methods for FDI
- Time series modeling
- Track quality estimation