Analyzing Major Track Quality Indices and Introducing a Universally Applicable TQI
Source abstract
Abstract
Adequate railway track condition is a prerequisite for safe and reliable railway operation. Many track quality indices (TQIs) have been developed with the aim of assessing the track condition holistically. These indices combine measurement signals of some or all relevant geometry parameters with different mathematical models. In this paper, a selection of important TQIs is evaluated. Using measurement data of a five kilometer track section, the indices are calculated and their properties are discussed. This study reveals that all indices exhibit drawbacks to varying degrees. As a consequence, a new index has been developed—the track quality index of Graz University of Technology (TUG_TQI). Its favorable characteristics are presented by means of the above-mentioned test section. The TUG_TQI combines all relevant track geometry parameters, which are normalized beforehand to eliminate over or underrepresentation of different parameters. Thus, the index reliably describes the overall geometrical track quality.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Railway Engineering and Dynamics · Railway Systems and Energy Efficiency
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Railway Engineering and Transport Economy, Graz University of Technology, 8010 Graz, Austria
References (38)
Show source-provided references
1. A modelling framework for railway infrastructure reliability analysis
2. Yuan, F.Q. (2016, January 4–7). Overall reliability index development for railway infrastructure and rolling stock with case study. Proceedings of the 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia.10.1109/IEEM.2016.7798081 ↗
3. Reliability, availability and maintainability analyses for railway infrastructure management10.1080/15732479.2019.1615964 ↗
4. Railway Infrastructure Maintenance - A Survey of Planning Problems and Conducted Research10.1016/j.trpro.2015.09.011 ↗
5. Wang, X., Yang, J., Wang, J., Wang, Y., and Xu, G. (2020). An Incentive Factor-Based Dynamic Comprehensive Evaluation on a High-Speed Railway Track. Appl. Sci., 10.10.3390/app10165546 ↗
6. Rail defects: An overview10.1046/j.1460-2695.2003.00693.x ↗
7. Effect of Sleeper Interventions on Railway Track Performance10.1061/(ASCE)GT.1943-5606.0002022 ↗
8. Developing Track Ballast Characteristic Guideline in Order to Evaluate Its Performance10.7782/IJR.2016.9.2.027 ↗
9. Advances in design theories of high-speed railway ballastless tracks10.1007/BF03325753 ↗
10. Austrian Standards Institute (2014). EN 13848-6: Railway Applications–Track–Track Geometry Quality–Part6: Characterisation of Track Geometry Quality, Austrian Standards Institute. Available online: https://standards.iteh.ai/catalog/standards/cen/741d0eac-5fda-472d-842c-0f78e01a1d47/en-13848-6-2014.
11. Assessment of the Track Geometry Quality from the Aspect of Safe and Reliable Operation of the Railway Track10.1016/j.proeng.2015.07.099 ↗
12. Evaluating track geometrical quality through different methodologies
13. Prediction of railway track geometry defects: A case study10.1080/15732479.2019.1679193 ↗
14. Track geometry degradation and maintenance modelling: A review10.1177/0954409716657849 ↗
15. Khajehei, H., Ahmadi, A., and Soleimanmeigouni, I. (2018, September 13). Forecasting Railway Track Geometry Condition Using Neural Network Approach. Available online: https://www.researchgate.net/publication/320170135_Forecasting_Railway_Track_Geometry_Condition_Using_Neural_Network_Approach.
16. Ballast Condition Monitoring for Turnouts Using Power Spectral Density10.1061/JTEPBS.0000433 ↗
17. Fractal analysis as an innovative approach for evaluating the condition of railway tracks10.1177/0954409718795763 ↗
18. Development of Railway Track Geometry Indexes Based on Statistical Distribution of Geometry Data10.1061/(ASCE)0733-947X(2010)136:8(693) ↗
19. Establishment of Track Quality Index Standard Recommendations for Beijing Metro
20. A stochastic model for railway track asset management10.1016/j.ress.2014.04.021 ↗
21. Vidovic, I., and Marschnig, S. (2020). Optical Fibres for Condition Monitoring of Railway Infrastructure—Encouraging Data Source or Errant Effort?. Appl. Sci., 10.10.3390/app10176016 ↗
22. Investigation of the effect of the inspection intervals on the track geometry condition10.1080/15732479.2019.1687528 ↗
23. Austrian Standards Institute (2019). EN 13848-1: Railway Applications-Track-Track Geometry Quality-Part 1: Characterization of Track Geometry, Austrian Standards Institute.
24. Preparing Track Geometry Data for Automated Maintenance Planning10.1061/JTEPBS.0000349 ↗
25. Objective Track Quality Indices10.3141/1863-11 ↗
26. Austrian Standards Institute (2017). EN 13848-5: Railway Applications-Track-Track Geometry Quality-Part 5: Geometrical Quality Levels-Plain Line, Switches and Crossings, Austrian Standards Institute.
27. Luber, B. (2011). Methode zur Bewertung von Gleislageabweichungen auf Basis von Fahrzeugreaktionen, Graz University of Technology.
28. Hummitzsch, R. (2009). Zur Prognostizierbarkeit des Qualitätsverhaltens von Gleisen: Statistische Analyse des Gleisverhaltens zur Erstellung eines Prognosemodells, TU Graz.
29. Mlinarić, T.J. (2002). Dugoročna Procjena Kvalitete Kolosiječne Geometrije s Ciljem Identificiranja Zahtjeva Održavanja, University of Zagreb.
30. Madejski, J., and Grabczyk, J. (2002, January 19–22). Continuous Geometry Measurement for Diagnostics of Tracks and Switches. Proceedings of the International Conference on Swithches, Delft, The Netherlands.
31. Andersson, M. (2020, October 23). Strategic Planning of Track Maintenance, Borlänge. Available online: http://www4.banverket.se/raildokuffe/pdf/MC0228.pdf.
32. (2020, October 28). Jernbanekompetanse.no, Textbooks in Railway Technology. Available online: https://www.jernbanekompetanse.no/wiki/Tilstandskontroll.
33. Berawi, A.R.B. (2013). Improving Railway Track Maintenance Using Power Spectral Density (PSD), University of Porto.
34. Use of Track Geometry Measurements for Maintenance Planning
35. Yu-Jiang, P., Zhang, P., El-Sibaie, M., and Sung, L. (2004, January 19–22). FRA Track Quality Indices and Distribution Characteristics. Proceedings of the AREMA Annual Conference, Nashville, TN, USA.
36. Lichtberger, B. (2005). Track Compendium, Eurailpress Tetzlaff-Hestra GmbH & Co.
37. Austrian Standards Institute (2017). EN 13803: Railway Applications-Track-Track Alignment Design Parameters-Track Gauges 1435 mm and Wider, Austrian Standards Institute.
38. Hansmann, F. (2015). Innovative Messdatenanalyse–ein Beitrag für ein Nachhaltiges Anlagenmanagement Gleis, Graz University of Technology. Available online: https://online.tugraz.at/tug_online/wbAbs.showThesis?pThesisNr=58734&pOrgNr=1228#.