Basic Prediction Model of Railway Track Circuit Failure

Document Type : Scientific - Research

Author

Project Manager of Station Master Plans, Iranian Islamic Republic Railways, Tehran, Iran

Abstract

 Railway superstructure availability is a clear precondition for presenting railway services. As public demands does not accept any interrupt in the planned railway services, superstructure failure prediction will be very important in maintenance. Among the superstructures’ elements, the track circuits with direct influence on track availability, has the most complex behavior to predict its failures. Recent literature has not provide any failure time estimation for general conditions of equipment specifications, maintenance and utilizations. Most of the research activity has been devoted to evaluating signaling system in a limited space of models for operational conditions. The basic predictive model is founded on the estimation of probability distribution and reliability functions over large number of real events with the validation of basic probability theorems on estimation and standard statistical test. Tehran railway station track circuits are selected as a general track circuit for the case study, to estimate its cumulative distribution function of failures by analyzing ten years’ experience   of operation in past. The estimation is fitted on the Weibull distribution function to obtain the shape, scale and threshold parameters of the model. Experimental results indicates that at the error acceptance level of 5%, the track circuit with the mean time between failures as 2.7 days, is foreseen  to operate correctly for the next 1.7, 7.5 and 11 days after starting with the probability of 50%, 10% and 5%, respectively.

Keywords

Main Subjects

References

یقینی، مسعود، غفرانی، فائزه، ملا، سمیه، عامره ، مهدی و جوانبخت، بهاره (1394)،" تحلیل داده‌های خرابی تجهیزات علائم در راه‌آهن ایران با استفاده از تکنیک های داده کاوی" ، پژوهشنامه حمل و نقل، دوره 11، ص. 379-389
- Arno, R., Dowling, N., Fairfax, S., Schuerger, R. J. and Weber (2015) "What is RCM and how could it be applied to the critical loads?", IEEE Transactions on Industry Applications, Vol.51, Issue 3, pp.2045-2053.
-  Bruin, T.,  Verbert. K. and Babuska, R. (2017) '' Railway track circuit fault diagnosis using recurrent neural networks", IEEE Trans. on Neural Networks and Learning Systems, Vol. 28,  Issue  3, pp. 523-533.
- EEIG ERTMS Users Group (1998) "ERTMS/ETCS RAMS Requirements Specification", EEIG, 1988.
- EN 50126 (1999) "The specification and demonstration of reliability, availability, maintainability and safety (RAMS) for railways applications", Comité Européen de Normalisation Electrotechnique (CENELEC), Brussels, Belgium, 1999.
- Campbell, J. D. and Jardine, A. (2001) "Maintenance excellence: optimizing equipment life-cycle decisions", New York, Marcel Dekker.
- Chen, J., Roberts, C. and Weston, P. (2008) "Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems", Control Engineering Practice, Vol.16, pp. 585-596.
Dehardt, J. (1971) "A generalization of the Glivenko-Cantelli Theorem", The Annals of Mathematical Statistics , Vol. 42, No. 6, pp. 2050-2055.
- Filler, F. (1943) "On the Kolmogorov Smirnov Theorems for Empirical Distribution", Giorn Inst. Ital. Attuari, Vol. 4, pp. 83-91.
- Henley, E. J. and Kumamoto, H., (1991) "Probabilistic risk assessment," IEEE Press.
- Jiang  Lei, Wang, Xi  and Liu, Yi (2016) "Reliability assessment of ZPW-2000A track circuit using Bayesian network",  IEEE Int. Conf. Reliability, Maintenance and  Safety (ICRMS), pp. 1-4.
- Johnson, L. G. (1964) "The statistical treatment of fatigue experiments", NewYork, Elsevier.
-Kaplan, E. L. and Meier, P. (1958) "Nonparametric estimation from incomplete observations", J. Amer. Statist. Assn., Vol. 53 No. 282, pp. 457–481.
-  Lalouette,  J. , Caron, R., Scherb, F., Brinzei, N., Aubry, J. and  Malassé, O. (2010)  "Performance assessment of european railway signalling system superposed of the French system in the presence of failures", Lamda-Mu’ 2010, Vol. 2, La Rochelle, France, pp. 2–9.
- Marsaglia, G., Tsang, W. and Wang, J. (2003) “Evaluating Kolmogorov’s Distribution", Journal of Statistical Software Vol. 8 No. 18, pp. 1–4.
- Márquez, C. A. (2007) "The maintenance management framework",  Chapter 4, pp. 49-67, Springer.
-Novak, P., Danhel, M., Blazek, R. B., Kohlic, M. and Kubatova, H. (2017) “Predicting the life expectancy of railway fail-safe signaling systems using dynamic models with censoring", IEEE Int. Conf.  on Soft. Qual. And Sec. (QRS), pp. 329-339.
- Nystrom, B. (2009) "Use of availability concepts in the railway system”, International Journal of Performability Engineering", Vol. 5, No. 2, pp.103-118.
- Papoulis, A. and Pillai, S. U. ( 2002) "Probability, random variables, and stochastic processes", (4th ed.), Boston: McGraw-Hill.
- Patra, A.P. and Kumar, U. (2010) '' Availability analysis of railway track circuit'', Proceedings of the Institution of Mechanical Engineers Part F. Journal of Rail and Rapid Transit , Vol. 224, No. 3, pp. 169-177.
- Saadat, A. (2018) ''Basic prediction model of railway track circuit failure'', Advances in Railway Engineering, Vol. 1, No. 3, pp. 45-55.
- Saad, J. A. and Saralees, N. (2014) '' Modifications of the Weibull distribution: A review'', Reliability Engineering & System Safety, Vol. 124, pp. 32-55.
-  Qiu, S.,  Sallak, M., Schِn, W. and Cherfi-Boulanger, Z. (2014) '' Availability assessment of railway signalling systems with uncertainty analysis using Statecharts", Simul. Model.Prac. and Theory. No. 47, pp. 1-18.
- Vernez, D.  and Vuille, F. (2009) ''Method to assess and optimise dependability of complex macro-systems: Application to a railway signalling system", Safety Sci. Vol. 47, Issue 3, pp. 382–394.
-  Wang, Xi,  Guo, Jin, Jiang, Lei, Fu, Ji. and Li, Bin (2016) ''Intelligent fault diagnosis and prediction technologies for condition based maintenance of track circuit" , IEEE Int. Conf. on Intell. Rail Trans. (ICIRT), pp. 276-283.
- Weibull, W. (1951) "A statistical distribution function of wide application", ASME, paper 51-A-6.
Quarterly Journal of Transportation Engineering
Volume 11, Issue 2 - Serial Number 43
January 2020
Pages 313-331

Files

History

  • Receive Date: 14 December 2015
  • Revise Date: 11 October 2018
  • Accept Date: 15 October 2018

Share

How to cite

Statistics