A robust complementary index for railway maintenance planning based on a probabilistic approach

References

• Åhrén T. Maintenance performance indicators (MPIs) for railway infrastructure: identification and analysis for improvement. Luleå, Sweden: Luleå University of Technology; 2008.
   Google Scholar
• Tzanakakis K. The railway track and its long term behaviour: A handbook for a railway track of high quality. In: Roess RP, editor. Berlin: Springer Berlin Heidelberg; 2013. p. 395.
   Google Scholar
• BSI Group. BS EN 13306:2010 maintenance. Maintenance terminology. London: BSI Stand. Publ; 2010. 36. Sep.
   Google Scholar
• BSI Group. British Standard 13848-5:2008+A1:2010 Railway applications. Track. Track geometry quality. Part 5: geometric quality levels. Plain line; 2010 p. 26.
   Google Scholar
• Soleimanmeigouni I, Ahmadi A, Kumar U. Track geometry degradation and maintenance modelling: A review. Proc Inst Mech Eng Part F J Rail Rapid Transit. 2018;232(1):73–102.
   Web of Science ®Google Scholar
• Vale C, Ribeiro IM, Calçada R. Integer programming to optimize tamping in railway tracks as preventive maintenance. J Transp Eng. 2012;138(1):123–131.
   Web of Science ®Google Scholar
• Abdur Rohim Boy Berawi. Improving railway track maintenance using Power Spectral Density (PSD). Porto, Portugal: University of porto; 2013.
   Google Scholar
• Satish Chandra A. Railway engineering. Vol. 1. New Delhi: Oxford University Press; 2007.
   Google Scholar
• Movaghar M, Mohammadzadeh S. Intelligent index for railway track quality evaluation based on Bayesian approaches. Struct Infrastruct Eng. 2020;16(7):968–986.
   Web of Science ®Google Scholar
• Bakhtiary A, Zakeri JA, Mohammadzadeh S. An opportunistic preventive maintenance policy for tamping scheduling of railway tracks. Int J Rail Transp. 2020. DOI:10.1080/23248378.2020.1737256
   Web of Science ®Google Scholar
• Falamarzi A, Moridpour S, Nazem M. A time-based track quality index: melbourne tram case study. Int J Rail Transp. 2019. DOI:10.1080/23248378.2019.1703838
   Google Scholar
• Stenström C, Parida A, Lundberg J, et al. Development of an integrity index for benchmarking and monitoring rail infrastructure: application of composite indicators. Int J Rail Transp. 2015;3(2):61–80.
   Web of Science ®Google Scholar
• Shafiee M, Patriksson M, Chukova S. An optimal age-usage maintenance strategy containing a failure penalty for application to railway tracks. Proc Inst Mech Eng Part F J Rail Rapid Transit. 2016;230(2):407–417.
   Web of Science ®Google Scholar
• Famurewa SM, Juntti U, Nissen A, et al. Augmented utilisation of possession time: analysis for track geometry maintenance. Proc Inst Mech Eng Part F J Rail Rapid Transit. 2016;230(4):1118–1130.
   Web of Science ®Google Scholar
• Mohammadi R, He Q, Ghofrani F, et al. Exploring the impact of foot-by-foot track geometry on the occurrence of rail defects. Transp Res Part C Emerg Technol. 2019;102:153–172.
   Web of Science ®Google Scholar
• Bai L, Liu R, Sun Q, et al. Markov-based model for the prediction of railway track irregularities. Proc Inst Mech Eng Part F J Rail Rapid Transit. 2015;229(2):150–159.
   Web of Science ®Google Scholar
• Higgins C, Liu X. Modeling of track geometry degradation and decisions on safety and maintenance: A literature review and possible future research directions. Proc Inst Mech Eng Part F J Rail Rapid Transit. 2018;232(5):1385–1397.
   Web of Science ®Google Scholar
• Khouy IA, Schunnesson H, Juntti U, et al. Evaluation of track geometry maintenance for a heavy haul railroad in Sweden: A case study. Proc Inst Mech Eng Part F J Rail Rapid Transit. 2014;228(5):496–503.
   Web of Science ®Google Scholar
• Jin X, Xiao X, Ling L, et al. Study on safety boundary for high-speed train running in severe environments. Int J Rail Transp. 2013;1(1–2):87–108.
   Google Scholar
• Famurewa SM, Asplund M, Rantatalo M, et al. Maintenance analysis for continuous improvement of railway infrastructure performance. Struct Infrastruct Eng. 2015;11(7):957–969.
   Web of Science ®Google Scholar
• Marquez FPG, Weston P, Roberts C. Failure analysis and diagnostics for railway trackside equipment. Eng Fail Anal. 2007;14(8):1411–1426.
   Web of Science ®Google Scholar
• Zhang T, Andrews J, Wang R. Optimal scheduling of track maintenance on a railway network. Qual Reliab Eng Int. 2013;29(2):285–297.
   Web of Science ®Google Scholar
• Esveld C. Investigation of the train accident on 22 July 2004 near Pamukova, Turkey. Pamukova: Esveld Consult. Serv. BV; 2004.
   Google Scholar
• Takai H, Uchida M, Muramatsu H, et al. Derailment safety evaluation by analytic equations. Q Rep RTRI. 2002;43(3):119–124.
   Google Scholar
• Zakar F, Mueller E. Investigation of a Columbus, Ohio train derailment caused by fractured rail. Case Stud Eng Fail Anal. 2016;7:41–49.
   Google Scholar
• Ishida H, Miyamoto T, Maebashi E, et al. Safety assessment for flange climb derailment of trains running at low speeds on sharp curves. Q Rep RTRI. 2006;47(2):65–71.
   Google Scholar
• Zeng J, Wu P. Study on the wheel/rail interaction and derailment safety. Wear. 2008;265(9–10):1452–1459.
   Web of Science ®Google Scholar
• Mohammadzadeh S, Ghahremani S. Estimation of train derailment probability using rail profile alterations. Struct Infrastruct Eng. 2012;8(11):1034–1053.
   Web of Science ®Google Scholar
• Zhang W, Wu P, Wu X, et al. An investigation into structural failures of Chinese high-speed trains. Eng Fail Anal. 2006;13(3):427–441.
   Web of Science ®Google Scholar
• Klinger C, Bettge D. Axle fracture of an ICE3 high speed train. Eng Fail Anal. 2013;35(15):66–81.
   Google Scholar
• Wu X, Chi M, Gao H, et al. The study of post-derailment measures to limit the extent of a derailment. Proc Inst Mech Eng Part F J Rail Rapid Transit. 2016;230(1):64–76.
   Web of Science ®Google Scholar
• Guo L, Wang K, Lin J, et al. Study of the post-derailment safety measures on low-speed derailment tests. Veh Syst Dyn. 2016;54(7):943–962.
   Web of Science ®Google Scholar
• Arvidsson T, Andersson A, Karoumi R. Train running safety on non-ballasted bridges. Int J Rail Transp. 2019;7(1):1–22.
   Web of Science ®Google Scholar
• Jafarian E, Rezvani MA. Application of fuzzy fault tree analysis for evaluation of railway safety risks: an evaluation of root causes for passenger train derailment. Proc Inst Mech Eng Part F J Rail Rapid Transit. 2012;226(1):14–25.
   Web of Science ®Google Scholar
• Mohammadzadeh S, Sangtarashha M, Molatefi H. A novel method to estimate derailment probability due to track geometric irregularities using reliability techniques and advanced simulation methods. Arch Appl Mech. 2011;81(11):1621–1637.
   Web of Science ®Google Scholar
• Mohammadzadeh S, Sharavi M, Keshavarzian H. Reliability analysis of fatigue crack initiation of railhead in bolted rail joint. Eng Fail Anal. 2013;29:132–148.
   Web of Science ®Google Scholar
• Iwnicki SD, Stow J, Parkinson H. Assessing railway vehicle derailment potential using neural networks. Proceedings of the International Conference on Fault-free Infrastructure. Derby; 1999. p. 147–155.
   Google Scholar
• Kaeeni S, Khalilian M, Mohammadzadeh J. Derailment accident risk assessment based on ensemble classification method. Saf Sci. 2018;110:3–10.
   Web of Science ®Google Scholar
• Peng Z, Lu Y, Miller A, et al. Risk assessment of railway transportation systems using timed fault trees. Qual Reliab Eng Int. 2016;32(1):181–194.
   Web of Science ®Google Scholar
• Kardas-Cinal E. Comparative study of running safety and ride comfort of railway vehicle. Pr Nauk Politech Warsz. 2009;71(2):75–84.
   Google Scholar
• Andrade AR, Stow J. Statistical modelling of wear and damage trajectories of railway wheelsets. Qual Reliab Eng Int. 2016;32(8):2909–2923.
   Web of Science ®Google Scholar
• Muller G, Albertini G, Molinari J-F, et al. A probabilistic approach to design civil engineering structures. Swiss Fed Inst Technol. Lausanne. 2015;57. https://lsms.epfl.ch/files/content/sites/lsms/files/education/projects/A_probabilistic_approach_to_design_civil_engineering_structures.pdf
   Google Scholar
• Consortium TS. Smart maintenance, analysis and remediation of transport infrastructure. Dublin: SMART-RAIL, 2014.
   Google Scholar
• Lopes Gerum PC, Altay A, Baykal-Gürsoy M. Data-driven predictive maintenance scheduling policies for railways. Transp Res Part C Emerg Technol. 2019;107:137–154.
   Web of Science ®Google Scholar
• Lasisi A, Attoh-Okine N. Principal components analysis and track quality index: A machine learning approach. Transp Res Part C Emerg Technol. 2018;91:230–248.
   Web of Science ®Google Scholar
• Attoh-Okine N. Big data challenges in railway engineering. 2014 IEEE Int Conf Big Data (Big Data). IEEE; 2014. p. 7–9. http://ieeexplore.ieee.org/document/7004424/.
   Google Scholar
• Wu H, Wilson N. Handbook of railway vehicle dynamics. Iwnicki S, editor. Boca Raton (FL): Taylor Fr. Gr. CRC Press, 2006.
   Google Scholar
• Kuo C-M, Lin -C-C. Analysis of derailment criteria. Proc Inst Mech Eng Part F J Rail Rapid Transit. 2016;230(4):1158–1163.
   Web of Science ®Google Scholar
• Nadal MJ. Locomotives à vapeur. Collection Encyclopedie Scientifique, editor. Paris: Biblioteque de Mecanique Appliquee et Genie; 1908.
   Google Scholar
• Matsumoto A, Sato Y, Ohno H, et al. Continuous observation of wheel/rail contact forces in curved track and theoretical considerations. Veh Syst Dyn. 2012;50(1):349–364.
   Google Scholar
• UIC Code 518. Testing and approval of railway vehicles from the point of view of their dynamic behaviour - Safety - Track fatigue - Running behaviour; 2009. p. 126.
   Google Scholar
• Choi S-K, Grandhi RV, Canfield RA. Reliability-based structural design. London: Springer; 2006.
   Google Scholar
• Billinton R, Allan RN. Reliability evaluation of engineering systems. Second. Electron. Power. Boston, MA: Springer US; 1992.
   Google Scholar
• Janatabadi F. Probabilistic safety assessment for train’s performance under the effect of track geometry condition. Tehran, Iran: Iran University of Science and technology; 2018.
   Google Scholar
• Mundrey JS. Railway track engineering. 4th ed. New Delhi: McGraw Hill Education; 2009.
   Google Scholar