Combining wavelet analysis of track irregularities and vehicle dynamics simulations to assess derailment risks

References

• 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
• Zhang T, Andrews J, Wang R. Optimal scheduling of track maintenance on a railway network. Quality Reliab Eng Int. 2013;29(2):285–297.
   Web of Science ®Google Scholar
• BSI. European Standard EN 13848-1: Railway applications – track – track geometry quality – Part 1: characterisation of track geometry; 2019.
   Google Scholar
• BSI. European Standard EN 13848-2: Railway applications – track – track geometry quality – Part 2: measuring systems – track recording vehicles; 2020.
   Google Scholar
• BSI. European Standard EN 13848-3: Railway applications – track – track geometry quality – Part 3: measuring systems – track construction and maintenance machines; 2009.
   Google Scholar
• BSI. European Standard EN 13848-4: Railway applications – track – track geometry quality – Part 4: measuring systems – manual and lightweight devices; 2011.
   Google Scholar
• BSI. European Standard EN 13848: Railway applications – track – track geometry quality – Part 5: geometric quality levels; 2017.
   Google Scholar
• BSI. European Standard EN 13848: Railway applications – track – track geometry quality – Part 6: characterisation of track geometry quality; 2014.
   Google Scholar
• Esveld C. Modern railway track. 2nd ed. Zaltbommel, The Netherlands: MRT Productions; 2001.
   Google Scholar
• Li MXD, Berggren EG, Berg M. Assessment of vertical track geometry quality based on simulations of dynamic track-vehicle interaction. Proc Inst Mech Eng, Part F: J Rail Rapid Transit. 2009;223(2):131–139.
   Web of Science ®Google Scholar
• Balouchi F, Bevan A, Formston R. Development of railway track condition monitoring from multi-train in-service vehicles. Veh Syst Dyn. 2020;59:1397–1417.
   Web of Science ®Google Scholar
• Baasch B, Heusel J, Roth M, et al. Train wheel condition monitoring via cepstral analysis of axle box accelerations. Appl Sci. 2021;11(4):1432.
   Google Scholar
• Karis T, Berg M, Stichel S, et al. Correlation of track irregularities and vehicle responses based on measured data. Veh Syst Dyn. 2018;56(6):967–981.
   Web of Science ®Google Scholar
• Popović Z, Trpčevski F, Pančić I, et al. Harmonization of European track quality. Gradjevinski Materijali I Konstrukcije. 2014;57(1):29–44.
   Google Scholar
• Li C, Wang P, Gao T, et al. Spatial–temporal model to identify the deformation of underlying high-speed railway infrastructure. J Trans Eng Part A: Syst. 2020;146(8):04020084.
   Web of Science ®Google Scholar
• BSI. European standard EN 14363-1: railway applications – testing and simulation for the acceptance of running characteristics of railway vehicles – running Behaviour and stationary tests; 2016.
   Google Scholar
• Bae SJ, Mun BM, Chang W, et al. Condition monitoring of a steam turbine generator using wavelet spectrum based control chart. Reliab Eng Syst Saf. 2019;184:13–20.
   Web of Science ®Google Scholar
• Jimenez AA, Gomez Munoz CQ, Garcia Marquez FP. Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliab Eng Syst Saf. 2019;184:2–12.
   Web of Science ®Google Scholar
• Xiao S, Lu Z, Wang P. Multivariate global sensitivity analysis for dynamic models based on wavelet analysis. Reliab Eng Syst Saf. 2018;170:20–30.
   Web of Science ®Google Scholar
• Salvador P, Naranjo V, Insa R, et al. Axlebox accelerations: their acquisition and time-frequency characterisation for railway track monitoring purposes. Measurement. 2016;82:301–312.
   Web of Science ®Google Scholar
• Caprioli A, Cigada A, Raveglia D. Rail inspection in track maintenance: A benchmark between the wavelet approach and the more conventional Fourier analysis. Mech Syst Signal Process. 2007;21(2):631–652.
   Web of Science ®Google Scholar
• Toliyat HA, Member S, Abbaszadeh K, et al. Packet decomposition. IEEE Trans Ind Appl. 2003;39(5):1454–1461.
   Web of Science ®Google Scholar
• Hopkins BM, Taheri S. Track Health Monitoring Using Wavelets. In: Proceedings of the ASME 2010 Rail Transportation Division Fall Technical Conference.Roanoke, Virginia, USA. October 12-13.2010. p. 9–15.
   Google Scholar
• Shah AA, Chowdhry BS, Memon TD, et al. Real time identification of railway track surface faults using canny edge detector and 2d discrete wavelet transform. Ann Emerg Technol Comput. 2020;4(2):53–60.
   Google Scholar
• Gomez MJ, Castejon C, Corral E, et al. Railway axle condition monitoring technique based on wavelet packet transform features and support vector machines. Sensors. 2020;20(12):3575.
   PubMed Web of Science ®Google Scholar
• Ahuja N, Lertrattanapanich S, Bose N. Properties determining choice of mother wavelet. IEE Proc Vis Image Signal Process. 2005;152(5):659-–664.
   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
• Costa J, Ambrósio J, Frey D, et al. A multivariate statistical representation of railway track irregularities using arma models. Veh Syst Dyn. 2021;1–17.
   Web of Science ®Google Scholar
• Scalea FLd, McNamara J. Wavelet transform for characterizing longitudinal and lateral transient vibrations of railroad tracks. Res Nondestruct Eval. 2004;15(2):87–98.
   Web of Science ®Google Scholar
• Zhiping Z, Fei L, Yong Z. Wavelet analysis of track profile irregularity for Beijing-Tianjin intercity high-speed railway on bridge. In: 2010 International Conference on Intelligent Computation Technology and Automation, Changsha, Hunan, China, Vol. 3, IEEE; 2010. p. 1155–1158.
   Google Scholar
• Zhang X, Feng N, Wang Y, et al. Acoustic emission detection of rail defect based on wavelet transform and Shannon entropy. J Sound Vib. 2015;339:419–432.
   Web of Science ®Google Scholar
• Koziol P. Experimental validation of wavelet based solution for dynamic response of railway track subjected to a moving train. Mech Syst Signal Process. 2016;79:174–181.
   Web of Science ®Google Scholar
• Xu L, Zhai W, Chen Z. On use of characteristic wavelengths of track irregularities to predict track portions with deteriorated wheel/rail forces. Mech Syst Signal Process. 2018;104:264–278.
   Web of Science ®Google Scholar
• Percival DB, Sardy S, Davison AC. Wavestrapping time series: adaptive wavelet-based bootstrapping. Nonlinear Nonstationary Signal Process. 2000;1–29.
   Google Scholar
• Andrade AR, Johnson T, Stow J. Application of wavestrapping statistical technique to estimate an extreme value in train aerodynamics. J Wind Eng Ind Aerodyn. 2018;175:419–427.
   Web of Science ®Google Scholar
• Vidakovic B. Statistical modeling by wavelets. 1st ed. New York (NY): John Wiley & Sons Inc.; 1999.
   Google Scholar
• Singh BN, Tiwari AK. Optimal selection of wavelet basis function applied to ECG signal denoising. Digit Signal Process: A Rev J. 2006;16(3):275–287.
   Web of Science ®Google Scholar
• Ahadi M, Bakhtiar MS. Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to acoustic emission signals. Appl Acoust. 2010;71(7):634–639.
   Web of Science ®Google Scholar
• Yang L, Judd M, Bennoch CJ. Denoising uhf signal for pd detection in transformers based on wavelet technique. Vol. 11; 2004. p. 166–169.
   Google Scholar
• Tsui PPC, Basir OA. Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification. IEEE Trans Ind Appl. 2006;45(1–4):1–14.
   Google Scholar
• Rafiee J, Tse PW. Use of autocorrelation of wavelet coefficients for fault diagnosis. Mech Syst Signal Process. 2009;23(5):1554–1572.
   Web of Science ®Google Scholar
• He H, Tan Y, Wang Y. Optimal base wavelet selection for ECG noise reduction using a comprehensive entropy criterion. Entropy. 2015;17(9):6093–6109.
   Web of Science ®Google Scholar
• Oliveira HM. Shannon and Renyi entropy of wavelets. Int J Math Comput Sci. 2015;10(1):13–26.
   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
• Nadal J. Locomotives a vapeur: collection encyclopedie scientifique. Paris: Biblioteque de Mecanique Appliquee et Genie; 1908.
   Google Scholar
• Pagaimo J, Magalhães H, Costa J, et al. Derailment study of railway cargo vehicles using a response surface methodology. Veh Syst Dyn. 2020;60:1–26.
   Web of Science ®Google Scholar
• SNC-Lavalin. VAMPIRE Pro Manual – V7.00; 2020.
   Google Scholar
• Antunes P, Magalhães H, Ambrosio J, et al. A co-simulation approach to the wheel–rail contact with flexible railway track. Multibody Syst Dyn. 2019;45(2):245–272.
   Web of Science ®Google Scholar
• Magalhães H. Development of advanced computational models of railway vehicles [master's thesis]. Instituto Superior Técnico; Lisbon, Portugal; 2013.
   Google Scholar