Estimation of the railway equivalent conicity under different contact adhesion levels and with no wheelset sensorization

References

• Strano S, Terzo M. 36665317300;25634884900; review on model-based methods for on-board condition monitoring in railway vehicle dynamics. Advances in mechanical engineering. 2019;11(2) .DOI: 10.1177/1687814019826795
   Web of Science ®Google Scholar
• Association of Train Operating Companies, < http://www.atoc.org>.
   Google Scholar
• Li C, Luo S, Cole C, et al. An overview: modern techniques for railway vehicle on-board health monitoring systems. Veh Syst Dyn. 2017;55(7):1045–1070.
   Web of Science ®Google Scholar
• UIC Code 518. Testing and approval of railway vehicles fromthe point of view of their dynamic behaviour – safety – track fatigue – running behaviour. 4th ed. Paris: International Union of Railways; 2009.
   Google Scholar
• EN BS. 3. Railway applications – testing for the acceptance of running characteristics of railway vehicles – testing of running behaviour and stationary tests. 1st ed Brussels: CEN; 1436; 2005.
   Google Scholar
• Shi H, Wang J, Wu P, et al. Field measurements of the evolution of wheel wear and vehicle dynamics for high-speed trains. Veh Syst Dyn. 2018;56(8):1187–1206. doi:10.1080/00423114.2017.1406963.
   Web of Science ®Google Scholar
• Xu P, et al. Condition monitoring of wheel wear for high-speed trains: A data-driven approach. 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), Seattle, WA; 2018. pp. 1–8, doi: 10.1109/ICPHM.2018.8448864.
   Google Scholar
• Mei T, Ding X. Condition monitoring of rail vehicle suspensions based on changes in system dynamic interactions. Veh Syst Dyn. 2009;47:1167–1181.
   Web of Science ®Google Scholar
• Masoumi H, Vermeulen F, Vanhonacker T. Wheelset condition monitoring based on pass-by vibration signals. IJRARE. 2018;5(2):1–8. URL: http://ijrare.iust.ac.ir/article-1-188-en.html
   Google Scholar
• Li P, Goodall R, Weston P, et al. Estimation of railway vehicle suspension parameters for condition monitoring. Control Eng Pract. 2007;15(1):43–55. ISSN 0967-0661, doi:10.1016/j.conengprac.2006.02.021
   Web of Science ®Google Scholar
• Simon D. Optimal state estimation: kalman, H infinity, and nonlinear approaches. John Wiley and Sons Inc; 2006.
   Google Scholar
• Xu B, Guan X, Zhou H, et al. Lateral contact forces estimation of locomotive wheelsets using a model-based filter for condition monitoring. Insight - Non-Destructive Testing and Condition Monitoring. 2013;55(11):614–620. (7); The British Institute of Non-Destructive Testing; DOI: doi:10.1784/insi.2012.55.11.614
   Google Scholar
• Strano S, Terzo M. On the real-time estimation of the wheel-rail contact force by means of a new nonlinear estimator design model, mechanical systems and signal processing. 2018;105:391–403.
   Google Scholar
• Niola V, Strano S, Terzo M. A Random Walk Model Approach for the Wheel-Rail Contact Force Estimation. ASME. J. Dyn. Sys.,: Meas., Control. 2018;140(7):071016.
   Web of Science ®Google Scholar
• Kaiser I, Strano S, Terzo M, et al. Anti-yaw damping monitoring of railway secondary suspension through a nonlinear constrained approach integrated with a randomly variable wheel-rail interaction. Mech Syst Signal Process. 2021;146:107040.
   Web of Science ®Google Scholar
• Zhang Z, Xu B, Ma L, et al. Parameter estimation of a railway vehicle running bogie using extended Kalman filter). Proceedings of the: 33r), d Chinese Control Conference; 2014; Nanjing, pp. 3393-3398.
   Google Scholar
• Mandela R, Kuppuraj V, Rengaswmy R, et al. Constrained unscented recursive estimator for nonlinear dynamic systems. J Process Control. 2012;22(4):718–728.
   Web of Science ®Google Scholar
• Kolas S, Foss B, Schei T. Constrained nonlinear state estimation based on the UKF approach. Comput Chem Eng. 2009;33(8):1386–1401.
   Web of Science ®Google Scholar
• Strano S, Terzo M. Constrained nonlinear filter for vehicle sideslip angle estimation with no a priori knowledge of tyre characteristics, control Engineering practice. 2018;71:10–17.
   Google Scholar
• Calabrese A, Strano S, Terzo M. Adaptive constrained unscented Kalman filtering for real-time nonlinear structural system identification. Struct Cont Healt Monitor. 2018;25:e2084. doi:10.1002/stc.2084
   Web of Science ®Google Scholar
• Calabrese A, Gandelli E, Quaglini V, et al. Monitoring of hysteretic friction degradation of curved surface sliders through a nonlinear constrained estimator. Eng Struct. 2021;226:111371), ISSN 0141-0296
   Web of Science ®Google Scholar
• Simulia. (2019). “Multi-Body Simulation Software”, https://www.3ds.com/products-services/simulia/training/course-descriptions/simpack-rail/.
   Google Scholar
• Julier SJ, Uhlrnann JK, Durrant-Whyte HF. A new approach for filtering nonlinear systems). Proceedings of the American Control Conference; 1995; Seattle, Washington.
   Google Scholar
• Julier S. The scaled Unscented Transformation). Proceedings of the 2002 American Control Conference; 2002; 6, Anchorage, AK.
   Google Scholar
• Kaiser I, Poll G, Voss G, et al. The impact of structural flexibilities of wheelsets and rails on the hunting behaviour of a railway vehicle. Veh Syst Dyn. 2019;57(4):564–594. doi:10.1080/00423114.2018.1484933.
   Web of Science ®Google Scholar
• Garg VK, Dukkipati RV. Dynamics of Railway Vehicle Systems. Don Millis, ON, Canada: Academic Press; 1984.
   Google Scholar
• Iwnicki S. Manchester benchmarks for rail vehicle simulation. Veh Syst Dyn. 1998;30:3–4. 295-313, DOI: 10.1080/00423119808969454
   Web of Science ®Google Scholar
• European Railway Research Institute, ERRI B 176/DT 290, B 176 / 3 Benchmark Problem – Results and Assessment, Utrecht 1993
   Google Scholar
• Zong L-H, Gong X-L, Xuan S-H, et al. Semi-active H∞ control of high-speed railway vehicle suspension with magnetorheological dampers. Veh Syst Dyn. 2013;51(5):600–626. doi:10.1080/00423114.2012.758858.
   Web of Science ®Google Scholar
• Kalker JJ. The computation of three-dimensional rolling contact with dry friction. Int J Numer Methods Eng. 1979;14:1293–1307.
   Web of Science ®Google Scholar
• Kalker JJ. Numerical calculation of the elastic field in a half-space. Commun Appl Numer Methods. 1986;2:401–410.
   Web of Science ®Google Scholar
• Zoljic-Beglerovic S, Stettinger G, Luber B, et al. Railway suspension system fault diagnosis using cubature Kalman filter techniques. IFAC-PapersOnLine. 2018;51(24):1330–1335. ISSN 2405-8963, doi:10.1016/j.ifacol.2018.09.562.
   Google Scholar
• Bosso N, Zampieri N. Numerical stability of co-simulation approaches to evaluate wheel profile evolution due to wear. International Journal of Rail Transportation. 2020: 159–179. doi:10.1080/23248378.2019.1672588.
   Web of Science ®Google Scholar
• Bruni S, Vinolas J, Berg M, et al. Modelling of suspension components in a rail vehicle dynamics context. Veh Syst Dyn. 2011;49(7):1021–1072. doi:10.1080/00423114.2011.586430.
   Web of Science ®Google Scholar
• Iwnicki S, Iwnicki S. Handbook of Railway Vehicle Dynamics (1st ed.). x: CRC Press; 2006. doi:10.1201/9781420004892
   Google Scholar
• Wickens AH. Fundamentals of Rail Vehicle Dynamics: Guidance and Stability. The Netherlands: Swets & Zeitlinger B.V.; 2003.
   Google Scholar
• Polach O. Characteristic parameters of nonlinear wheel/rail contact geometry. Veh Syst Dyn. 2010;48(sup1):19–36. DOI: 10.1080/00423111003668203.
   Web of Science ®Google Scholar
• J Nkcooperrider, E Khedrick, C Hlaw, et al. The application of quasi-linearization to the prediction of nonlinear railway vehicle response. Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility. 1975;4(2–3):141–148. DOI: 10.1080/00423117508968479.
   Google Scholar
• Bosso N, Gugliotta A, Somà A, et al. Adhesion force estimation on 1/5 scaled test rig. Proceedings of the ECCOMAS Multibody Dynamics 2009 Conference; Warsaw, Poland.
   Google Scholar