Integrated Health Monitoring for the actuation system of high-speed tilting trains



Published Nov 17, 2020
Andrea De Martin Andrea Dellacasa Giovanni Jacazio Massimo Sorli


Tilting trains are designed to reach high speed on pre-existing railroads without the need of adjusting the tracks geometry or building dedicated lines; the tilting of the carbody keeps an acceptable level of comfort by limiting the lateral acceleration felt by passengers when the train runs along curved tracks with speed higher than the balance speed built into the curve geometry. As such, they are often used to reduce travel times on routes with several curves. Tilting is performed through a position-controlled actuation system which operates according to the commands received from the train control system: in the studied configuration, the torque needed to tilt the car body with respect to the bogie is provided by a series of hydraulic actuators, while the position information used to close the control loop comes from two capacitive sensors located in the front and rear part of each vehicle. Tilt angle measurement is vital for the system operation and for ensuring a safe ride; the traditional solution in case of discrepancy between the signals of the two tilt angle sensors of any vehicle is to disable the tilting function while limiting the train speed to avoid issues during changes of direction. In a similar fashion, the failure in one (or more) of the tilting actuators would result in the loss of the tilting capability and the return to a fixed configuration operating at reduced speed. It should be noticed that the negative impact of the loss of the tilting system is not limited to the faulty train, since it might affect the entire traffic schedule on the interested lines. The paper presents an integrated Health Monitoring framework that makes intelligent use of all available information thus enhancing the system availability, allowing its operation even in presence of faulty sensors and detecting the onset of failures in the actuation system. At the same time its use can facilitate maintenance organization, simplify the spare parts logistics and provide help to the traffic management. The proposed framework has been developed taking advantage of a high-fidelity model of the physical system validated through comparison with experimental mission profiles on the Lichtenfels - Saalfeld and Battipaglia - Reggio Calabria routes, which have been used by the train manufacturer to assess the performance of their tilting trains.

Abstract 187 | PDF Downloads 195



tilting trains, Automatic diagnostics, PHM in Railways

Bishop, R.H. (2007) The Mechatronics Handbook – 2nd edition, Boca Raton, FL, USA: CRC press.
Boon, C.J., Hayes, W.F (1992) High speed rail tilt train technology: a state of the art survey. US Department of Transportation – Federal Railroad Administration.
Borello, L., Dalla Vedova, M.D.L., Jacazio, G., & Sorli, M. (2009) A prognostic model for hydraulic servovalves, Annual Conference of the Prognostics and Health Management Society, September 27 – October 1, San Diego, CA, USA.
Byington, C.S., Watson, M., Edwards, D., & Stoelting, P. (2004) A model-based approach to prognostics and health management for flight control actuators. IEEE Aerospace Conf. Proc., Vol. 6, pp. 3551-3562.
Chandra, S., Agarwal, M.M. (2013) Railway Engineering 2nd edition, Oxford, UK: Oxford University Press.
Feldman, A., Kurtoglu, T., Narashimhan, S., Poll, S., Garcia, D., de Kleer, J., Kuhn, L., & van Gemund, A. (2010) Empirical evaluation of diagnostic algorithm performance using a generic framework. International Journal of Prognostics and Health management, vol. 1, pp. 1-28.
Jacazio, G., Risso, D., Sorli, M, & Tommasini, L. (2012) Adaptive control for improved efficiency of hydraulic systems for high-speed tilting trains. Proceeding of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit, vol. 226, pp. 272-283.
Jacazio, G., Sorli, M., Bolognese, D., & Ferrara, D. (2012) Health management system for the pantographs of tilting trains. First European Conference of the Prognostics and Health Management Society, July 3-5, Dresden, Germany.
Jennions, I.K., Niculita, O., & Esperon-Miguez, M. (2016) Integrating IVHM and asset design. International Journal of Prognostics and Health Management, vol. 7 (2) 028.
Shahidi, P., Maraini, D., & Hopkins, B. (2016) Railcar diagnostics using minimal-redundancy maximum-relevance feature selection and support vector machine classification. International Journal of Prognostics and Health Management, vol.7 (Special Issue: Big Data and Analytics) 034.
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering system. Hoboken, NJ: John Wiley & Sons, Inc.
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