Abstract
Predictive maintenance is one of the technology enablers in the railway industry to get rid of fixed service intervals and switch to maintenance on demand to reduce life cycle costs. Until now inspection intervals on regular basis lead to high costs to ensure the overall goal of high availability. To strengthen the condition-triggered maintenance this work presents a cubature Kalman filter approach for parameter identification of complex railway suspension systems. In detail, the approach is designed for the identification of the spring stiffness and damping coefficient of the secondary suspension system using measurements from real world operation. The parametrization of the filter is performed in a way that the filter shows a so called consistency property which ensures a statistical correct behaviour. Furthermore the cubature Kalman filter approach shows promising properties regards computational complexity in combination with the achievable accuracy.
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References
Li, P., Goodall, R.: Model-based condition monitoring for railway vehicle systems. In: Control 2004 (2004)
Tsunashima, H., Mori, H.: Condition monitoring of railway vehicle suspension using adaptive multiple model approach. In: 2010 International Conference on Control Automation and Systems (ICCAS), pp. 584–589. IEEE (2010)
Hayashi, Y., Tsunashima, H., Marumo, Y.: Fault detection of railway vehicles using multiple model approach. In: 2006 SICEICASE International Joint Conference, pp. 2812–2817 (2006)
Jesussek, M., Ellermann, K.: Fault detection and isolation for a nonlinear railway vehicle suspension with a hybrid extended kalman flter. Veh. Syst. Dyn. 51(10), 1489–1501 (2013)
Ward, C.P., Goodall, R.M., Dixon, R.: Wheel rail profile condition monitoring. In: UKACC International Conference on Control 2010, pp. 1–6 (2010)
Liu, X., Alfi, S., Bruni, S.: An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system. Veh. Syst. Dyn. 54(6), 814–830 (2016)
Li, P., Goodall, R., Kadirkamanathan, V.: Parameter estimation of railway vehicle dynamic model using Rao-Blackwellised particle filter. In: European Control Conference (ECC), 2003, pp. 2384–2389. IEEE (2003)
Arasaratnam, I., Haykin, S.: Cubature Kalman filters. IEEE Trans. Autom. Control 54(6), 1254–1269 (2009)
Zoljic-Beglerovic, S., Stettinger, G., Luber, B., Horn, M.: Railway suspension system fault diagnosis using Cubature Kalman filter techniques. IFAC-PapersOnLine 51(24), 1330–1335 (2018)
Bar-Shalom, Y., Li, X., Kirubarajan, T.: Frontmatter and index. In: Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software, pp. i–xxiii (2001)
Mazaheri, A., Radan, A.: Performance evaluation of nonlinear Kalman filtering techniques in low speed brushless DC motors driven sensor-less positioning systems. Control Eng. Pract. 60, 148–156 (2017)
Acknowledgements
This work was written at VIRTUAL VEHICLE Research Center in Graz, Austria. I would like to acknowledge the financial support of the COMET K2 – Competence Centers for Excellent Technologies Programme of the Federal Ministry for Transport, Innovation and Technology (BMVIT), the Federal Ministry for Digital and Economic Affairs (BMDW), the Austrian Research Promotion Agency (FFG), the Province of Styria and the Styrian Business Promotion Agency (SFG).
Furthermore, the authors would like to express their thanks to the supporting industrial and scientific project partners, namely Siemens Mobility and to the Graz University of Technology.
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Zoljic-Beglerovic, S., Luber, B., Stettinger, G., Müller, G., Horn, M. (2020). Parameter Identification for Railway Suspension Systems Using Cubature Kalman Filter. In: Klomp, M., Bruzelius, F., Nielsen, J., Hillemyr, A. (eds) Advances in Dynamics of Vehicles on Roads and Tracks. IAVSD 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-38077-9_15
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DOI: https://doi.org/10.1007/978-3-030-38077-9_15
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