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2020 | OriginalPaper | Chapter

Parameter Identification for Railway Suspension Systems Using Cubature Kalman Filter

Authors: Selma Zoljic-Beglerovic, Bernd Luber, Georg Stettinger, Gabor Müller, Martin Horn

Published in: Advances in Dynamics of Vehicles on Roads and Tracks

Publisher: Springer International Publishing

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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.
Literature
1.
go back to reference Li, P., Goodall, R.: Model-based condition monitoring for railway vehicle systems. In: Control 2004 (2004) Li, P., Goodall, R.: Model-based condition monitoring for railway vehicle systems. In: Control 2004 (2004)
2.
go back to reference 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) 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)
3.
go back to reference 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) 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)
4.
go back to reference 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) CrossRef 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) CrossRef
5.
go back to reference Ward, C.P., Goodall, R.M., Dixon, R.: Wheel rail profile condition monitoring. In: UKACC International Conference on Control 2010, pp. 1–6 (2010) Ward, C.P., Goodall, R.M., Dixon, R.: Wheel rail profile condition monitoring. In: UKACC International Conference on Control 2010, pp. 1–6 (2010)
6.
go back to reference 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) CrossRef 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) CrossRef
7.
go back to reference 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) 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)
8.
9.
go back to reference 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) CrossRef 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) CrossRef
10.
go back to reference 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) 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)
11.
go back to reference 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) CrossRef 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) CrossRef
Metadata
Title
Parameter Identification for Railway Suspension Systems Using Cubature Kalman Filter
Authors
Selma Zoljic-Beglerovic
Bernd Luber
Georg Stettinger
Gabor Müller
Martin Horn
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-38077-9_15

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