Parameter Identification for Railway Suspension Systems Using Cubature Kalman Filter
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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.
KeywordsParameter identification Fault detection Cubature Kalman filter Railway suspension system
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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