A linear Kalman filtering scheme for estimation of secondary vertical suspension of railway vehicles

Abstract

Model based filtering is fast becoming a key instrument for maintenance and conditio monitoring of railway vehicles. This study presents the use of linear Kalman filtering scheme t identify vertical secondary suspension of a railway vehicle by using the vertical vibrations of vehicle due to vertical track irregularities. As well as the use of linear Kalman filtering scheme, weighted least squares estimation is used to identify vertical secondary spring coefficient as parameter by using residuals of the filter. In this investigation, a 7 degree of freedom dynami model of ERRI B176 benchmark vehicle is considered. Unlike previous studies, to the authors knowledge, this research provides the simplest estimation scheme for identification of secondar vertical spring parameter and can be used to achieve a cost-effective condition based maintenanc for railway vehicles.