Estimation of railway vehicle suspension parameters for condition monitoring

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This paper investigates the problem of parameter estimation for railway vehicle suspensions so as to provide information to support condition-based (instead of calendar-based) maintenance. A simplified plan view railway vehicle dynamical model is derived and a newly developed Rao-Blackwellized particle filter (RBPF) based method is used for parameter estimation. Computer simulations are carried out to assess and compare the performance of parameter estimation with different sensor configurations as well as the robustness with respect to the uncertainty in the statistics of the random track inputs. The method is then verified practically using real test data from a Coradia Class 175 railway vehicle with only bogie and body mounted sensors, and some preliminary results are presented.


Original languageEnglish
Pages (from-to)43-55
Number of pages13
JournalControl Engineering Practice
Issue number1
Early online date17 Apr 2006
Publication statusPublished - 1 Jan 2007


  • Condition monitoring, Extended Kalman filter, Parameter estimation, Railway vehicle dynamical modelling, Rao-Blackwellized particle filter