The parameters of the railway vehicle suspensions are estimation in order to provide information to support condition-based (instead of calendar-based) maintenance. A simplified plan view railway vehicle dynamical model is derived. A half rather than full vehicle model is used because the dynamic coupling between the two bogies via the soft secondary suspension is small. The wheel-sets and bogie each have two degrees-of-freedom (lateral and yaw), and for a half-vehicle model the vehicle body just has the lateral degree-of freedom. A newly developed RBPF (Rao-Blackwellized Particle Filter) based method is used for parameter estimation. Two added advantages of using the RBPF-based method are that no analytical derivatives (such as Jacobians) need to be calculated and no prior knowledge on the particular initial values for the parameters to be estimated is required as for classical EKF (Extended Kalman Filter) based and ML (Maximum Likelihood) methods. 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.


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    Title :

    Estimation of railway vehicle suspension parameters for condition monitoring


    Additional title:

    Parameterschätzung eines Modells einer Eisenbahnradaufhängung für die Zustandsüberwachung


    Contributors:

    Published in:

    Publication date :

    2007


    Size :

    13 Seiten, 13 Bilder, 1 Tabelle, 24 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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