DescriptionOne simple model-based condition monitoring approach for the rail vehicle suspension system is proposed in this paper. This method is based on recursive least-square (RLS) algorithm regarding a deterministic model. RLS is able to identify the unknown parameters of an ‘input-output’ system by using the correlation properties of variables. In a vehicle dynamic system, the identification of suspension parameter is achieved by utilizing the relationship between the excitation and response. One fault detection method for the vertical suspension system is discussed as an example of this condition monitoring scheme. In order to validate the feasibility of this approach, simulation results from the vehicle dynamics software ‘ADTreS’ are employed as ‘virtual measurements’ considering a trailer car of Italian ETR500 high-speed train. The identification results indicate that the suspension parameter estimates are consistent with the reference values, thereby supporting this fault diagnosis technique to be applied in the future vehicle-based condition monitoring system.
|11 Jul 2017
|First International Conference on Rail Transportation
|Degree of Recognition