The Validation of an ACS-SSI based Online Condition Monitoring for Railway Vehicle Suspension Systems using a SIMPACK Model

To enhance the safe operation of modern railway vehicles, an online condition monitoring scheme is proposed for vehicle suspension systems. The core technology of the scheme is based on the average correlation signals based stochastic subspace identification (ACS-SSI) algorithm which allows system identification to be implemented reliably with output signals only that have strong noise and nonlinearity in vehicle applications. To validate the scheme, a series simulation studies were carried out based on a more realistic bogie model, developed in SIMPACK, under typical random excitations including vertical, lateral, rolling and gauging directions. ACS-SSI then applied to the signals from the model under common faults in the bogie suspensions to identify the system parameters. The agreeable results obtained by comparing the identified results with that calculated by SIMPACK shows that the proposed scheme performs reliably in obtaining the system parameters: modal frequency, damping and shape that are required for online diagnosis.