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This paper demonstrates the possibility to detect suspension failures of railway vehicles using a multiple-model approach from on-board measurement data. The railway vehicle model used in this study includes lateral and yaw motions of wheelsets and bogie, and the lateral motion of the vehicle body. These motions are measured by on-board sensors for lateral acceleration and yaw rate. The detection algorithm is formulated based on the interacting multiple-model (IMM) algorithm adding a method updating estimation model. The IMM method has been applied for detecting faults in vehicle suspension systems in a simulation study. The mode probabilities and states of vehicle suspension systems are estimated based on a Kalman filter (KF). This algorithm is evaluated in simulation examples. Simulation results indicate that the algorithm effectively detects on-board faults of railway vehicle suspension systems in realistic situation.

Condition Monitoring of Railway Vehicle Suspension Using Multiple Model Approach